phase 15: custom GEMV kernel — 46.6 tok/s serial (3.5x improvement, 130% of HF)
Custom bandwidth-optimized GEMV kernel for M=1 BF16 decode, replacing
cuBLAS which achieves only ~8% bandwidth utilization for tiny M=1 GEMMs.
Kernel design (csrc/gemm/gemv.cu):
- K-split tiled: TILE_N=128, TILE_K=256, Grid=(N/128, K/256)=512 blocks
- High occupancy: 512 blocks / 170 SMs = ~3 blocks/SM
- Coalesced memory access: adjacent threads read adjacent columns of W
- Shared memory for x vector (avoids redundant global reads)
- FP32 accumulation via atomicAdd (K-split partial sums)
- Separate fp32→bf16 conversion kernel
Integration:
- matmul() auto-dispatches to custom GEMV when M==1 && dtype==BF16
- Batched decode (M>1) continues to use cuBLAS
- Caching allocator provides FP32 temp buffer (pooled, no per-call malloc)
Ablation results (dash5, RTX 5090, Qwen3-8B BF16):
| Config | tok/s | vs HF (36) | vs roofline (112) |
|--------|-------|-----------|-------------------|
| Phase 14 (cuBLAS M=1) | 13.2 | 37% | 12% |
| + Custom GEMV (M=1) | 46.6 | 130% | 42% |
| Concurrent batch=4 | 28.2 | 78% | — |
Single-request throughput now EXCEEDS HuggingFace transformers by 30%.
The custom GEMV achieves ~42% of the theoretical roofline (vs 12% before).
Note: concurrent batch=4 (28.2 tok/s) is slower than serial (46.6 tok/s)
because the per-seq attention/reshape overhead in batched decode outweighs
the cuBLAS M=4 benefit when the custom GEMV already handles M=1 efficiently.
Engine should prefer serial decode when custom GEMV is available.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Gahow Wang
e207523e21