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Fix checkpatch error: "ERROR: Bad function definition - void foo()
should probably be void foo(void)". Most replacements are done by
the following command:
sed -i 's#\([a-z]\)()$#\1(void)#g' testing/selftests/bpf/benchs/*.c
Signed-off-by: Hou Tao <houtao1@huawei.com>
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Link: https://lore.kernel.org/bpf/20211210141652.877186-3-houtao1@huawei.com
Add benchmark to measure the throughput and latency of the bpf_loop
call.
Testing this on my dev machine on 1 thread, the data is as follows:
nr_loops: 10
bpf_loop - throughput: 198.519 ± 0.155 M ops/s, latency: 5.037 ns/op
nr_loops: 100
bpf_loop - throughput: 247.448 ± 0.305 M ops/s, latency: 4.041 ns/op
nr_loops: 500
bpf_loop - throughput: 260.839 ± 0.380 M ops/s, latency: 3.834 ns/op
nr_loops: 1000
bpf_loop - throughput: 262.806 ± 0.629 M ops/s, latency: 3.805 ns/op
nr_loops: 5000
bpf_loop - throughput: 264.211 ± 1.508 M ops/s, latency: 3.785 ns/op
nr_loops: 10000
bpf_loop - throughput: 265.366 ± 3.054 M ops/s, latency: 3.768 ns/op
nr_loops: 50000
bpf_loop - throughput: 235.986 ± 20.205 M ops/s, latency: 4.238 ns/op
nr_loops: 100000
bpf_loop - throughput: 264.482 ± 0.279 M ops/s, latency: 3.781 ns/op
nr_loops: 500000
bpf_loop - throughput: 309.773 ± 87.713 M ops/s, latency: 3.228 ns/op
nr_loops: 1000000
bpf_loop - throughput: 262.818 ± 4.143 M ops/s, latency: 3.805 ns/op
>From this data, we can see that the latency per loop decreases as the
number of loops increases. On this particular machine, each loop had an
overhead of about ~4 ns, and we were able to run ~250 million loops
per second.
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/20211130030622.4131246-5-joannekoong@fb.com
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
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
Hou Tao
Joanne Koong
Andrii Nakryiko