record_uuid stringlengths 36 36 | target_hardware_platform stringclasses 2
values | problem_statement stringlengths 562 605 | naive_cpu_code_reference stringclasses 4
values | failed_triton_attempt stringlengths 411 482 | compiler_exception_class stringclasses 3
values | execution_error_log stringclasses 3
values | expert_root_cause_analysis stringclasses 3
values | fixed_triton_code stringlengths 539 593 | pipeline_metadata dict |
|---|---|---|---|---|---|---|---|---|---|
9e8131da-0f29-41c2-a331-e83aa9bab707 | 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=32). 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 = 32):
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 = 32):
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
} |
c2503131-4a8d-4dc6-bf42-18c2e7cf3041 | 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=32). 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 #2)
@triton.jit
def flash_attn_fwd_kernel_v2_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 blocks... | 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 = 32):
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
} |
d714951b-416f-4e46-b8cb-31d22767dd96 | 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=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 #3)
@triton.jit
def flash_attn_fwd_kernel_v3_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 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 = 256):
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
} |
a2f66a69-c764-46bf-a4b1-80b0b4d12693 | 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=128). 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 #4)
@triton.jit
def flash_attn_fwd_kernel_v4_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 cop... | 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 = 128):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
# Enforce explicit structura... | {
"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
} |
167cb2c7-551f-4f64-8250-0215b2c2ae2f | 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=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 #5)
@triton.jit
def flash_attn_fwd_kernel_v5_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 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 = 32):
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
} |
d9fad908-2309-4643-987c-e6124a96fdab | 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=128). 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 #6)
@triton.jit
def flash_attn_fwd_kernel_v6_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 bloc... | 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 = 128):
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
} |
64ad7f73-a793-408c-9292-9c32286fc07f | 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=256). 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 = 256):
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 = 256):
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
} |
7fabd5c8-49f7-4914-b8ba-16feb1222fae | 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=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 #8)
@triton.jit
def rope_embedding_kernel_v8_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 blocks... | 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 = 32):
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
} |
f93245eb-f8cc-46e0-abfc-5afa532337d3 | 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=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 #9)
@triton.jit
def rope_embedding_kernel_v9_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 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 = 64):
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
} |
95e8854b-a49f-4398-8a35-06fa3660ba26 | 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=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 #10)
@triton.jit
def rope_embedding_kernel_v10_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 #10)
@triton.jit
def rope_embedding_kernel_v10_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": 10
} |
deda0528-06a6-469b-b342-3fc59a50f999 | 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
} |
40ffa428-09cf-4559-9e3e-cf9d1d72ad32 | 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=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 #12)
@triton.jit
def rope_embedding_kernel_v12_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 #12)
@triton.jit
def rope_embedding_kernel_v12_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": 12
} |
dc6078ec-f28c-4f57-b8bf-eb5a97b15ec1 | 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=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 #13)
@triton.jit
def fused_swiglu_quant_kernel_v13_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 #13)
@triton.jit
def fused_swiglu_quant_kernel_v13_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": 13
} |
bb8b34e1-8954-4dbf-9fd5-763df94cf615 | 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=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 #14)
@triton.jit
def fused_swiglu_quant_kernel_v14_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... | 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 = 256):
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": 14
} |
0153d776-576d-41d1-a61f-60441c40328c | 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
} |
0fbc6cfb-3406-40e5-9d8c-b11e3023951a | 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=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 #16)
@triton.jit
def fused_swiglu_quant_kernel_v16_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 #16)
@triton.jit
def fused_swiglu_quant_kernel_v16_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": 16
} |
a6dd2adc-4e9c-4c3e-b7c2-8cec9d07fde7 | 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=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 #17)
@triton.jit
def fused_swiglu_quant_kernel_v17_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 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 #17)
@triton.jit
def fused_swiglu_quant_kernel_v17_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 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": 17
} |
94f213cf-dbb8-43bb-9565-e783aec752ed | 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=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 #18)
@triton.jit
def fused_swiglu_quant_kernel_v18_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 #18)
@triton.jit
def fused_swiglu_quant_kernel_v18_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": 18
} |
f6d7d6a8-f561-46fb-9488-f09df33044ca | 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=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 #19)
@triton.jit
def fused_layernorm_kernel_v19_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 #19)
@triton.jit
def fused_layernorm_kernel_v19_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": 19
} |
c60f7efa-4593-4aea-b4a1-2072d8866aee | 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=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 #20)
@triton.jit
def fused_layernorm_kernel_v20_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 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 #20)
@triton.jit
def fused_layernorm_kernel_v20_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 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": 20
} |
812fd05e-4629-47ee-8618-d3836e0d0483 | 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=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 #21)
@triton.jit
def fused_layernorm_kernel_v21_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 ... | 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 = 64):
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
} |
4e1898c8-1b49-4666-854d-79e52699e42a | 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=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 #22)
@triton.jit
def fused_layernorm_kernel_v22_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 ... | 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 = 128):
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
} |
1ff8f20d-47f2-4eab-b156-0a35d0a1506d | 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=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 #23)
@triton.jit
def fused_layernorm_kernel_v23_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 ... | 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 = 32):
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": 23
} |
abc3705e-8ff9-4fba-9ed3-4c587e7ba7ac | 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=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 #24)
@triton.jit
def fused_layernorm_kernel_v24_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 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 = 64):
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
} |
2dbdcd6e-c84e-4d75-8afc-4c71e120b77f | 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=256). 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 = 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 #25)
@triton.jit
def flash_attn_fwd_kernel_v25_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": 25
} |
443d9dfe-ab37-497e-b50c-5596aa660100 | 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=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 #26)
@triton.jit
def flash_attn_fwd_kernel_v26_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 #26)
@triton.jit
def flash_attn_fwd_kernel_v26_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": 26
} |
6f924a6b-fd33-40d5-9981-51fa01d580e8 | 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
} |
83765f0a-227b-4fba-bb41-9cc56f387a98 | 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=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 #28)
@triton.jit
def flash_attn_fwd_kernel_v28_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 #28)
@triton.jit
def flash_attn_fwd_kernel_v28_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": 28
} |
09e7ffbd-0cb2-4eb3-8c63-81b2e32d03f8 | 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
} |
4abc291f-f51e-4b6c-a123-43f23865b652 | 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
} |
c5d8ca1d-57db-4a61-abdb-61b1c1bb1956 | 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=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 #31)
@triton.jit
def rope_embedding_kernel_v31_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 #31)
@triton.jit
def rope_embedding_kernel_v31_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": 31
} |
9c0f1d9d-f7dc-436c-8b91-878f9cee2755 | 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=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 #32)
@triton.jit
def rope_embedding_kernel_v32_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 #32)
@triton.jit
def rope_embedding_kernel_v32_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": 32
} |
38d0aca2-30e9-44cb-b157-192528132c2e | 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=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 #33)
@triton.jit
def rope_embedding_kernel_v33_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 #33)
@triton.jit
def rope_embedding_kernel_v33_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": 33
} |
c50cbacb-a09a-4bdb-9d6b-451ce86cc85f | 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=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 #34)
@triton.jit
def rope_embedding_kernel_v34_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 #34)
@triton.jit
def rope_embedding_kernel_v34_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": 34
} |
795b52ab-d588-44e8-b8b1-3b6a6a814c7e | 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=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 #35)
@triton.jit
def rope_embedding_kernel_v35_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 #35)
@triton.jit
def rope_embedding_kernel_v35_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": 35
} |
5bf855c1-78bd-4b01-957e-0519985a80af | 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=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 #36)
@triton.jit
def rope_embedding_kernel_v36_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 #36)
@triton.jit
def rope_embedding_kernel_v36_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": 36
} |
235b401d-dc55-4441-b4dd-2dc6b8131104 | 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
} |
d419dcc9-899d-4c4e-b49b-59c274d8897a | 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=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 #38)
@triton.jit
def fused_swiglu_quant_kernel_v38_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 #38)
@triton.jit
def fused_swiglu_quant_kernel_v38_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": 38
} |
770edb8c-5b65-4046-8bda-da112e1abcd6 | 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=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 #39)
@triton.jit
def fused_swiglu_quant_kernel_v39_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 siz... | 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 = 32):
program_id_x = tl.program_id(0)
# ARCHITECTURE REMEDIATION APPLIED
BLOCK_N: tl.constexpr = 128 # Tun... | {
"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
} |
a8538848-76aa-4f18-ac17-f6aa3f13695b | 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=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 #40)
@triton.jit
def fused_swiglu_quant_kernel_v40_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 #40)
@triton.jit
def fused_swiglu_quant_kernel_v40_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": 40
} |
5eceaba7-5977-46f0-bca6-cd92814b4b7b | 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=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 #41)
@triton.jit
def fused_swiglu_quant_kernel_v41_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 #41)
@triton.jit
def fused_swiglu_quant_kernel_v41_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": 41
} |
0ac55c64-b798-47ca-8d8d-d732afd3ba0f | 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=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 #42)
@triton.jit
def fused_swiglu_quant_kernel_v42_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 #42)
@triton.jit
def fused_swiglu_quant_kernel_v42_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": 42
} |
d38cc72a-6b98-4357-be6f-db4d88d4b44c | 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=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 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 = 128):
program_id_x = tl.program_id(0)
# SYSTEM ERROR EMBEDDED BELOW
# Trigger asynchronous global to shared copy via h... | 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 = 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": 43
} |
4f1ddd02-e790-4dfc-9138-0d95866cf664 | 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=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 #44)
@triton.jit
def fused_layernorm_kernel_v44_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 #44)
@triton.jit
def fused_layernorm_kernel_v44_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": 44
} |
21db14fc-8bf4-454c-a63d-1ce8562d35d4 | 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=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 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 = 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 #45)
@triton.jit
def fused_layernorm_kernel_v45_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 ... | {
"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
} |
c5c9ae1f-ffc3-4ddd-a3b8-9e0629ca04aa | 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=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 #46)
@triton.jit
def fused_layernorm_kernel_v46_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 ... | 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 = 128):
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": 46
} |
533d8ef6-4b00-4250-996d-10b1db436216 | 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=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 #47)
@triton.jit
def fused_layernorm_kernel_v47_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... | 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 = 256):
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
} |
61f9c3e3-f03d-4c74-a571-4df7bfe38b4e | 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=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 #48)
@triton.jit
def fused_layernorm_kernel_v48_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 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 = 256):
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
} |
ea99af5a-e13c-43da-9a16-13e89958db4f | 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
} |
ed579326-487a-4194-b72a-add6932da576 | 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=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 #50)
@triton.jit
def flash_attn_fwd_kernel_v50_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 #50)
@triton.jit
def flash_attn_fwd_kernel_v50_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": 50
} |
52a7693b-1288-4eed-a710-82d203877dc7 | 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=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 #51)
@triton.jit
def flash_attn_fwd_kernel_v51_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 #51)
@triton.jit
def flash_attn_fwd_kernel_v51_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": 51
} |
69582812-693a-4278-9591-3e29f27d96a5 | 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=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 #52)
@triton.jit
def flash_attn_fwd_kernel_v52_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 #52)
@triton.jit
def flash_attn_fwd_kernel_v52_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": 52
} |
e9671e87-87ec-4b41-aad8-22f47bb5ba91 | 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
} |
524a9c9d-22a6-4b9b-afcd-14d1ebd8a0ea | 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=256). 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 #54)
@triton.jit
def flash_attn_fwd_kernel_v54_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 #54)
@triton.jit
def flash_attn_fwd_kernel_v54_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": 54
} |
73cb1c41-318b-4b6d-baba-3b25e162d9db | 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=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 #55)
@triton.jit
def rope_embedding_kernel_v55_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 #55)
@triton.jit
def rope_embedding_kernel_v55_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": 55
} |
ea5f80ca-3c0d-473b-b32f-c66139a0e9e8 | 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=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 #56)
@triton.jit
def rope_embedding_kernel_v56_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 #56)
@triton.jit
def rope_embedding_kernel_v56_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": 56
} |
1d637476-4ed2-4715-8692-03888f7d7b8b | 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=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 #57)
@triton.jit
def rope_embedding_kernel_v57_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 #57)
@triton.jit
def rope_embedding_kernel_v57_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 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
} |
3f104196-33f2-4f64-a828-30c67107a6f9 | 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=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 #58)
@triton.jit
def rope_embedding_kernel_v58_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 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 #58)
@triton.jit
def rope_embedding_kernel_v58_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 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": 58
} |
92d1abc3-eafc-46fb-bd23-87a2c9d4e001 | 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=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 #59)
@triton.jit
def rope_embedding_kernel_v59_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 #59)
@triton.jit
def rope_embedding_kernel_v59_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": 59
} |
af4196e6-4716-42cf-96ce-f4faffbba2a2 | 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=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 #60)
@triton.jit
def rope_embedding_kernel_v60_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 #60)
@triton.jit
def rope_embedding_kernel_v60_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": 60
} |
90cfbc73-164c-4d11-b296-dfe1d8d8c73c | 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=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 #61)
@triton.jit
def fused_swiglu_quant_kernel_v61_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 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 = 128):
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
} |
b4d715cd-3286-43aa-be52-f9f8055dccb2 | 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=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 #62)
@triton.jit
def fused_swiglu_quant_kernel_v62_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 #62)
@triton.jit
def fused_swiglu_quant_kernel_v62_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": 62
} |
bddfea01-e865-4837-8304-50f8673b3ec5 | 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
} |
71a29957-0b1a-4539-8961-d013c8c4bbeb | 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=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 #64)
@triton.jit
def fused_swiglu_quant_kernel_v64_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 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 #64)
@triton.jit
def fused_swiglu_quant_kernel_v64_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 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": 64
} |
87f061b7-b38d-4be4-a149-c19d0e321d53 | 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=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 #65)
@triton.jit
def fused_swiglu_quant_kernel_v65_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 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 #65)
@triton.jit
def fused_swiglu_quant_kernel_v65_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 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": 65
} |
378b792e-32b2-4904-bf5d-b5c8846bffbf | 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=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 #66)
@triton.jit
def fused_swiglu_quant_kernel_v66_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 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 #66)
@triton.jit
def fused_swiglu_quant_kernel_v66_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 ... | {
"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
} |
05fee42d-0154-448d-a85c-feaa463de2e4 | 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=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 #67)
@triton.jit
def fused_layernorm_kernel_v67_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 h... | 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 = 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": 67
} |
c6107435-091f-4da1-8137-bc7ffc25e999 | 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=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 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 = 128):
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 = 128):
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
} |
069aa7a9-13b9-4a0b-85a2-c9bafd48dd1b | 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=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 #69)
@triton.jit
def fused_layernorm_kernel_v69_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 ... | 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 = 64):
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
} |
511040c3-969f-4336-bf27-01a05b68c486 | 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=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 #70)
@triton.jit
def fused_layernorm_kernel_v70_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 ... | 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 = 128):
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": 70
} |
4f6e3eff-d62e-45ec-ab10-cb85ca89c69a | 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=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 #71)
@triton.jit
def fused_layernorm_kernel_v71_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 #71)
@triton.jit
def fused_layernorm_kernel_v71_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": 71
} |
b757474a-fee6-486a-b6af-2a3a69ed5387 | 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=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 #72)
@triton.jit
def fused_layernorm_kernel_v72_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 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 #72)
@triton.jit
def fused_layernorm_kernel_v72_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 = 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": 72
} |
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