2 commits

Author	SHA1	Message	Date
Joanne Koong	57fd1c63c9	bpf/benchs: Add benchmark tests for bloom filter throughput + false positive ... This patch adds benchmark tests for the throughput (for lookups + updates) and the false positive rate of bloom filter lookups, as well as some minor refactoring of the bash script for running the benchmarks. These benchmarks show that as the number of hash functions increases, the throughput and the false positive rate of the bloom filter decreases. >From the benchmark data, the approximate average false-positive rates are roughly as follows: 1 hash function = ~30% 2 hash functions = ~15% 3 hash functions = ~5% 4 hash functions = ~2.5% 5 hash functions = ~1% 6 hash functions = ~0.5% 7 hash functions = ~0.35% 8 hash functions = ~0.15% 9 hash functions = ~0.1% 10 hash functions = ~0% For reference data, the benchmarks run on one thread on a machine with one numa node for 1 to 5 hash functions for 8-byte and 64-byte values are as follows: 1 hash function: 50k entries 8-byte value Lookups - 51.1 M/s operations Updates - 33.6 M/s operations False positive rate: 24.15% 64-byte value Lookups - 15.7 M/s operations Updates - 15.1 M/s operations False positive rate: 24.2% 100k entries 8-byte value Lookups - 51.0 M/s operations Updates - 33.4 M/s operations False positive rate: 24.04% 64-byte value Lookups - 15.6 M/s operations Updates - 14.6 M/s operations False positive rate: 24.06% 500k entries 8-byte value Lookups - 50.5 M/s operations Updates - 33.1 M/s operations False positive rate: 27.45% 64-byte value Lookups - 15.6 M/s operations Updates - 14.2 M/s operations False positive rate: 27.42% 1 mil entries 8-byte value Lookups - 49.7 M/s operations Updates - 32.9 M/s operations False positive rate: 27.45% 64-byte value Lookups - 15.4 M/s operations Updates - 13.7 M/s operations False positive rate: 27.58% 2.5 mil entries 8-byte value Lookups - 47.2 M/s operations Updates - 31.8 M/s operations False positive rate: 30.94% 64-byte value Lookups - 15.3 M/s operations Updates - 13.2 M/s operations False positive rate: 30.95% 5 mil entries 8-byte value Lookups - 41.1 M/s operations Updates - 28.1 M/s operations False positive rate: 31.01% 64-byte value Lookups - 13.3 M/s operations Updates - 11.4 M/s operations False positive rate: 30.98% 2 hash functions: 50k entries 8-byte value Lookups - 34.1 M/s operations Updates - 20.1 M/s operations False positive rate: 9.13% 64-byte value Lookups - 8.4 M/s operations Updates - 7.9 M/s operations False positive rate: 9.21% 100k entries 8-byte value Lookups - 33.7 M/s operations Updates - 18.9 M/s operations False positive rate: 9.13% 64-byte value Lookups - 8.4 M/s operations Updates - 7.7 M/s operations False positive rate: 9.19% 500k entries 8-byte value Lookups - 32.7 M/s operations Updates - 18.1 M/s operations False positive rate: 12.61% 64-byte value Lookups - 8.4 M/s operations Updates - 7.5 M/s operations False positive rate: 12.61% 1 mil entries 8-byte value Lookups - 30.6 M/s operations Updates - 18.9 M/s operations False positive rate: 12.54% 64-byte value Lookups - 8.0 M/s operations Updates - 7.0 M/s operations False positive rate: 12.52% 2.5 mil entries 8-byte value Lookups - 25.3 M/s operations Updates - 16.7 M/s operations False positive rate: 16.77% 64-byte value Lookups - 7.9 M/s operations Updates - 6.5 M/s operations False positive rate: 16.88% 5 mil entries 8-byte value Lookups - 20.8 M/s operations Updates - 14.7 M/s operations False positive rate: 16.78% 64-byte value Lookups - 7.0 M/s operations Updates - 6.0 M/s operations False positive rate: 16.78% 3 hash functions: 50k entries 8-byte value Lookups - 25.1 M/s operations Updates - 14.6 M/s operations False positive rate: 7.65% 64-byte value Lookups - 5.8 M/s operations Updates - 5.5 M/s operations False positive rate: 7.58% 100k entries 8-byte value Lookups - 24.7 M/s operations Updates - 14.1 M/s operations False positive rate: 7.71% 64-byte value Lookups - 5.8 M/s operations Updates - 5.3 M/s operations False positive rate: 7.62% 500k entries 8-byte value Lookups - 22.9 M/s operations Updates - 13.9 M/s operations False positive rate: 2.62% 64-byte value Lookups - 5.6 M/s operations Updates - 4.8 M/s operations False positive rate: 2.7% 1 mil entries 8-byte value Lookups - 19.8 M/s operations Updates - 12.6 M/s operations False positive rate: 2.60% 64-byte value Lookups - 5.3 M/s operations Updates - 4.4 M/s operations False positive rate: 2.69% 2.5 mil entries 8-byte value Lookups - 16.2 M/s operations Updates - 10.7 M/s operations False positive rate: 4.49% 64-byte value Lookups - 4.9 M/s operations Updates - 4.1 M/s operations False positive rate: 4.41% 5 mil entries 8-byte value Lookups - 18.8 M/s operations Updates - 9.2 M/s operations False positive rate: 4.45% 64-byte value Lookups - 5.2 M/s operations Updates - 3.9 M/s operations False positive rate: 4.54% 4 hash functions: 50k entries 8-byte value Lookups - 19.7 M/s operations Updates - 11.1 M/s operations False positive rate: 1.01% 64-byte value Lookups - 4.4 M/s operations Updates - 4.0 M/s operations False positive rate: 1.00% 100k entries 8-byte value Lookups - 19.5 M/s operations Updates - 10.9 M/s operations False positive rate: 1.00% 64-byte value Lookups - 4.3 M/s operations Updates - 3.9 M/s operations False positive rate: 0.97% 500k entries 8-byte value Lookups - 18.2 M/s operations Updates - 10.6 M/s operations False positive rate: 2.05% 64-byte value Lookups - 4.3 M/s operations Updates - 3.7 M/s operations False positive rate: 2.05% 1 mil entries 8-byte value Lookups - 15.5 M/s operations Updates - 9.6 M/s operations False positive rate: 1.99% 64-byte value Lookups - 4.0 M/s operations Updates - 3.4 M/s operations False positive rate: 1.99% 2.5 mil entries 8-byte value Lookups - 13.8 M/s operations Updates - 7.7 M/s operations False positive rate: 3.91% 64-byte value Lookups - 3.7 M/s operations Updates - 3.6 M/s operations False positive rate: 3.78% 5 mil entries 8-byte value Lookups - 13.0 M/s operations Updates - 6.9 M/s operations False positive rate: 3.93% 64-byte value Lookups - 3.5 M/s operations Updates - 3.7 M/s operations False positive rate: 3.39% 5 hash functions: 50k entries 8-byte value Lookups - 16.4 M/s operations Updates - 9.1 M/s operations False positive rate: 0.78% 64-byte value Lookups - 3.5 M/s operations Updates - 3.2 M/s operations False positive rate: 0.77% 100k entries 8-byte value Lookups - 16.3 M/s operations Updates - 9.0 M/s operations False positive rate: 0.79% 64-byte value Lookups - 3.5 M/s operations Updates - 3.2 M/s operations False positive rate: 0.78% 500k entries 8-byte value Lookups - 15.1 M/s operations Updates - 8.8 M/s operations False positive rate: 1.82% 64-byte value Lookups - 3.4 M/s operations Updates - 3.0 M/s operations False positive rate: 1.78% 1 mil entries 8-byte value Lookups - 13.2 M/s operations Updates - 7.8 M/s operations False positive rate: 1.81% 64-byte value Lookups - 3.2 M/s operations Updates - 2.8 M/s operations False positive rate: 1.80% 2.5 mil entries 8-byte value Lookups - 10.5 M/s operations Updates - 5.9 M/s operations False positive rate: 0.29% 64-byte value Lookups - 3.2 M/s operations Updates - 2.4 M/s operations False positive rate: 0.28% 5 mil entries 8-byte value Lookups - 9.6 M/s operations Updates - 5.7 M/s operations False positive rate: 0.30% 64-byte value Lookups - 3.2 M/s operations Updates - 2.7 M/s operations False positive rate: 0.30% Signed-off-by: Joanne Koong <joannekoong@fb.com> Signed-off-by: Alexei Starovoitov <ast@kernel.org> Acked-by: Andrii Nakryiko <andrii@kernel.org> Link: https://lore.kernel.org/bpf/20211027234504.30744-5-joannekoong@fb.com	2021-10-28 13:22:49 -07:00
Andrii Nakryiko	8e7c2a023a	selftests/bpf: Add benchmark runner infrastructure ... While working on BPF ringbuf implementation, testing, and benchmarking, I've developed a pretty generic and modular benchmark runner, which seems to be generically useful, as I've already used it for one more purpose (testing fastest way to trigger BPF program, to minimize overhead of in-kernel code). This patch adds generic part of benchmark runner and sets up Makefile for extending it with more sets of benchmarks. Benchmarker itself operates by spinning up specified number of producer and consumer threads, setting up interval timer sending SIGALARM signal to application once a second. Every second, current snapshot with hits/drops counters are collected and stored in an array. Drops are useful for producer/consumer benchmarks in which producer might overwhelm consumers. Once test finishes after given amount of warm-up and testing seconds, mean and stddev are calculated (ignoring warm-up results) and is printed out to stdout. This setup seems to give consistent and accurate results. To validate behavior, I added two atomic counting tests: global and local. For global one, all the producer threads are atomically incrementing same counter as fast as possible. This, of course, leads to huge drop of performance once there is more than one producer thread due to CPUs fighting for the same memory location. Local counting, on the other hand, maintains one counter per each producer thread, incremented independently. Once per second, all counters are read and added together to form final "counting throughput" measurement. As expected, such setup demonstrates linear scalability with number of producers (as long as there are enough physical CPU cores, of course). See example output below. Also, this setup can nicely demonstrate disastrous effects of false sharing, if care is not taken to take those per-producer counters apart into independent cache lines. Demo output shows global counter first with 1 producer, then with 4. Both total and per-producer performance significantly drop. The last run is local counter with 4 producers, demonstrating near-perfect scalability. $ ./bench -a -w1 -d2 -p1 count-global Setting up benchmark 'count-global'... Benchmark 'count-global' started. Iter 0 ( 24.822us): hits 148.179M/s (148.179M/prod), drops 0.000M/s Iter 1 ( 37.939us): hits 149.308M/s (149.308M/prod), drops 0.000M/s Iter 2 (-10.774us): hits 150.717M/s (150.717M/prod), drops 0.000M/s Iter 3 ( 3.807us): hits 151.435M/s (151.435M/prod), drops 0.000M/s Summary: hits 150.488 ± 1.079M/s (150.488M/prod), drops 0.000 ± 0.000M/s $ ./bench -a -w1 -d2 -p4 count-global Setting up benchmark 'count-global'... Benchmark 'count-global' started. Iter 0 ( 60.659us): hits 53.910M/s ( 13.477M/prod), drops 0.000M/s Iter 1 (-17.658us): hits 53.722M/s ( 13.431M/prod), drops 0.000M/s Iter 2 ( 5.865us): hits 53.495M/s ( 13.374M/prod), drops 0.000M/s Iter 3 ( 0.104us): hits 53.606M/s ( 13.402M/prod), drops 0.000M/s Summary: hits 53.608 ± 0.113M/s ( 13.402M/prod), drops 0.000 ± 0.000M/s $ ./bench -a -w1 -d2 -p4 count-local Setting up benchmark 'count-local'... Benchmark 'count-local' started. Iter 0 ( 23.388us): hits 640.450M/s (160.113M/prod), drops 0.000M/s Iter 1 ( 2.291us): hits 605.661M/s (151.415M/prod), drops 0.000M/s Iter 2 ( -6.415us): hits 607.092M/s (151.773M/prod), drops 0.000M/s Iter 3 ( -1.361us): hits 601.796M/s (150.449M/prod), drops 0.000M/s Summary: hits 604.849 ± 2.739M/s (151.212M/prod), drops 0.000 ± 0.000M/s Benchmark runner supports setting thread affinity for producer and consumer threads. You can use -a flag for default CPU selection scheme, where first consumer gets CPU #0, next one gets CPU #1, and so on. Then producer threads pick up next CPU and increment one-by-one as well. But user can also specify a set of CPUs independently for producers and consumers with --prod-affinity 1,2-10,15 and --cons-affinity <set-of-cpus>. The latter allows to force producers and consumers to share same set of CPUs, if necessary. Signed-off-by: Andrii Nakryiko <andriin@fb.com> Signed-off-by: Alexei Starovoitov <ast@kernel.org> Acked-by: Yonghong Song <yhs@fb.com> Link: https://lore.kernel.org/bpf/20200512192445.2351848-3-andriin@fb.com	2020-05-13 12:19:38 -07:00