implements weighted shuffle using binary tree #185

behzadnouri · 2024-03-11T18:06:33Z

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

Problem

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.

Summary of Changes

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)

codecov-commenter · 2024-03-11T20:21:07Z

Codecov Report

Attention: Patch coverage is 99.00000% with 1 lines in your changes are missing coverage. Please review.

Project coverage is 81.9%. Comparing base (b3fd87f) to head (18531c9).
Report is 1 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #185   +/-   ##
=======================================
  Coverage    81.9%    81.9%           
=======================================
  Files         837      837           
  Lines      226792   226842   +50     
=======================================
+ Hits       185795   185851   +56     
+ Misses      40997    40991    -6

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)

behzadnouri · 2024-03-14T23:50:32Z

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

The rest is implemented in #259.

gregcusack

looks good to me. is it worth periodically caching the weighted shuffle in push_active_set.rs since it takes longer to build? Although the shuffle op more than makes up for the slower build.

I also tested a few other combinations on my end that worked as expected.

gregcusack · 2024-03-15T19:41:46Z

gossip/src/weighted_shuffle.rs

-            self.arr[k] -= weight;
-            k += k & k.wrapping_neg();
+    fn remove(&mut self, k: usize, weight: T) {
+        self.weight -= weight;


can this result in a negative self.weight? or an underflow if self.weight is unsigned

no, unless there is a bug in this code.

self.weight is meant to be sum of all weights.
This function remove is only meant to be called when an item is sampled and its corresponding weight is removed from the tree. Because before sampling the item's weight is included in self.weight, self.weight >= weight.

gregcusack · 2024-03-15T22:03:20Z

gossip/src/weighted_shuffle.rs

+        while index < self.tree.len() {
+            if val < self.tree[index] {
+                weight = self.tree[index];
+                index = (index << 1) + 1;


possible over flow here? but i the tree would have to be insanely big to have any issues. so should be fine.

behzadnouri · 2024-03-23T13:52:51Z

is it worth periodically caching the weighted shuffle in push_active_set.rs since it takes longer to build?

The following commit #259 will speed up WeightedShuffle::new as well.

This is port of firedancer's implementation of weighted shuffle: https://github.com/firedancer-io/firedancer/blob/3401bfc26/src/ballet/wsample/fd_wsample.c anza-xyz#185 implemented weighted shuffle using binary tree. Though asymptotically a binary tree has better performance, compared to a Fenwick tree, it is less cache local resulting in smaller improvements and in particular slower WeightedShuffle::new. In order to improve cache locality and reduce the overheads of traversing the tree, this commit instead uses a generalized N-ary tree with fanout of 16, showing significant improvements in both WeightedShuffle::new and WeightedShuffle::shuffle. With 4000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 36,244 ns/iter (+/- 243) test bench_weighted_shuffle_shuffle ... bench: 149,082 ns/iter (+/- 1,474) Binary tree: test bench_weighted_shuffle_new ... bench: 58,514 ns/iter (+/- 229) test bench_weighted_shuffle_shuffle ... bench: 269,961 ns/iter (+/- 16,446) Fenwick tree: test bench_weighted_shuffle_new ... bench: 39,413 ns/iter (+/- 179) test bench_weighted_shuffle_shuffle ... bench: 364,771 ns/iter (+/- 2,078) The improvements become even more significant as there are more items to shuffle. With 20_000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 200,659 ns/iter (+/- 4,395) test bench_weighted_shuffle_shuffle ... bench: 941,928 ns/iter (+/- 26,492) Binary tree: test bench_weighted_shuffle_new ... bench: 881,114 ns/iter (+/- 12,343) test bench_weighted_shuffle_shuffle ... bench: 1,822,257 ns/iter (+/- 12,772) Fenwick tree: test bench_weighted_shuffle_new ... bench: 276,936 ns/iter (+/- 14,692) test bench_weighted_shuffle_shuffle ... bench: 2,644,713 ns/iter (+/- 49,252)

This is port of firedancer's implementation of weighted shuffle: https://github.com/firedancer-io/firedancer/blob/3401bfc26/src/ballet/wsample/fd_wsample.c #185 implemented weighted shuffle using binary tree. Though asymptotically a binary tree has better performance, compared to a Fenwick tree, it has less cache locality resulting in smaller improvements and in particular slower WeightedShuffle::new. In order to improve cache locality and reduce the overheads of traversing the tree, this commit instead uses a generalized N-ary tree with fanout of 16, showing significant improvements in both WeightedShuffle::new and WeightedShuffle::shuffle. With 4000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 36,244 ns/iter (+/- 243) test bench_weighted_shuffle_shuffle ... bench: 149,082 ns/iter (+/- 1,474) Binary tree: test bench_weighted_shuffle_new ... bench: 58,514 ns/iter (+/- 229) test bench_weighted_shuffle_shuffle ... bench: 269,961 ns/iter (+/- 16,446) Fenwick tree: test bench_weighted_shuffle_new ... bench: 39,413 ns/iter (+/- 179) test bench_weighted_shuffle_shuffle ... bench: 364,771 ns/iter (+/- 2,078) The improvements become even more significant as there are more items to shuffle. With 20_000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 200,659 ns/iter (+/- 4,395) test bench_weighted_shuffle_shuffle ... bench: 941,928 ns/iter (+/- 26,492) Binary tree: test bench_weighted_shuffle_new ... bench: 881,114 ns/iter (+/- 12,343) test bench_weighted_shuffle_shuffle ... bench: 1,822,257 ns/iter (+/- 12,772) Fenwick tree: test bench_weighted_shuffle_new ... bench: 276,936 ns/iter (+/- 14,692) test bench_weighted_shuffle_shuffle ... bench: 2,644,713 ns/iter (+/- 49,252)

mergify · 2024-03-26T05:32:55Z

Backports to the beta branch are to be avoided unless absolutely necessary for fixing bugs, security issues, and perf regressions. Changes intended for backport should be structured such that a minimum effective diff can be committed separately from any refactoring, plumbing, cleanup, etc that are not strictly necessary to achieve the goal. Any of the latter should go only into master and ride the normal stabilization schedule. Exceptions include CI/metrics changes, CLI improvements and documentation updates on a case by case basis.

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)

… (#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]>

This is port of firedancer's implementation of weighted shuffle: https://github.com/firedancer-io/firedancer/blob/3401bfc26/src/ballet/wsample/fd_wsample.c #185 implemented weighted shuffle using binary tree. Though asymptotically a binary tree has better performance, compared to a Fenwick tree, it has less cache locality resulting in smaller improvements and in particular slower WeightedShuffle::new. In order to improve cache locality and reduce the overheads of traversing the tree, this commit instead uses a generalized N-ary tree with fanout of 16, showing significant improvements in both WeightedShuffle::new and WeightedShuffle::shuffle. With 4000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 36,244 ns/iter (+/- 243) test bench_weighted_shuffle_shuffle ... bench: 149,082 ns/iter (+/- 1,474) Binary tree: test bench_weighted_shuffle_new ... bench: 58,514 ns/iter (+/- 229) test bench_weighted_shuffle_shuffle ... bench: 269,961 ns/iter (+/- 16,446) Fenwick tree: test bench_weighted_shuffle_new ... bench: 39,413 ns/iter (+/- 179) test bench_weighted_shuffle_shuffle ... bench: 364,771 ns/iter (+/- 2,078) The improvements become even more significant as there are more items to shuffle. With 20_000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 200,659 ns/iter (+/- 4,395) test bench_weighted_shuffle_shuffle ... bench: 941,928 ns/iter (+/- 26,492) Binary tree: test bench_weighted_shuffle_new ... bench: 881,114 ns/iter (+/- 12,343) test bench_weighted_shuffle_shuffle ... bench: 1,822,257 ns/iter (+/- 12,772) Fenwick tree: test bench_weighted_shuffle_new ... bench: 276,936 ns/iter (+/- 14,692) test bench_weighted_shuffle_shuffle ... bench: 2,644,713 ns/iter (+/- 49,252) (cherry picked from commit 30eecd6)

#429) implements weighted shuffle using N-ary tree (#259) This is port of firedancer's implementation of weighted shuffle: https://github.com/firedancer-io/firedancer/blob/3401bfc26/src/ballet/wsample/fd_wsample.c #185 implemented weighted shuffle using binary tree. Though asymptotically a binary tree has better performance, compared to a Fenwick tree, it has less cache locality resulting in smaller improvements and in particular slower WeightedShuffle::new. In order to improve cache locality and reduce the overheads of traversing the tree, this commit instead uses a generalized N-ary tree with fanout of 16, showing significant improvements in both WeightedShuffle::new and WeightedShuffle::shuffle. With 4000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 36,244 ns/iter (+/- 243) test bench_weighted_shuffle_shuffle ... bench: 149,082 ns/iter (+/- 1,474) Binary tree: test bench_weighted_shuffle_new ... bench: 58,514 ns/iter (+/- 229) test bench_weighted_shuffle_shuffle ... bench: 269,961 ns/iter (+/- 16,446) Fenwick tree: test bench_weighted_shuffle_new ... bench: 39,413 ns/iter (+/- 179) test bench_weighted_shuffle_shuffle ... bench: 364,771 ns/iter (+/- 2,078) The improvements become even more significant as there are more items to shuffle. With 20_000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 200,659 ns/iter (+/- 4,395) test bench_weighted_shuffle_shuffle ... bench: 941,928 ns/iter (+/- 26,492) Binary tree: test bench_weighted_shuffle_new ... bench: 881,114 ns/iter (+/- 12,343) test bench_weighted_shuffle_shuffle ... bench: 1,822,257 ns/iter (+/- 12,772) Fenwick tree: test bench_weighted_shuffle_new ... bench: 276,936 ns/iter (+/- 14,692) test bench_weighted_shuffle_shuffle ... bench: 2,644,713 ns/iter (+/- 49,252) (cherry picked from commit 30eecd6) Co-authored-by: behzad nouri <[email protected]>

#429) implements weighted shuffle using N-ary tree (#259) This is port of firedancer's implementation of weighted shuffle: https://github.com/firedancer-io/firedancer/blob/3401bfc26/src/ballet/wsample/fd_wsample.c anza-xyz#185 implemented weighted shuffle using binary tree. Though asymptotically a binary tree has better performance, compared to a Fenwick tree, it has less cache locality resulting in smaller improvements and in particular slower WeightedShuffle::new. In order to improve cache locality and reduce the overheads of traversing the tree, this commit instead uses a generalized N-ary tree with fanout of 16, showing significant improvements in both WeightedShuffle::new and WeightedShuffle::shuffle. With 4000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 36,244 ns/iter (+/- 243) test bench_weighted_shuffle_shuffle ... bench: 149,082 ns/iter (+/- 1,474) Binary tree: test bench_weighted_shuffle_new ... bench: 58,514 ns/iter (+/- 229) test bench_weighted_shuffle_shuffle ... bench: 269,961 ns/iter (+/- 16,446) Fenwick tree: test bench_weighted_shuffle_new ... bench: 39,413 ns/iter (+/- 179) test bench_weighted_shuffle_shuffle ... bench: 364,771 ns/iter (+/- 2,078) The improvements become even more significant as there are more items to shuffle. With 20_000 weights: N-ary tree (fanout 16): test bench_weighted_shuffle_new ... bench: 200,659 ns/iter (+/- 4,395) test bench_weighted_shuffle_shuffle ... bench: 941,928 ns/iter (+/- 26,492) Binary tree: test bench_weighted_shuffle_new ... bench: 881,114 ns/iter (+/- 12,343) test bench_weighted_shuffle_shuffle ... bench: 1,822,257 ns/iter (+/- 12,772) Fenwick tree: test bench_weighted_shuffle_new ... bench: 276,936 ns/iter (+/- 14,692) test bench_weighted_shuffle_shuffle ... bench: 2,644,713 ns/iter (+/- 49,252) (cherry picked from commit 30eecd6) Co-authored-by: behzad nouri <[email protected]>

…a-xyz#185) (anza-xyz#425) implements weighted shuffle using binary tree (anza-xyz#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]>

behzadnouri force-pushed the weighted-shuffle-tree branch from e5d304a to 9c3461f Compare March 11, 2024 18:15

behzadnouri changed the title ~~implements weighted shuffle using binary search tree~~ implements weighted shuffle using binary tree Mar 11, 2024

behzadnouri force-pushed the weighted-shuffle-tree branch from 9c3461f to 9558d6d Compare March 11, 2024 18:50

behzadnouri force-pushed the weighted-shuffle-tree branch 2 times, most recently from 8e8e949 to 52e26b9 Compare March 14, 2024 21:29

behzadnouri force-pushed the weighted-shuffle-tree branch from 52e26b9 to 18531c9 Compare March 14, 2024 23:41

behzadnouri requested a review from gregcusack March 14, 2024 23:42

gregcusack approved these changes Mar 15, 2024

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behzadnouri merged commit b6d2237 into anza-xyz:master Mar 23, 2024
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behzadnouri deleted the weighted-shuffle-tree branch March 23, 2024 13:53

behzadnouri mentioned this pull request Mar 25, 2024

implements weighted shuffle using N-ary tree #259

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behzadnouri added the v1.18 label Mar 26, 2024

mergify bot mentioned this pull request Mar 26, 2024

v1.18: implements weighted shuffle using N-ary tree (backport of #259) #429

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willhickey mentioned this pull request Mar 28, 2024

v1.18 commits - please ignore #475

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implements weighted shuffle using binary tree #185

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implements weighted shuffle using binary tree #185

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