record_uuid stringlengths 36 36 | target_hardware_platform stringclasses 2
values | problem_statement stringlengths 562 604 | naive_cpu_code_reference stringclasses 4
values | failed_triton_attempt stringlengths 411 480 | compiler_exception_class stringclasses 3
values | execution_error_log stringclasses 3
values | expert_root_cause_analysis stringclasses 3
values | fixed_triton_code stringlengths 539 591 | pipeline_metadata dict |
|---|---|---|---|---|---|---|---|---|---|
0d79ced6-b729-4806-b5f0-0c750f905dd7 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #1, GridBlock=64). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 SXM... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #1)
@triton.jit
def flash_attn_fwd_kernel_v1_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via hardw... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #1)
@triton.jit
def flash_attn_fwd_kernel_v1_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch pad... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 1
} |
a408179c-2b55-4c51-9c41-085e4a03cc50 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #2, GridBlock=256). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 SX... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #2)
@triton.jit
def flash_attn_fwd_kernel_v2_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix block... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #2)
@triton.jit
def flash_attn_fwd_kernel_v2_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling patterns... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 2
} |
e7427886-149a-4d7f-b13b-5ca85198a383 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #3, GridBlock=128). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 SX... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #3)
@triton.jit
def flash_attn_fwd_kernel_v3_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes to... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #3)
@triton.jit
def flash_attn_fwd_kernel_v3_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune blo... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 3
} |
37731add-ea73-44c4-9cc9-0df1ed5a5663 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #4, GridBlock=32). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 Ten... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #4)
@triton.jit
def flash_attn_fwd_kernel_v4_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #4)
@triton.jit
def flash_attn_fwd_kernel_v4_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 4
} |
55bf4cbb-86f1-4b42-92f8-3ae311068379 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #5, GridBlock=64). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 Ten... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #5)
@triton.jit
def flash_attn_fwd_kernel_v5_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory mat... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #5)
@triton.jit
def flash_attn_fwd_kernel_v5_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 5
} |
810ceff2-5701-4ee3-9273-012bc7666f14 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #6, GridBlock=32). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 Ten... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #6)
@triton.jit
def flash_attn_fwd_kernel_v6_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #6)
@triton.jit
def flash_attn_fwd_kernel_v6_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 #... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 6
} |
b7d9f3d9-ee2c-4334-8877-e6a9baba7b76 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #7, GridBlock=128). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H100... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #7)
@triton.jit
def rope_embedding_kernel_v7_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via hard... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #7)
@triton.jit
def rope_embedding_kernel_v7_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch pa... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 7
} |
9fc1bbc4-c89f-4c0f-8534-668d8fbf7b21 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #8, GridBlock=128). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H100... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #8)
@triton.jit
def rope_embedding_kernel_v8_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix block... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #8)
@triton.jit
def rope_embedding_kernel_v8_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling patterns... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 8
} |
ca13fb9d-350f-48e2-9d2b-ec2186419519 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #9, GridBlock=32). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H100 ... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #9)
@triton.jit
def rope_embedding_kernel_v9_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes to ... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #9)
@triton.jit
def rope_embedding_kernel_v9_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune bloc... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 9
} |
76ee887c-8847-4540-9fd5-eab8dc7f328b | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #10, GridBlock=128). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B20... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #10)
@triton.jit
def rope_embedding_kernel_v10_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared c... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #10)
@triton.jit
def rope_embedding_kernel_v10_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structu... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 10
} |
c7b5e478-531e-4a06-b58a-92a4fe0cef54 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #11, GridBlock=32). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B200... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #11)
@triton.jit
def rope_embedding_kernel_v11_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory m... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #11)
@triton.jit
def rope_embedding_kernel_v11_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzli... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 11
} |
257510ca-528a-4f52-8ac6-c8bbfe033495 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #12, GridBlock=128). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B20... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #12)
@triton.jit
def rope_embedding_kernel_v12_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge bl... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #12)
@triton.jit
def rope_embedding_kernel_v12_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 12... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 12
} |
4f3f429d-ccc3-4e64-a1c9-5c7d218eafb4 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #13, GridBlock=64). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NVI... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #13)
@triton.jit
def fused_swiglu_quant_kernel_v13_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #13)
@triton.jit
def fused_swiglu_quant_kernel_v13_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pit... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 13
} |
069594b1-817f-4ddb-bfc8-d4400684924e | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #14, GridBlock=64). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NVI... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #14)
@triton.jit
def fused_swiglu_quant_kernel_v14_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix ... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #14)
@triton.jit
def fused_swiglu_quant_kernel_v14_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling pat... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 14
} |
49b607d3-3df1-41c6-b673-8a88d5e94bb6 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #15, GridBlock=256). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #15)
@triton.jit
def fused_swiglu_quant_kernel_v15_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block si... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #15)
@triton.jit
def fused_swiglu_quant_kernel_v15_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tu... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 15
} |
edff875a-76c8-4d0f-bd66-e99fb8f79aba | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #16, GridBlock=256). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #16)
@triton.jit
def fused_swiglu_quant_kernel_v16_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shar... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #16)
@triton.jit
def fused_swiglu_quant_kernel_v16_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit str... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 16
} |
dd52302d-2e78-42f3-97e3-60961d302fa3 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #17, GridBlock=32). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NVI... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #17)
@triton.jit
def fused_swiglu_quant_kernel_v17_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memo... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #17)
@triton.jit
def fused_swiglu_quant_kernel_v17_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swi... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 17
} |
5d53d477-2e10-4516-b7d0-083b0eda3322 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #18, GridBlock=128). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #18)
@triton.jit
def fused_swiglu_quant_kernel_v18_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set hug... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #18)
@triton.jit
def fused_swiglu_quant_kernel_v18_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr ... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 18
} |
42a828c8-b8b9-4fd9-8b2e-06b8d8e7628e | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #19, GridBlock=32). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #19)
@triton.jit
def fused_layernorm_kernel_v19_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via ha... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #19)
@triton.jit
def fused_layernorm_kernel_v19_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch ... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 19
} |
ddf11edb-883f-4708-b3b4-f90b44d5c043 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #20, GridBlock=256). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platfor... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #20)
@triton.jit
def fused_layernorm_kernel_v20_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix bl... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #20)
@triton.jit
def fused_layernorm_kernel_v20_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling patte... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 20
} |
a95cece0-db81-46e8-8a0c-c1c568c4c62e | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #21, GridBlock=32). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #21)
@triton.jit
def fused_layernorm_kernel_v21_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes ... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #21)
@triton.jit
def fused_layernorm_kernel_v21_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune b... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 21
} |
4cc1421a-d0a8-4b2a-87a4-ba0e91ba37ef | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #22, GridBlock=256). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platfor... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #22)
@triton.jit
def fused_layernorm_kernel_v22_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared ... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #22)
@triton.jit
def fused_layernorm_kernel_v22_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit struct... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 22
} |
7334ee2f-b164-484a-994a-91cc7aa642fb | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #23, GridBlock=128). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platfor... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #23)
@triton.jit
def fused_layernorm_kernel_v23_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #23)
@triton.jit
def fused_layernorm_kernel_v23_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizz... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 23
} |
0ab391fa-7096-4ff4-8086-01416f78cd44 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #24, GridBlock=32). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #24)
@triton.jit
def fused_layernorm_kernel_v24_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge bl... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #24)
@triton.jit
def fused_layernorm_kernel_v24_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 12... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 24
} |
eeaaa93b-812c-44f5-9018-919d2590c18c | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #25, GridBlock=128). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 S... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #25)
@triton.jit
def flash_attn_fwd_kernel_v25_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via ha... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #25)
@triton.jit
def flash_attn_fwd_kernel_v25_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch ... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 25
} |
9eb72876-99c0-4b19-8b1a-bb783fff9fd7 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #26, GridBlock=64). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 SX... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #26)
@triton.jit
def flash_attn_fwd_kernel_v26_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix bloc... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #26)
@triton.jit
def flash_attn_fwd_kernel_v26_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling pattern... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 26
} |
c936c0ed-e451-412d-aa74-e5ae40d632dd | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #27, GridBlock=128). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 S... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #27)
@triton.jit
def flash_attn_fwd_kernel_v27_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes ... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #27)
@triton.jit
def flash_attn_fwd_kernel_v27_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune b... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 27
} |
02c4de4e-18db-4cd2-82f2-773cd240bbef | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #28, GridBlock=32). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 Te... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #28)
@triton.jit
def flash_attn_fwd_kernel_v28_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared co... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #28)
@triton.jit
def flash_attn_fwd_kernel_v28_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structur... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 28
} |
bcbd4156-f12a-4f5e-9492-665f4b512181 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #29, GridBlock=32). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 Te... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #29)
@triton.jit
def flash_attn_fwd_kernel_v29_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory m... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #29)
@triton.jit
def flash_attn_fwd_kernel_v29_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzli... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 29
} |
fabccf0e-f437-41bd-bc79-792f9568f1de | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #30, GridBlock=128). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 T... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #30)
@triton.jit
def flash_attn_fwd_kernel_v30_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge bl... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #30)
@triton.jit
def flash_attn_fwd_kernel_v30_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 12... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 30
} |
586fbf64-a946-472a-90b3-87abd1590222 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #31, GridBlock=64). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H100... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #31)
@triton.jit
def rope_embedding_kernel_v31_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via har... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #31)
@triton.jit
def rope_embedding_kernel_v31_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch p... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 31
} |
bd88c528-7a94-4b36-b9db-93adebaad7c0 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #32, GridBlock=128). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H10... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #32)
@triton.jit
def rope_embedding_kernel_v32_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix blo... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #32)
@triton.jit
def rope_embedding_kernel_v32_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling patter... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 32
} |
c2d1c530-b8a9-4c03-a931-49e323aabf2f | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #33, GridBlock=64). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H100... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #33)
@triton.jit
def rope_embedding_kernel_v33_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes t... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #33)
@triton.jit
def rope_embedding_kernel_v33_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune bl... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 33
} |
9fbf5528-2f7b-4ae8-b56d-92d20432043a | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #34, GridBlock=64). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B200... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #34)
@triton.jit
def rope_embedding_kernel_v34_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared co... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #34)
@triton.jit
def rope_embedding_kernel_v34_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structur... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 34
} |
6eef2b72-cf6f-41ae-81e3-6f6d1c466999 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #35, GridBlock=32). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B200... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #35)
@triton.jit
def rope_embedding_kernel_v35_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory m... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #35)
@triton.jit
def rope_embedding_kernel_v35_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzli... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 35
} |
af16f190-5339-44dc-b926-4a5e1f18c6ea | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #36, GridBlock=256). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B20... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #36)
@triton.jit
def rope_embedding_kernel_v36_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge bl... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #36)
@triton.jit
def rope_embedding_kernel_v36_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 12... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 36
} |
2e21b631-fb4b-41b9-8550-f0fe4fc6757c | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #37, GridBlock=256). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #37)
@triton.jit
def fused_swiglu_quant_kernel_v37_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy vi... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #37)
@triton.jit
def fused_swiglu_quant_kernel_v37_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pi... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 37
} |
aee5ce1d-2324-4958-aa5a-d1cb328a4fa6 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #38, GridBlock=32). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NVI... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #38)
@triton.jit
def fused_swiglu_quant_kernel_v38_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix ... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #38)
@triton.jit
def fused_swiglu_quant_kernel_v38_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling pat... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 38
} |
6071e54f-e83e-4862-b38c-d382d6462b2a | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #39, GridBlock=128). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #39)
@triton.jit
def fused_swiglu_quant_kernel_v39_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block si... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #39)
@triton.jit
def fused_swiglu_quant_kernel_v39_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tu... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 39
} |
f204008e-0d4a-4f89-8ddd-ceed79beb194 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #40, GridBlock=32). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NVI... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #40)
@triton.jit
def fused_swiglu_quant_kernel_v40_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to share... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #40)
@triton.jit
def fused_swiglu_quant_kernel_v40_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit stru... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 40
} |
e36a0301-d983-4b02-b1b2-2742bd9aa3d3 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #41, GridBlock=32). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NVI... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #41)
@triton.jit
def fused_swiglu_quant_kernel_v41_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memo... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #41)
@triton.jit
def fused_swiglu_quant_kernel_v41_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swi... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 41
} |
3d94cb18-4d03-4840-81d7-4c3ff98c4a58 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #42, GridBlock=64). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NVI... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #42)
@triton.jit
def fused_swiglu_quant_kernel_v42_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #42)
@triton.jit
def fused_swiglu_quant_kernel_v42_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr =... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 42
} |
b2efc18a-d268-43a0-b486-9c2ccbfba806 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #43, GridBlock=32). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #43)
@triton.jit
def fused_layernorm_kernel_v43_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via ha... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #43)
@triton.jit
def fused_layernorm_kernel_v43_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch ... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 43
} |
dcb7b96e-547f-427d-a403-9d348c8378ed | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #44, GridBlock=64). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #44)
@triton.jit
def fused_layernorm_kernel_v44_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix blo... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #44)
@triton.jit
def fused_layernorm_kernel_v44_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling patter... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 44
} |
118c8a80-42f7-489c-a165-475c878f858f | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #45, GridBlock=256). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platfor... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #45)
@triton.jit
def fused_layernorm_kernel_v45_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #45)
@triton.jit
def fused_layernorm_kernel_v45_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune ... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 45
} |
219a2c42-50ef-42dc-b87a-85fe81e03599 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #46, GridBlock=64). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #46)
@triton.jit
def fused_layernorm_kernel_v46_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared c... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #46)
@triton.jit
def fused_layernorm_kernel_v46_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structu... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 46
} |
2e7ff34a-fbc2-46a2-8098-d556a399ef5d | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #47, GridBlock=128). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platfor... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #47)
@triton.jit
def fused_layernorm_kernel_v47_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #47)
@triton.jit
def fused_layernorm_kernel_v47_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizz... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 47
} |
a185d2fe-33f5-4940-a23d-a0e563bb28f2 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #48, GridBlock=128). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platfor... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #48)
@triton.jit
def fused_layernorm_kernel_v48_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge b... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #48)
@triton.jit
def fused_layernorm_kernel_v48_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 1... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 48
} |
6907e80a-213d-4d71-90a4-ecd17f70c0fb | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #49, GridBlock=32). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 SX... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #49)
@triton.jit
def flash_attn_fwd_kernel_v49_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via har... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #49)
@triton.jit
def flash_attn_fwd_kernel_v49_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch p... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 49
} |
6c3a15d7-41c8-437d-9f69-e89d65193634 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #50, GridBlock=32). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 SX... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #50)
@triton.jit
def flash_attn_fwd_kernel_v50_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix bloc... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #50)
@triton.jit
def flash_attn_fwd_kernel_v50_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling pattern... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 50
} |
7ee89061-2111-4bb8-8212-b307f8c59f0c | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #51, GridBlock=64). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA H100 SX... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #51)
@triton.jit
def flash_attn_fwd_kernel_v51_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes t... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #51)
@triton.jit
def flash_attn_fwd_kernel_v51_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune bl... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 51
} |
9fbf8fac-ced8-4957-9b6a-4255ee775fd9 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #52, GridBlock=128). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 T... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #52)
@triton.jit
def flash_attn_fwd_kernel_v52_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared c... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #52)
@triton.jit
def flash_attn_fwd_kernel_v52_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structu... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 52
} |
e00d8ad1-491e-4d84-8a2b-b8794e6740b8 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #53, GridBlock=64). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 Te... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #53)
@triton.jit
def flash_attn_fwd_kernel_v53_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory m... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #53)
@triton.jit
def flash_attn_fwd_kernel_v53_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzli... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 53
} |
0a17fb59-13da-48f3-b536-ee015cb78d14 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'FlashAttention-3 Forward Pass' operational pass (Variant #54, GridBlock=64). Target Framework Spec: Computes fused scaled dot-product attention utilizing shared-memory tiling and causal masking rules.
Target Infrastructure Platform: NVIDIA B200 Te... | def native_attention(q, k, v):
scores = q @ k.transpose(-1, -2) * (1.0 / math.sqrt(128))
mask_causal(scores)
attn = softmax(scores) @ v
return attn | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #54)
@triton.jit
def flash_attn_fwd_kernel_v54_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge blo... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #54)
@triton.jit
def flash_attn_fwd_kernel_v54_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 54
} |
4167bc44-5331-4160-90b5-1f199f0fd834 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #55, GridBlock=256). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H10... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #55)
@triton.jit
def rope_embedding_kernel_v55_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via ha... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #55)
@triton.jit
def rope_embedding_kernel_v55_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch ... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 55
} |
a007bb61-46c0-40b5-8ef8-6501e1252fa4 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #56, GridBlock=256). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H10... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #56)
@triton.jit
def rope_embedding_kernel_v56_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix blo... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #56)
@triton.jit
def rope_embedding_kernel_v56_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling patter... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 56
} |
f4060a04-26f7-49b9-8ca6-5717d3179e95 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #57, GridBlock=128). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA H10... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #57)
@triton.jit
def rope_embedding_kernel_v57_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes ... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #57)
@triton.jit
def rope_embedding_kernel_v57_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune b... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 57
} |
fc52f58a-b25b-4b35-b7ad-b10c95962c57 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #58, GridBlock=32). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B200... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #58)
@triton.jit
def rope_embedding_kernel_v58_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared co... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #58)
@triton.jit
def rope_embedding_kernel_v58_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structur... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 58
} |
9f66ecbc-ba86-4057-9784-506dc06b4ed7 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #59, GridBlock=32). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B200... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #59)
@triton.jit
def rope_embedding_kernel_v59_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory m... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #59)
@triton.jit
def rope_embedding_kernel_v59_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzli... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 59
} |
795a6098-6d88-482b-95b9-33fb48b371c1 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Rotary Position Embedding (RoPE)' operational pass (Variant #60, GridBlock=128). Target Framework Spec: Applies sinusoidal rotation matrices to query and key tensors for advanced context window expansion.
Target Infrastructure Platform: NVIDIA B20... | def native_rope(x, cos, sin):
x1 = x[..., :32]
x2 = x[..., 32:]
x_rotated = cat([-x2, x1], dim=-1)
return x * cos + x_rotated * sin | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #60)
@triton.jit
def rope_embedding_kernel_v60_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge bl... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #60)
@triton.jit
def rope_embedding_kernel_v60_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 12... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 60
} |
b5f25e84-e26e-4308-893b-092eb32282fb | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #61, GridBlock=256). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #61)
@triton.jit
def fused_swiglu_quant_kernel_v61_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy vi... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #61)
@triton.jit
def fused_swiglu_quant_kernel_v61_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pi... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 61
} |
006014a0-cdc4-4e9a-a92c-a796b7ea9768 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #62, GridBlock=128). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #62)
@triton.jit
def fused_swiglu_quant_kernel_v62_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #62)
@triton.jit
def fused_swiglu_quant_kernel_v62_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling pa... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 62
} |
015c2977-d028-48b8-b00f-c23faa810f57 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #63, GridBlock=256). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #63)
@triton.jit
def fused_swiglu_quant_kernel_v63_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block si... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #63)
@triton.jit
def fused_swiglu_quant_kernel_v63_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tu... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 63
} |
ff533ca8-485f-4165-8573-b66a6c9982b7 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #64, GridBlock=128). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #64)
@triton.jit
def fused_swiglu_quant_kernel_v64_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shar... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #64)
@triton.jit
def fused_swiglu_quant_kernel_v64_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit str... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 64
} |
9cbad2d4-7dde-4dff-a0b1-944d9e2d39b3 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #65, GridBlock=256). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NV... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #65)
@triton.jit
def fused_swiglu_quant_kernel_v65_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared mem... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #65)
@triton.jit
def fused_swiglu_quant_kernel_v65_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR sw... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 65
} |
25c1c484-4961-4b37-8027-710d64abb7ae | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Quantized Gated Linear Unit (Fused FFN)' operational pass (Variant #66, GridBlock=32). Target Framework Spec: Executes FP8 tensor core matrix multiplication combined with element-wise Swish activation functions.
Target Infrastructure Platform: NVI... | def native_swiglu(x, w1, w2):
return (x @ dequantize(w1) * sigmoid(x @ dequantize(w1))) * (x @ dequantize(w2)) | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #66)
@triton.jit
def fused_swiglu_quant_kernel_v66_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #66)
@triton.jit
def fused_swiglu_quant_kernel_v66_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr =... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 66
} |
9df0635e-413a-4947-96de-fec4e0853ac6 | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #67, GridBlock=64). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #67)
@triton.jit
def fused_layernorm_kernel_v67_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via ha... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #67)
@triton.jit
def fused_layernorm_kernel_v67_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structural pitch ... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 67
} |
8d244250-5090-4924-970d-4d51072151ae | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #68, GridBlock=256). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platfor... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #68)
@triton.jit
def fused_layernorm_kernel_v68_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory matrix bl... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #68)
@triton.jit
def fused_layernorm_kernel_v68_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 256):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzling patte... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 68
} |
03b107c9-e6f9-4e81-92ff-2d8c84ec275a | NVIDIA H100 SXM5 (Hopper) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #69, GridBlock=32). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #69)
@triton.jit
def fused_layernorm_kernel_v69_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge block sizes ... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA H100 SXM5 (Hopper) (Variant #69)
@triton.jit
def fused_layernorm_kernel_v69_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tune b... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 69
} |
ee6b928a-be84-4dc8-9a4b-666f9967954d | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #70, GridBlock=32). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #70)
@triton.jit
def fused_layernorm_kernel_v70_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared c... | TritonCompilerError | TritonCompilerError: TMA descriptor validation failed. Global memory pointer pitch alignment must be a multiple of 128 bytes for asynchronous hardware multi-tiling. | The kernel schedules asynchronous TMA copies directly into shared memory layouts. However, because the global baseline matrix dimensions are not explicitly padded to 128-byte alignment constraints, the hardware address generator triggers a segmentation trap. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #70)
@triton.jit
def fused_layernorm_kernel_v70_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structu... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 70
} |
6d88398b-052c-4702-a5f0-e3ea8cd0ba14 | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #71, GridBlock=64). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platform... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #71)
@triton.jit
def fused_layernorm_kernel_v71_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Direct linear mapping to shared memory ... | RuntimeCUDAError | RuntimeCUDAError: Address spaces collision detected in Shared Memory Warp Indexing. Throughput dropped below 8.5% capacity boundary. | Warp execution threads are mapping column-indexed array slots concurrently. Since sequential threads hit identical 32-bit hardware memory banks across the shared array configuration, execution gets stuck in sequential queues. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #71)
@triton.jit
def fused_layernorm_kernel_v71_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 64):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Apply bitwise XOR swizzl... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 71
} |
3694c216-6894-4631-b267-10b53d95216a | NVIDIA B200 Tensor Core (Blackwell) | Task: Implement a high-performance raw Triton GPU kernel variant for a 'Fused Persistent Layer Normalization' operational pass (Variant #72, GridBlock=128). Target Framework Spec: Performs mean and variance calculations across the hidden dimension with integrated residual scale additions.
Target Infrastructure Platfor... | def native_layernorm(x, weight, bias, eps=1e-5):
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True, unbiased=False)
return weight * (x - mean) / sqrt(var + eps) + bias | # Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #72)
@triton.jit
def fused_layernorm_kernel_v72_baseline(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
BLOCK_N: tl.constexpr = 512 # Set huge b... | TritonExecutionError | TritonExecutionError: Static Register Allocator Failure. Thread requirement [67 registers] exceeds block multiprocessor cap. Spilling to off-chip DRAM. | Declaring oversized static layout arrays within unrolled loop spaces forces the software compiler to use slow off-chip local memory instead of high-speed registers, dropping GPU computation speeds dramatically. | # Flawless Fused Optimization Variant targeting NVIDIA B200 Tensor Core (Blackwell) (Variant #72)
@triton.jit
def fused_layernorm_kernel_v72_optimized(A_ptr, B_ptr, Out_ptr, M, N, K, BLOCK_SIZE: tl.constexpr = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 1... | {
"dataset_tier": "Elite Agentic GPU Trajectories",
"internal_fidelity_rating": "High-Fidelity 10.0",
"logical_consistency_verified": true,
"generation_engine_type": "Deterministic Matrix Logic Generator",
"variant_id": 72
} |
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