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Summary
Add benchmarks for uniform distribution / single-sample variates for
Simdtypes:u8x8, u8x16, u8x32, u8x64, i16x8, i16x16, i16x32.Also, change the name of the non-SIMD benchmarks to "x1" e.g.
sample_i16x1/SmallRng/distr.Motivation
This is a pre-requisite for any type of SIMD optimisation.
Details
Sample output (5800X):
$ cargo +nightly bench --bench uniform --features simd_support -- SmallRng Finished `bench` profile [optimized] target(s) in 0.03s Running benches/uniform.rs (target/release/deps/uniform-fff5b09bd763a405) sample_i8x1/SmallRng/single time: [1.5923 ns 1.5929 ns 1.5936 ns] Found 8408 outliers among 100000 measurements (8.41%) 278 (0.28%) low severe 303 (0.30%) low mild 3554 (3.55%) high mild 4273 (4.27%) high severe Criterion.rs ERROR: Error in Gnuplot: line 0: Can't plot with an empty x range! Benchmarking sample_i8x1/SmallRng/distr: Warming up for 1.0000 s Warning: Unable to complete 100000 samples in 3.0s. You may wish to increase target time to 5.3s, enable flat sampling, or reduce sample count to 53020. sample_i8x1/SmallRng/distr time: [1.0419 ns 1.0422 ns 1.0425 ns] Found 12826 outliers among 100000 measurements (12.83%) 4043 (4.04%) high mild 8783 (8.78%) high severe sample_i16x1/SmallRng/single time: [1.4809 ns 1.4812 ns 1.4817 ns] Found 22841 outliers among 100000 measurements (22.84%) 105 (0.10%) high mild 22736 (22.74%) high severe Criterion.rs ERROR: Error in Gnuplot: line 0: Can't plot with an empty x range! Criterion.rs ERROR: Error in Gnuplot: line 0: Can't plot with an empty x range! Criterion.rs ERROR: Error in Gnuplot: line 0: Can't plot with an empty x range! Benchmarking sample_i16x1/SmallRng/distr: Warming up for 1.0000 s Warning: Unable to complete 100000 samples in 3.0s. You may wish to increase target time to 5.3s, enable flat sampling, or reduce sample count to 52990. sample_i16x1/SmallRng/distr time: [1.0454 ns 1.0458 ns 1.0461 ns] Found 12165 outliers among 100000 measurements (12.16%) 3140 (3.14%) high mild 9025 (9.03%) high severe sample_i32x1/SmallRng/single time: [3.1251 ns 3.1331 ns 3.1411 ns] Found 12 outliers among 100000 measurements (0.01%) 5 (0.01%) high mild 7 (0.01%) high severe sample_i32x1/SmallRng/distr time: [1.9073 ns 1.9139 ns 1.9206 ns] Found 8875 outliers among 100000 measurements (8.88%) 5546 (5.55%) high mild 3329 (3.33%) high severe sample_i64x1/SmallRng/single time: [4.3892 ns 4.3953 ns 4.4018 ns] Found 73 outliers among 100000 measurements (0.07%) 64 (0.06%) high mild 9 (0.01%) high severe sample_i64x1/SmallRng/distr time: [1.7550 ns 1.7616 ns 1.7681 ns] Found 9194 outliers among 100000 measurements (9.19%) 5378 (5.38%) high mild 3816 (3.82%) high severe sample_i128x1/SmallRng/single time: [9.7639 ns 9.7734 ns 9.7839 ns] Found 162 outliers among 100000 measurements (0.16%) 135 (0.14%) high mild 27 (0.03%) high severe sample_i128x1/SmallRng/distr time: [3.8971 ns 3.9066 ns 3.9166 ns] Found 8601 outliers among 100000 measurements (8.60%) 6231 (6.23%) high mild 2370 (2.37%) high severe sample_u8x8/SmallRng/single time: [23.973 ns 24.000 ns 24.027 ns] Found 1602 outliers among 100000 measurements (1.60%) 1209 (1.21%) low mild 300 (0.30%) high mild 93 (0.09%) high severe sample_u8x8/SmallRng/distr time: [12.358 ns 12.379 ns 12.400 ns] Found 300 outliers among 100000 measurements (0.30%) 264 (0.26%) high mild 36 (0.04%) high severe sample_u8x16/SmallRng/single time: [43.275 ns 43.308 ns 43.344 ns] Found 407 outliers among 100000 measurements (0.41%) 2 (0.00%) low mild 342 (0.34%) high mild 63 (0.06%) high severe sample_u8x16/SmallRng/distr time: [26.532 ns 26.560 ns 26.587 ns] Found 289 outliers among 100000 measurements (0.29%) 6 (0.01%) low mild 243 (0.24%) high mild 40 (0.04%) high severe sample_u8x32/SmallRng/single time: [71.023 ns 71.052 ns 71.083 ns] Found 1010 outliers among 100000 measurements (1.01%) 350 (0.35%) low mild 625 (0.62%) high mild 35 (0.04%) high severe sample_u8x32/SmallRng/distr time: [35.570 ns 35.598 ns 35.625 ns] Found 906 outliers among 100000 measurements (0.91%) 397 (0.40%) low mild 402 (0.40%) high mild 107 (0.11%) high severe sample_u8x64/SmallRng/single time: [121.40 ns 121.44 ns 121.49 ns] Found 3279 outliers among 100000 measurements (3.28%) 206 (0.21%) low mild 1918 (1.92%) high mild 1155 (1.16%) high severe sample_u8x64/SmallRng/distr time: [54.650 ns 54.680 ns 54.711 ns] Found 845 outliers among 100000 measurements (0.84%) 358 (0.36%) low mild 363 (0.36%) high mild 124 (0.12%) high severe sample_i16x8/SmallRng/single time: [31.671 ns 31.700 ns 31.730 ns] Found 1196 outliers among 100000 measurements (1.20%) 475 (0.47%) low mild 637 (0.64%) high mild 84 (0.08%) high severe sample_i16x8/SmallRng/distr time: [21.576 ns 21.602 ns 21.628 ns] Found 335 outliers among 100000 measurements (0.34%) 31 (0.03%) low mild 283 (0.28%) high mild 21 (0.02%) high severe sample_i16x16/SmallRng/single time: [50.998 ns 51.044 ns 51.093 ns] Found 1362 outliers among 100000 measurements (1.36%) 755 (0.76%) high mild 607 (0.61%) high severe sample_i16x16/SmallRng/distr time: [29.717 ns 29.745 ns 29.773 ns] Found 228 outliers among 100000 measurements (0.23%) 1 (0.00%) low mild 179 (0.18%) high mild 48 (0.05%) high severe sample_i16x32/SmallRng/single time: [83.029 ns 83.066 ns 83.106 ns] Found 2168 outliers among 100000 measurements (2.17%) 534 (0.53%) low mild 1000 (1.00%) high mild 634 (0.63%) high severe sample_i16x32/SmallRng/distr time: [43.454 ns 43.482 ns 43.511 ns] Found 1070 outliers among 100000 measurements (1.07%) 516 (0.52%) low mild 497 (0.50%) high mild 57 (0.06%) high severeFurther motivation
In particular, I wanted to know whether the
target_featureoptimisations insrc/distr/utils.rsare useful. Not using the sse2 and avx2 features on my CPU (which doesn't support AVX512) I get very similar results implying they may not be useful:I was planning to then test whether or not run-time detection of CPU features was viable, but with the above results it may not even be worth asking.