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Applies Split-K, persistent kernels, Stream-K/Stream-K++, warp specialization, and multi-GPU fused schedules. Use when simple tiling leaves tails, load imbalance, or excessive kernel launch overhead on large GPUs.
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SOC 職業分類に基づく
Optimize fused cross-entropy loss kernels in Triton for NVIDIA and AMD GPUs. Covers fused log-softmax + NLL, online log-sum-exp, and large vocabulary handling. Use when writing or optimizing cross-entropy, label-smoothed CE, or similar loss kernels.
Optimize FlashAttention-style fused attention kernels in Triton for NVIDIA and AMD GPUs. Covers online softmax, tiled QK/AV GEMM, causal masking, and memory-efficient attention. Use when writing or optimizing self-attention, cross-attention, or any QKV attention kernel.
Optimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs. Covers tiled blocking, L2 cache grouping, tensor core utilization, and dual-platform autotune. Use when writing or optimizing matmul, linear layers, or any GEMM-based kernel.
Optimize RMS Normalization kernels in Triton for NVIDIA and AMD GPUs. Covers row-parallel reduction, vectorized loads, warp shuffle patterns, and rsqrt usage. Use when writing or optimizing RMSNorm, LayerNorm, or similar normalization kernels.
Optimize Rotary Position Embedding (RoPE) kernels in Triton for NVIDIA and AMD GPUs. Covers interleaved/sequential layouts, sincos precomputation, and multi-row processing. Use when writing or optimizing RoPE, position embeddings, or rotary transformations.
Optimize fused softmax kernels in Triton for NVIDIA and AMD GPUs. Covers online max/sum reduction, multi-row processing, numerical stability, and warp-level patterns. Use when writing or optimizing softmax, log-softmax, or similar row-wise normalization kernels.
| name | gpu-opt-tier4-advanced-scheduling |
| description | Applies Split-K, persistent kernels, Stream-K/Stream-K++, warp specialization, and multi-GPU fused schedules. Use when simple tiling leaves tails, load imbalance, or excessive kernel launch overhead on large GPUs. |
AutoKernel Tier 4 addresses parallelism structure beyond per-block tiling: how work maps to SMs over time and across chips.
BLOCK_K alone overspills SMEM/regs or hurts occupancy, but more K-parallelism improves utilization.| Symptom | Try first | Watch for |
|---|---|---|
| K too large for one tile’s SMEM | Split-K | Partial traffic + atomics |
| Huge grid, tiny per-kernel time | Persistent | Correctness of tile iterator |
| Classic 2D tail idle on big GPU | Stream-K | Reduction complexity |
| Hopper + TMA in play | Warp specialization | Producer/consumer deadlock |
| TP/PP pipeline bubbles | Comm/compute overlap | Ordering / race bugs |