selftests/bpf: Add benchmark for local_storage get

Add a benchmarks to demonstrate the performance cliff for local_storage
get as the number of local_storage maps increases beyond current
local_storage implementation's cache size.

"sequential get" and "interleaved get" benchmarks are added, both of
which do many bpf_task_storage_get calls on sets of task local_storage
maps of various counts, while considering a single specific map to be
'important' and counting task_storage_gets to the important map
separately in addition to normal 'hits' count of all gets. Goal here is
to mimic scenario where a particular program using one map - the
important one - is running on a system where many other local_storage
maps exist and are accessed often.

While "sequential get" benchmark does bpf_task_storage_get for map 0, 1,
..., {9, 99, 999} in order, "interleaved" benchmark interleaves 4
bpf_task_storage_gets for the important map for every 10 map gets. This
is meant to highlight performance differences when important map is
accessed far more frequently than non-important maps.

A "hashmap control" benchmark is also included for easy comparison of
standard bpf hashmap lookup vs local_storage get. The benchmark is
similar to "sequential get", but creates and uses BPF_MAP_TYPE_HASH
instead of local storage. Only one inner map is created - a hashmap
meant to hold tid -> data mapping for all tasks. Size of the hashmap is
hardcoded to my system's PID_MAX_LIMIT (4,194,304). The number of these
keys which are actually fetched as part of the benchmark is
configurable.

Addition of this benchmark is inspired by conversation with Alexei in a
previous patchset's thread [0], which highlighted the need for such a
benchmark to motivate and validate improvements to local_storage
implementation. My approach in that series focused on improving
performance for explicitly-marked 'important' maps and was rejected
with feedback to make more generally-applicable improvements while
avoiding explicitly marking maps as important. Thus the benchmark
reports both general and important-map-focused metrics, so effect of
future work on both is clear.

Regarding the benchmark results. On a powerful system (Skylake, 20
cores, 256gb ram):

Hashmap Control
===============
num keys: 10
hashmap (control) sequential get: hits throughput: 20.900 ± 0.334 M ops/s, hits latency: 47.847 ns/op, important_hits throughput: 20.900 ± 0.334 M ops/s

num keys: 1000
hashmap (control) sequential get: hits throughput: 13.758 ± 0.219 M ops/s, hits latency: 72.683 ns/op, important_hits throughput: 13.758 ± 0.219 M ops/s

num keys: 10000
hashmap (control) sequential get: hits throughput: 6.995 ± 0.034 M ops/s, hits latency: 142.959 ns/op, important_hits throughput: 6.995 ± 0.034 M ops/s

num keys: 100000
hashmap (control) sequential get: hits throughput: 4.452 ± 0.371 M ops/s, hits latency: 224.635 ns/op, important_hits throughput: 4.452 ± 0.371 M ops/s

num keys: 4194304
hashmap (control) sequential get: hits throughput: 3.043 ± 0.033 M ops/s, hits latency: 328.587 ns/op, important_hits throughput: 3.043 ± 0.033 M ops/s

Local Storage
=============
num_maps: 1
local_storage cache sequential get: hits throughput: 47.298 ± 0.180 M ops/s, hits latency: 21.142 ns/op, important_hits throughput: 47.298 ± 0.180 M ops/s
local_storage cache interleaved get: hits throughput: 55.277 ± 0.888 M ops/s, hits latency: 18.091 ns/op, important_hits throughput: 55.277 ± 0.888 M ops/s

num_maps: 10
local_storage cache sequential get: hits throughput: 40.240 ± 0.802 M ops/s, hits latency: 24.851 ns/op, important_hits throughput: 4.024 ± 0.080 M ops/s
local_storage cache interleaved get: hits throughput: 48.701 ± 0.722 M ops/s, hits latency: 20.533 ns/op, important_hits throughput: 17.393 ± 0.258 M ops/s

num_maps: 16
local_storage cache sequential get: hits throughput: 44.515 ± 0.708 M ops/s, hits latency: 22.464 ns/op, important_hits throughput: 2.782 ± 0.044 M ops/s
local_storage cache interleaved get: hits throughput: 49.553 ± 2.260 M ops/s, hits latency: 20.181 ns/op, important_hits throughput: 15.767 ± 0.719 M ops/s

num_maps: 17
local_storage cache sequential get: hits throughput: 38.778 ± 0.302 M ops/s, hits latency: 25.788 ns/op, important_hits throughput: 2.284 ± 0.018 M ops/s
local_storage cache interleaved get: hits throughput: 43.848 ± 1.023 M ops/s, hits latency: 22.806 ns/op, important_hits throughput: 13.349 ± 0.311 M ops/s

num_maps: 24
local_storage cache sequential get: hits throughput: 19.317 ± 0.568 M ops/s, hits latency: 51.769 ns/op, important_hits throughput: 0.806 ± 0.024 M ops/s
local_storage cache interleaved get: hits throughput: 24.397 ± 0.272 M ops/s, hits latency: 40.989 ns/op, important_hits throughput: 6.863 ± 0.077 M ops/s

num_maps: 32
local_storage cache sequential get: hits throughput: 13.333 ± 0.135 M ops/s, hits latency: 75.000 ns/op, important_hits throughput: 0.417 ± 0.004 M ops/s
local_storage cache interleaved get: hits throughput: 16.898 ± 0.383 M ops/s, hits latency: 59.178 ns/op, important_hits throughput: 4.717 ± 0.107 M ops/s

num_maps: 100
local_storage cache sequential get: hits throughput: 6.360 ± 0.107 M ops/s, hits latency: 157.233 ns/op, important_hits throughput: 0.064 ± 0.001 M ops/s
local_storage cache interleaved get: hits throughput: 7.303 ± 0.362 M ops/s, hits latency: 136.930 ns/op, important_hits throughput: 1.907 ± 0.094 M ops/s

num_maps: 1000
local_storage cache sequential get: hits throughput: 0.452 ± 0.010 M ops/s, hits latency: 2214.022 ns/op, important_hits throughput: 0.000 ± 0.000 M ops/s
local_storage cache interleaved get: hits throughput: 0.542 ± 0.007 M ops/s, hits latency: 1843.341 ns/op, important_hits throughput: 0.136 ± 0.002 M ops/s

Looking at the "sequential get" results, it's clear that as the
number of task local_storage maps grows beyond the current cache size
(16), there's a significant reduction in hits throughput. Note that
current local_storage implementation assigns a cache_idx to maps as they
are created. Since "sequential get" is creating maps 0..n in order and
then doing bpf_task_storage_get calls in the same order, the benchmark
is effectively ensuring that a map will not be in cache when the program
tries to access it.

For "interleaved get" results, important-map hits throughput is greatly
increased as the important map is more likely to be in cache by virtue
of being accessed far more frequently. Throughput still reduces as #
maps increases, though.

To get a sense of the overhead of the benchmark program, I
commented out bpf_task_storage_get/bpf_map_lookup_elem in
local_storage_bench.c and ran the benchmark on the same host as the
'real' run. Results:

Hashmap Control
===============
num keys: 10
hashmap (control) sequential get: hits throughput: 54.288 ± 0.655 M ops/s, hits latency: 18.420 ns/op, important_hits throughput: 54.288 ± 0.655 M ops/s

num keys: 1000
hashmap (control) sequential get: hits throughput: 52.913 ± 0.519 M ops/s, hits latency: 18.899 ns/op, important_hits throughput: 52.913 ± 0.519 M ops/s

num keys: 10000
hashmap (control) sequential get: hits throughput: 53.480 ± 1.235 M ops/s, hits latency: 18.699 ns/op, important_hits throughput: 53.480 ± 1.235 M ops/s

num keys: 100000
hashmap (control) sequential get: hits throughput: 54.982 ± 1.902 M ops/s, hits latency: 18.188 ns/op, important_hits throughput: 54.982 ± 1.902 M ops/s

num keys: 4194304
hashmap (control) sequential get: hits throughput: 50.858 ± 0.707 M ops/s, hits latency: 19.662 ns/op, important_hits throughput: 50.858 ± 0.707 M ops/s

Local Storage
=============
num_maps: 1
local_storage cache sequential get: hits throughput: 110.990 ± 4.828 M ops/s, hits latency: 9.010 ns/op, important_hits throughput: 110.990 ± 4.828 M ops/s
local_storage cache interleaved get: hits throughput: 161.057 ± 4.090 M ops/s, hits latency: 6.209 ns/op, important_hits throughput: 161.057 ± 4.090 M ops/s

num_maps: 10
local_storage cache sequential get: hits throughput: 112.930 ± 1.079 M ops/s, hits latency: 8.855 ns/op, important_hits throughput: 11.293 ± 0.108 M ops/s
local_storage cache interleaved get: hits throughput: 115.841 ± 2.088 M ops/s, hits latency: 8.633 ns/op, important_hits throughput: 41.372 ± 0.746 M ops/s

num_maps: 16
local_storage cache sequential get: hits throughput: 115.653 ± 0.416 M ops/s, hits latency: 8.647 ns/op, important_hits throughput: 7.228 ± 0.026 M ops/s
local_storage cache interleaved get: hits throughput: 138.717 ± 1.649 M ops/s, hits latency: 7.209 ns/op, important_hits throughput: 44.137 ± 0.525 M ops/s

num_maps: 17
local_storage cache sequential get: hits throughput: 112.020 ± 1.649 M ops/s, hits latency: 8.927 ns/op, important_hits throughput: 6.598 ± 0.097 M ops/s
local_storage cache interleaved get: hits throughput: 128.089 ± 1.960 M ops/s, hits latency: 7.807 ns/op, important_hits throughput: 38.995 ± 0.597 M ops/s

num_maps: 24
local_storage cache sequential get: hits throughput: 92.447 ± 5.170 M ops/s, hits latency: 10.817 ns/op, important_hits throughput: 3.855 ± 0.216 M ops/s
local_storage cache interleaved get: hits throughput: 128.844 ± 2.808 M ops/s, hits latency: 7.761 ns/op, important_hits throughput: 36.245 ± 0.790 M ops/s

num_maps: 32
local_storage cache sequential get: hits throughput: 102.042 ± 1.462 M ops/s, hits latency: 9.800 ns/op, important_hits throughput: 3.194 ± 0.046 M ops/s
local_storage cache interleaved get: hits throughput: 126.577 ± 1.818 M ops/s, hits latency: 7.900 ns/op, important_hits throughput: 35.332 ± 0.507 M ops/s

num_maps: 100
local_storage cache sequential get: hits throughput: 111.327 ± 1.401 M ops/s, hits latency: 8.983 ns/op, important_hits throughput: 1.113 ± 0.014 M ops/s
local_storage cache interleaved get: hits throughput: 131.327 ± 1.339 M ops/s, hits latency: 7.615 ns/op, important_hits throughput: 34.302 ± 0.350 M ops/s

num_maps: 1000
local_storage cache sequential get: hits throughput: 101.978 ± 0.563 M ops/s, hits latency: 9.806 ns/op, important_hits throughput: 0.102 ± 0.001 M ops/s
local_storage cache interleaved get: hits throughput: 141.084 ± 1.098 M ops/s, hits latency: 7.088 ns/op, important_hits throughput: 35.430 ± 0.276 M ops/s

Adjusting for overhead, latency numbers for "hashmap control" and
"sequential get" are:

hashmap_control_1k: ~53.8ns
hashmap_control_10k: ~124.2ns
hashmap_control_100k: ~206.5ns
sequential_get_1: ~12.1ns
sequential_get_10: ~16.0ns
sequential_get_16: ~13.8ns
sequential_get_17: ~16.8ns
sequential_get_24: ~40.9ns
sequential_get_32: ~65.2ns
sequential_get_100: ~148.2ns
sequential_get_1000: ~2204ns

Clearly demonstrating a cliff.

In the discussion for v1 of this patch, Alexei noted that local_storage
was 2.5x faster than a large hashmap when initially implemented [1]. The
benchmark results show that local_storage is 5-10x faster: a
long-running BPF application putting some pid-specific info into a
hashmap for each pid it sees will probably see on the order of 10-100k
pids. Bench numbers for hashmaps of this size are ~10x slower than
sequential_get_16, but as the number of local_storage maps grows far
past local_storage cache size the performance advantage shrinks and
eventually reverses.

When running the benchmarks it may be necessary to bump 'open files'
ulimit for a successful run.

[0]: https://lore.kernel.org/all/20220420002143.1096548-1-davemarchevsky@fb.com
[1]: https://lore.kernel.org/bpf/20220511173305.ftldpn23m4ski3d3@MBP-98dd607d3435.dhcp.thefacebook.com/

Signed-off-by: Dave Marchevsky <davemarchevsky@fb.com>
Link: https://lore.kernel.org/r/20220620222554.270578-1-davemarchevsky@fb.com
Signed-off-by: Alexei Starovoitov <ast@kernel.org>

selftest/bpf/benchs: Add bpf_loop benchmark

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

bpf/benchs: Add benchmarks for comparing hashmap lookups w/ vs. w/out bloom filter

This patch adds benchmark tests for comparing the performance of hashmap
lookups without the bloom filter vs. hashmap lookups with the bloom filter.

Checking the bloom filter first for whether the element exists should
overall enable a higher throughput for hashmap lookups, since if the
element does not exist in the bloom filter, we can avoid a costly lookup in
the hashmap.

On average, using 5 hash functions in the bloom filter tended to perform
the best across the widest range of different entry sizes. The benchmark
results using 5 hash functions (running on 8 threads on a machine with one
numa node, and taking the average of 3 runs) were roughly as follows:

value_size = 4 bytes -
10k entries: 30% faster
50k entries: 40% faster
100k entries: 40% faster
500k entres: 70% faster
1 million entries: 90% faster
5 million entries: 140% faster

value_size = 8 bytes -
10k entries: 30% faster
50k entries: 40% faster
100k entries: 50% faster
500k entres: 80% faster
1 million entries: 100% faster
5 million entries: 150% faster

value_size = 16 bytes -
10k entries: 20% faster
50k entries: 30% faster
100k entries: 35% faster
500k entres: 65% faster
1 million entries: 85% faster
5 million entries: 110% faster

value_size = 40 bytes -
10k entries: 5% faster
50k entries: 15% faster
100k entries: 20% faster
500k entres: 65% faster
1 million entries: 75% faster
5 million entries: 120% faster

Signed-off-by: Joanne Koong <joannekoong@fb.com>
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Link: https://lore.kernel.org/bpf/20211027234504.30744-6-joannekoong@fb.com

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