CUDA kernel benchmark baselines recorded 2026-05-10 (previous recording: 2026-05-05)
Platform: RTX 3070 Ti Laptop (GA104, sm_86, Ampere)
Note: Re-baselined under Tier 10 valid_when policy on CUDA 13.2 (median of 5 valid samples per kernel; SKIPPED samples discarded). Original 2026-05-05 baselines were CUDA 12.8 — most kernels match within 0.5%, two showed real measured improvement, igemm_sparse_tiled regressed per Obs HH (CUDA 13.2 IMMA stall behavior change).

Default valid_when policy:
require_no_throttle: yes
allow_throttle: GpuIdle
comment: Inherited by every kernel that doesn't set its own valid_when. Override per-kernel for stricter requirements (min_clock_sm, max_temp_c, require_ac).

Baseline entry schema (field meanings):
exe: (optional) path to bench binary; defaults to <kernel-dir>/bench or <kernel-dir>/bench_<basename>
match: (optional) substring of the bench-output line that holds this kernel's number; required when bench.cu prints multiple kernels
section: (optional) substring of a section header; parser only considers lines after this header until the next header
value_label: (optional) text immediately following the throughput number on the matched line; required when the line has multiple numbers
valid_when: (optional) GPU/host state requirement for a fair comparison; refuses to compare if violated. Fields: require_no_throttle (default TRUE), allow_throttle (default ['GpuIdle']), min_clock_sm (MHz), max_temp_c (degC), require_ac (laptop). Tier 10.
tolerance: (optional) per-config tolerance override (fraction). Default is the CLI --tolerance (0.10). Use for kernels that are intrinsically noisy on this hardware.

Per-kernel baselines:
kernels/gemm/hgemm/hgemm_16warp.cu: exe: kernels/gemm/hgemm/bench
2048_2048_2048: ms: 0.539
gflops: 31875
match: hgemm_16warp (128x128 2blk/SM)
note: median of 5 valid samples, 2026-05-10 (was 0.527 / 31910 on CUDA 12.8)
4096_4096_4096: ms: 4.327
gflops: 31765
match: hgemm_16warp (128x128 2blk/SM)
note: median of 5 valid samples, 2026-05-10 (was 4.220 / 31910 on CUDA 12.8)
kernels/gemm/igemm/igemm_sparse_tiled.cu: exe: kernels/gemm/igemm/bench_sparse
2048_2048_2048: ms: 0.544
tops: 31588
match: igemm_sparse_tiled
value_label: dense-equiv GFLOPS
note: median of 5 valid samples, 2026-05-10. Was 0.433 / 39674 on CUDA 12.8; CUDA 13.2 reverts the metadata-preload IMMA gain (see Obs HH). Stable at this level on the new toolchain.
4096_4096_4096: ms: 4.449
tops: 30889
match: igemm_sparse_tiled
value_label: dense-equiv GFLOPS
tolerance: 0.3
note: median of 5 valid samples, 2026-05-10. Bimodal on this laptop: 3.85ms (boost) vs 6.5ms (steady) at the same clock and no throttle signal — likely DRAM cache state at launch. Wider tolerance accepts both modes; re-investigate when running on a desktop GPU.
kernels/gemm/igemm/igemm_pipelined_cpasync.cu: exe: kernels/gemm/igemm/bench
4096_4096_4096: ms: 6.796
tops: 20227
match: igemm_cpasync
note: median of 2 valid samples (3 of 5 hit SwPowerCap and were dropped), 2026-05-10. Within 2.5% of CUDA-12.8 baseline (6.6 ms / 20688 TOPS).
kernels/attention/flash_attention/flash_attn_br16_regpv.cu: exe: kernels/attention/flash_attention/bench_br16_regpv
1024_8_8: ms: 2.449
gflops: 7152
match: flash_attn_br16_regpv
config_note: seq_len=1024, batch=8, heads=8
note: median of 5 valid samples, 2026-05-10. +17% throughput vs 2026-05-05 baseline (was 2.810 / 6112) — real improvement from regpv tuning between recordings.
kernels/convolution/conv2d/conv2d_implicit_gemm.cu: exe: kernels/convolution/conv2d/bench_implicit_gemm
1_64_64_320_320: ms: 1.056
gflops: 7150
section: SD 64
match: Implicit (single kern)
config_note: N=1, H=W=64, Cin=Cout=320; bench shape 'SD 64x64 Cin=Cout=320'
note: median of 5 valid samples, 2026-05-10. +7% throughput vs 2026-05-05 baseline (was 1.130 / 6687).