Two findings from CCCL benchmarks applied to Triton autotune configs:
1. num_stages=2 (from transform bif=8 finding):
CCCL transform benchmark (babelstream.cu) search space includes
TUNE_BIF_BIAS from -16 to +16. BI-V100 bench found bif=8 (64KB
prefetch window) dominates across all problem sizes. Physical basis:
BW_per_SM × memory_latency = 56 GB/s × 1100ns ≈ 62KB
Triton's num_stages is the software pipelining equivalent of CCCL's
bytes_in_flight. num_stages=2 doubles the prefetch window from ~32KB
to ~64KB, matching the optimal BW×latency product.
2. Small-tile high-occupancy (from scan no_delay finding):
CCCL scan benchmark (sum.cu) found dcid=0 (no_delay) optimal on
BI-V100 because 16 SMs produce only ~32 CTAs, so the tile_status
array fits entirely in 6MB L2 with zero inter-CTA contention.
Implication: more smaller CTAs can saturate the 16 SMs better than
fewer large CTAs, especially for short sequences.
Added 3 new configs, all with num_stages=2 or waves_per_eu=4.
Triton autotune will select the fastest; no risk of regression.
Source: cccl_upstream/cub/benchmarks/bench/transform/babelstream.cu
cccl_upstream/cub/benchmarks/bench/scan/exclusive/sum.cu
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