moves where the exit flag is checked #185
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jeffwashington
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May 16, 2024
… (solana-labs#425) implements weighted shuffle using binary tree (#185) This is partial port of firedancer's implementation of weighted shuffle: https://github.com/firedancer-io/firedancer/blob/3401bfc26/src/ballet/wsample/fd_wsample.c Though Fenwick trees use less space, inverse queries require an additional O(log n) factor for binary search resulting an overall O(n log n log n) performance for weighted shuffle. This commit instead uses a binary tree where each node contains the sum of all weights in its left sub-tree. The weights themselves are implicitly stored at the leaves. Inverse queries and updates to the tree all can be done O(log n) resulting an overall O(n log n) weighted shuffle implementation. Based on benchmarks, this results in 24% improvement in WeightedShuffle::shuffle: Fenwick tree: test bench_weighted_shuffle_new ... bench: 36,686 ns/iter (+/- 191) test bench_weighted_shuffle_shuffle ... bench: 342,625 ns/iter (+/- 4,067) Binary tree: test bench_weighted_shuffle_new ... bench: 59,131 ns/iter (+/- 362) test bench_weighted_shuffle_shuffle ... bench: 260,194 ns/iter (+/- 11,195) Though WeightedShuffle::new is now slower, it generally can be cached and reused as in Turbine: https://github.com/anza-xyz/agave/blob/b3fd87fe8/turbine/src/cluster_nodes.rs#L68 Additionally the new code has better asymptotic performance. For example with 20_000 weights WeightedShuffle::shuffle is 31% faster: Fenwick tree: test bench_weighted_shuffle_new ... bench: 255,071 ns/iter (+/- 9,591) test bench_weighted_shuffle_shuffle ... bench: 2,466,058 ns/iter (+/- 9,873) Binary tree: test bench_weighted_shuffle_new ... bench: 830,727 ns/iter (+/- 10,210) test bench_weighted_shuffle_shuffle ... bench: 1,696,160 ns/iter (+/- 75,271) (cherry picked from commit b6d2237) Co-authored-by: behzad nouri <[email protected]>
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