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Merge tag 'bpf-next-6.16' of git://git.kernel.org/pub/scm/linux/kernel/git/bpf/bpf-next
Pull bpf updates from Alexei Starovoitov:
- Fix and improve BTF deduplication of identical BTF types (Alan
Maguire and Andrii Nakryiko)
- Support up to 12 arguments in BPF trampoline on arm64 (Xu Kuohai and
Alexis Lothoré)
- Support load-acquire and store-release instructions in BPF JIT on
riscv64 (Andrea Parri)
- Fix uninitialized values in BPF_{CORE,PROBE}_READ macros (Anton
Protopopov)
- Streamline allowed helpers across program types (Feng Yang)
- Support atomic update for hashtab of BPF maps (Hou Tao)
- Implement json output for BPF helpers (Ihor Solodrai)
- Several s390 JIT fixes (Ilya Leoshkevich)
- Various sockmap fixes (Jiayuan Chen)
- Support mmap of vmlinux BTF data (Lorenz Bauer)
- Support BPF rbtree traversal and list peeking (Martin KaFai Lau)
- Tests for sockmap/sockhash redirection (Michal Luczaj)
- Introduce kfuncs for memory reads into dynptrs (Mykyta Yatsenko)
- Add support for dma-buf iterators in BPF (T.J. Mercier)
- The verifier support for __bpf_trap() (Yonghong Song)
* tag 'bpf-next-6.16' of git://git.kernel.org/pub/scm/linux/kernel/git/bpf/bpf-next: (135 commits)
bpf, arm64: Remove unused-but-set function and variable.
selftests/bpf: Add tests with stack ptr register in conditional jmp
bpf: Do not include stack ptr register in precision backtracking bookkeeping
selftests/bpf: enable many-args tests for arm64
bpf, arm64: Support up to 12 function arguments
bpf: Check rcu_read_lock_trace_held() in bpf_map_lookup_percpu_elem()
bpf: Avoid __bpf_prog_ret0_warn when jit fails
bpftool: Add support for custom BTF path in prog load/loadall
selftests/bpf: Add unit tests with __bpf_trap() kfunc
bpf: Warn with __bpf_trap() kfunc maybe due to uninitialized variable
bpf: Remove special_kfunc_set from verifier
selftests/bpf: Add test for open coded dmabuf_iter
selftests/bpf: Add test for dmabuf_iter
bpf: Add open coded dmabuf iterator
bpf: Add dmabuf iterator
dma-buf: Rename debugfs symbols
bpf: Fix error return value in bpf_copy_from_user_dynptr
libbpf: Use mmap to parse vmlinux BTF from sysfs
selftests: bpf: Add a test for mmapable vmlinux BTF
btf: Allow mmap of vmlinux btf
...
Static analysis found an issue in bench_htab_mem.c and sk_assign.c
cppcheck output before this patch:
tools/testing/selftests/bpf/benchs/bench_htab_mem.c:284:3: error: Resource leak: fd [resourceLeak]
tools/testing/selftests/bpf/prog_tests/sk_assign.c:41:3: error: Resource leak: tc [resourceLeak]
cppcheck output after this patch:
No resource leaks found
Fix the issue by closing the file descriptors fd and tc.
Signed-off-by: Malaya Kumar Rout <malayarout91@gmail.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20250421174405.26080-1-malayarout91@gmail.com
Add a 5-byte NOP uprobe trigger benchmark (x86_64 specific) to measure
uprobes/uretprobes on top of NOP5 instructions.
Signed-off-by: Jiri Olsa <jolsa@kernel.org>
Signed-off-by: Ingo Molnar <mingo@kernel.org>
Acked-by: Andrii Nakryiko <andrii@kernel.org>
Cc: Oleg Nesterov <oleg@redhat.com>
Cc: Song Liu <songliubraving@fb.com>
Cc: Yonghong Song <yhs@fb.com>
Cc: John Fastabend <john.fastabend@gmail.com>
Cc: Hao Luo <haoluo@google.com>
Cc: Steven Rostedt <rostedt@goodmis.org>
Cc: Masami Hiramatsu <mhiramat@kernel.org>
Cc: Alan Maguire <alan.maguire@oracle.com>
Link: https://lore.kernel.org/r/20250414083647.1234007-2-jolsa@kernel.org
Add TCP+sockmap-based benchmark.
Since sockmap's own update and delete operations are generally less
critical, the performance of the fast forwarding framework built upon
it is the key aspect.
Also with cgset/cgexec, we can observe the behavior of sockmap under
memory pressure.
The benchmark can be run with:
'''
./bench sockmap -c 2 -p 1 -a --rx-verdict-ingress
'''
In the future, we plan to move socket_helpers.h out of the prog_tests
directory to make it accessible for the benchmark. This will enable
better support for various socket types.
Signed-off-by: Jiayuan Chen <jiayuan.chen@linux.dev>
Link: https://lore.kernel.org/r/20250407142234.47591-5-jiayuan.chen@linux.dev
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Availability of the gettid definition across glibc versions supported by
BPF selftests is not certain. Currently, all users in the tree open-code
syscall to gettid. Convert them to a common macro definition.
Reviewed-by: Jiri Olsa <jolsa@kernel.org>
Signed-off-by: Kumar Kartikeya Dwivedi <memxor@gmail.com>
Link: https://lore.kernel.org/r/20241104171959.2938862-3-memxor@gmail.com
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Utilize bpf_modify_return_test_tp() kfunc to have a fast way to trigger
tp/raw_tp/fmodret programs from another BPF program, which gives us
comparable batched benchmarks to (batched) kprobe/fentry benchmarks.
We don't switch kprobe/fentry batched benchmarks to this kfunc to make
bench tool usable on older kernels as well.
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/r/20240326162151.3981687-7-andrii@kernel.org
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Instead of front-loading all possible benchmarking BPF programs for
trigger benchmarks, explicitly specify which BPF programs are used by
specific benchmark and load only it.
This allows to be more flexible in supporting older kernels, where some
program types might not be possible to load (e.g., those that rely on
newly added kfunc).
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/r/20240326162151.3981687-5-andrii@kernel.org
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Remove "legacy" benchmarks triggered by syscalls in favor of newly added
in-kernel/batched benchmarks. Drop -batched suffix now as well.
Next patch will restore "feature parity" by adding back
tp/raw_tp/fmodret benchmarks based on in-kernel kfunc approach.
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/r/20240326162151.3981687-4-andrii@kernel.org
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Existing kprobe/fentry triggering benchmarks have 1-to-1 mapping between
one syscall execution and BPF program run. While we use a fast
get_pgid() syscall, syscall overhead can still be non-trivial.
This patch adds kprobe/fentry set of benchmarks significantly amortizing
the cost of syscall vs actual BPF triggering overhead. We do this by
employing BPF_PROG_TEST_RUN command to trigger "driver" raw_tp program
which does a tight parameterized loop calling cheap BPF helper
(bpf_get_numa_node_id()), to which kprobe/fentry programs are
attached for benchmarking.
This way 1 bpf() syscall causes N executions of BPF program being
benchmarked. N defaults to 100, but can be adjusted with
--trig-batch-iters CLI argument.
For comparison we also implement a new baseline program that instead of
triggering another BPF program just does N atomic per-CPU counter
increments, establishing the limit for all other types of program within
this batched benchmarking setup.
Taking the final set of benchmarks added in this patch set (including
tp/raw_tp/fmodret, added in later patch), and keeping for now "legacy"
syscall-driven benchmarks, we can capture all triggering benchmarks in
one place for comparison, before we remove the legacy ones (and rename
xxx-batched into just xxx).
$ benchs/run_bench_trigger.sh
usermode-count : 79.500 ± 0.024M/s
kernel-count : 49.949 ± 0.081M/s
syscall-count : 9.009 ± 0.007M/s
fentry-batch : 31.002 ± 0.015M/s
fexit-batch : 20.372 ± 0.028M/s
fmodret-batch : 21.651 ± 0.659M/s
rawtp-batch : 36.775 ± 0.264M/s
tp-batch : 19.411 ± 0.248M/s
kprobe-batch : 12.949 ± 0.220M/s
kprobe-multi-batch : 15.400 ± 0.007M/s
kretprobe-batch : 5.559 ± 0.011M/s
kretprobe-multi-batch: 5.861 ± 0.003M/s
fentry-legacy : 8.329 ± 0.004M/s
fexit-legacy : 6.239 ± 0.003M/s
fmodret-legacy : 6.595 ± 0.001M/s
rawtp-legacy : 8.305 ± 0.004M/s
tp-legacy : 6.382 ± 0.001M/s
kprobe-legacy : 5.528 ± 0.003M/s
kprobe-multi-legacy : 5.864 ± 0.022M/s
kretprobe-legacy : 3.081 ± 0.001M/s
kretprobe-multi-legacy: 3.193 ± 0.001M/s
Note how xxx-batch variants are measured with significantly higher
throughput, even though it's exactly the same in-kernel overhead. As
such, results can be compared only between benchmarks of the same kind
(syscall vs batched):
fentry-legacy : 8.329 ± 0.004M/s
fentry-batch : 31.002 ± 0.015M/s
kprobe-multi-legacy : 5.864 ± 0.022M/s
kprobe-multi-batch : 15.400 ± 0.007M/s
Note also that syscall-count is setting a theoretical limit for
syscall-triggered benchmarks, while kernel-count is setting similar
limits for batch variants. usermode-count is a happy and unachievable
case of user space counting without doing any syscalls, and is mostly
the measure of CPU speed for such a trivial benchmark.
As was mentioned, tp/raw_tp/fmodret require kernel-side kfunc to produce
similar benchmark, which we address in a separate patch.
Note that run_bench_trigger.sh allows to override a list of benchmarks
to run, which is very useful for performance work.
Cc: Jiri Olsa <jolsa@kernel.org>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/r/20240326162151.3981687-3-andrii@kernel.org
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Rename uprobe-base to more precise usermode-count (it will match other
baseline-like benchmarks, kernel-count and syscall-count). Also use
BENCH_TRIG_USERMODE() macro to define all usermode-based triggering
benchmarks, which include usermode-count and uprobe/uretprobe benchmarks.
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/r/20240326162151.3981687-2-andrii@kernel.org
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Some distros seem to enable the -fcf-protection=branch by default,
which breaks our setup on first instruction of uprobe trigger
functions and place there endbr64 instruction.
Marking them with nocf_check attribute to skip that.
Ignoring unknown attribute warning in gcc for bench objects, because
nocf_check can be used only when -fcf-protection=branch is enabled,
otherwise we get a warning and break compilation.
Signed-off-by: Jiri Olsa <jolsa@kernel.org>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20240322134936.1075395-1-jolsa@kernel.org
With glibc 2.28, selftests compilation fails for benchs/bench_trigger.c:
benchs/bench_trigger.c: In function ‘inc_counter’:
benchs/bench_trigger.c:25:23: error: implicit declaration of function ‘gettid’; did you mean ‘getgid’? [-Werror=implicit-function-declaration]
25 | tid = gettid();
| ^~~~~~
| getgid
cc1: all warnings being treated as errors
It appears support for the gettid() wrapper is variable across glibc
versions, so may be safer to use syscall(SYS_gettid) instead.
Fixes: 520fad2e32 ("selftests/bpf: scale benchmark counting by using per-CPU counters")
Signed-off-by: Alan Maguire <alan.maguire@oracle.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20240322095728.95671-1-alan.maguire@oracle.com
When benchmarking with multiple threads (-pN, where N>1), we start
contending on single atomic counter that both BPF trigger benchmarks are
using, as well as "baseline" tests in user space (trig-base and
trig-uprobe-base benchmarks). As such, we start bottlenecking on
something completely irrelevant to benchmark at hand.
Scale counting up by using per-CPU counters on BPF side. On use space
side we do the next best thing: hash thread ID to approximate per-CPU
behavior. It seems to work quite well in practice.
To demonstrate the difference, I ran three benchmarks with 1, 2, 4, 8,
16, and 32 threads:
- trig-uprobe-base (no syscalls, pure tight counting loop in user-space);
- trig-base (get_pgid() syscall, atomic counter in user-space);
- trig-fentry (syscall to trigger fentry program, atomic uncontended per-CPU
counter on BPF side).
Command used:
for b in uprobe-base base fentry; do \
for p in 1 2 4 8 16 32; do \
printf "%-11s %2d: %s\n" $b $p \
"$(sudo ./bench -w2 -d5 -a -p$p trig-$b | tail -n1 | cut -d'(' -f1 | cut -d' ' -f3-)"; \
done; \
done
Before these changes, aggregate throughput across all threads doesn't
scale well with number of threads, it actually even falls sharply for
uprobe-base due to a very high contention:
uprobe-base 1: 138.998 ± 0.650M/s
uprobe-base 2: 70.526 ± 1.147M/s
uprobe-base 4: 63.114 ± 0.302M/s
uprobe-base 8: 54.177 ± 0.138M/s
uprobe-base 16: 45.439 ± 0.057M/s
uprobe-base 32: 37.163 ± 0.242M/s
base 1: 16.940 ± 0.182M/s
base 2: 19.231 ± 0.105M/s
base 4: 21.479 ± 0.038M/s
base 8: 23.030 ± 0.037M/s
base 16: 22.034 ± 0.004M/s
base 32: 18.152 ± 0.013M/s
fentry 1: 14.794 ± 0.054M/s
fentry 2: 17.341 ± 0.055M/s
fentry 4: 23.792 ± 0.024M/s
fentry 8: 21.557 ± 0.047M/s
fentry 16: 21.121 ± 0.004M/s
fentry 32: 17.067 ± 0.023M/s
After these changes, we see almost perfect linear scaling, as expected.
The sub-linear scaling when going from 8 to 16 threads is interesting
and consistent on my test machine, but I haven't investigated what is
causing it this peculiar slowdown (across all benchmarks, could be due
to hyperthreading effects, not sure).
uprobe-base 1: 139.980 ± 0.648M/s
uprobe-base 2: 270.244 ± 0.379M/s
uprobe-base 4: 532.044 ± 1.519M/s
uprobe-base 8: 1004.571 ± 3.174M/s
uprobe-base 16: 1720.098 ± 0.744M/s
uprobe-base 32: 3506.659 ± 8.549M/s
base 1: 16.869 ± 0.071M/s
base 2: 33.007 ± 0.092M/s
base 4: 64.670 ± 0.203M/s
base 8: 121.969 ± 0.210M/s
base 16: 207.832 ± 0.112M/s
base 32: 424.227 ± 1.477M/s
fentry 1: 14.777 ± 0.087M/s
fentry 2: 28.575 ± 0.146M/s
fentry 4: 56.234 ± 0.176M/s
fentry 8: 106.095 ± 0.385M/s
fentry 16: 181.440 ± 0.032M/s
fentry 32: 369.131 ± 0.693M/s
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Message-ID: <20240315213329.1161589-1-andrii@kernel.org>
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Adding kprobe multi triggering benchmarks. It's useful now to bench
new fprobe implementation and might be useful later as well.
Signed-off-by: Jiri Olsa <jolsa@kernel.org>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20240311211023.590321-1-jolsa@kernel.org
We already have kprobe and fentry benchmarks. Let's add kretprobe and
fexit ones for completeness.
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Signed-off-by: Daniel Borkmann <daniel@iogearbox.net>
Acked-by: Jiri Olsa <jolsa@kernel.org>
Link: https://lore.kernel.org/bpf/20240309005124.3004446-1-andrii@kernel.org
Settle on three "flavors" of uprobe/uretprobe, installed on different
kinds of instruction: nop, push, and ret. All three are testing
different internal code paths emulating or single-stepping instructions,
so are interesting to compare and benchmark separately.
To ensure `push rbp` instruction we ensure that uprobe_target_push() is
not a leaf function by calling (global __weak) noop function and
returning something afterwards (if we don't do that, compiler will just
do a tail call optimization).
Also, we need to make sure that compiler isn't skipping frame pointer
generation, so let's add `-fno-omit-frame-pointers` to Makefile.
Just to give an idea of where we currently stand in terms of relative
performance of different uprobe/uretprobe cases vs a cheap syscall
(getpgid()) baseline, here are results from my local machine:
$ benchs/run_bench_uprobes.sh
base : 1.561 ± 0.020M/s
uprobe-nop : 0.947 ± 0.007M/s
uprobe-push : 0.951 ± 0.004M/s
uprobe-ret : 0.443 ± 0.007M/s
uretprobe-nop : 0.471 ± 0.013M/s
uretprobe-push : 0.483 ± 0.004M/s
uretprobe-ret : 0.306 ± 0.007M/s
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Signed-off-by: Daniel Borkmann <daniel@iogearbox.net>
Link: https://lore.kernel.org/bpf/20240301214551.1686095-1-andrii@kernel.org
There is error log when htab-mem benchmark completes. The error log
looks as follows:
$ ./bench htab-mem -d1
Setting up benchmark 'htab-mem'...
Benchmark 'htab-mem' started.
......
(cgroup_helpers.c:353: errno: Device or resource busy) umount cgroup2
Fix it by closing cgrp fd before invoking cleanup_cgroup_environment().
Signed-off-by: Hou Tao <houtao1@huawei.com>
Link: https://lore.kernel.org/r/20231219135727.2661527-1-houtao@huaweicloud.com
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Running the bench_rename test script, the following error occurs:
# ./benchs/run_bench_rename.sh
base : 0.819 ± 0.012M/s
kprobe : 0.538 ± 0.009M/s
kretprobe : 0.503 ± 0.004M/s
rawtp : 0.779 ± 0.020M/s
fentry : 0.726 ± 0.007M/s
fexit : 0.691 ± 0.007M/s
benchmark 'rename-fmodret' not found
The bench_rename_fmodret has been removed in commit b000def2e0
("selftests: Remove fmod_ret from test_overhead"), thus remove it
from the runners in the test script.
Fixes: b000def2e0 ("selftests: Remove fmod_ret from test_overhead")
Signed-off-by: Yipeng Zou <zouyipeng@huawei.com>
Signed-off-by: Daniel Borkmann <daniel@iogearbox.net>
Link: https://lore.kernel.org/bpf/20230814030727.3010390-1-zouyipeng@huawei.com
When wrapping code, use ';' better than using ',' which is more in line with
the coding habits of most engineers.
Signed-off-by: Lu Hongfei <luhongfei@vivo.com>
Signed-off-by: Daniel Borkmann <daniel@iogearbox.net>
Acked-by: Hou Tao <houtao1@huawei.com>
Acked-by: Stanislav Fomichev <sdf@google.com>
Link: https://lore.kernel.org/bpf/20230707081253.34638-1-luhongfei@vivo.com
The benchmark could be used to compare the performance of hash map
operations and the memory usage between different flavors of bpf memory
allocator (e.g., no bpf ma vs bpf ma vs reuse-after-gp bpf ma). It also
could be used to check the performance improvement or the memory saving
provided by optimization.
The benchmark creates a non-preallocated hash map which uses bpf memory
allocator and shows the operation performance and the memory usage of
the hash map under different use cases:
(1) overwrite
Each CPU overwrites nonoverlapping part of hash map. When each CPU
completes overwriting of 64 elements in hash map, it increases the
op_count.
(2) batch_add_batch_del
Each CPU adds then deletes nonoverlapping part of hash map in batch.
When each CPU adds and deletes 64 elements in hash map, it increases
the op_count twice.
(3) add_del_on_diff_cpu
Each two-CPUs pair adds and deletes nonoverlapping part of map
cooperatively. When each CPU adds or deletes 64 elements in hash map,
it will increase the op_count.
The following is the benchmark results when comparing between different
flavors of bpf memory allocator. These tests are conducted on a KVM guest
with 8 CPUs and 16 GB memory. The command line below is used to do all
the following benchmarks:
./bench htab-mem --use-case $name ${OPTS} -w3 -d10 -a -p8
These results show that preallocated hash map has both better performance
and smaller memory footprint.
(1) non-preallocated + no bpf memory allocator (v6.0.19)
use kmalloc() + call_rcu
overwrite per-prod-op: 11.24 ± 0.07k/s, avg mem: 82.64 ± 26.32MiB, peak mem: 119.18MiB
batch_add_batch_del per-prod-op: 18.45 ± 0.10k/s, avg mem: 50.47 ± 14.51MiB, peak mem: 94.96MiB
add_del_on_diff_cpu per-prod-op: 14.50 ± 0.03k/s, avg mem: 4.64 ± 0.73MiB, peak mem: 7.20MiB
(2) preallocated
OPTS=--preallocated
overwrite per-prod-op: 191.42 ± 0.09k/s, avg mem: 1.24 ± 0.00MiB, peak mem: 1.49MiB
batch_add_batch_del per-prod-op: 221.83 ± 0.17k/s, avg mem: 1.23 ± 0.00MiB, peak mem: 1.49MiB
add_del_on_diff_cpu per-prod-op: 39.66 ± 0.31k/s, avg mem: 1.47 ± 0.13MiB, peak mem: 1.75MiB
(3) normal bpf memory allocator
overwrite per-prod-op: 126.59 ± 0.02k/s, avg mem: 2.26 ± 0.00MiB, peak mem: 2.74MiB
batch_add_batch_del per-prod-op: 83.37 ± 0.20k/s, avg mem: 2.14 ± 0.17MiB, peak mem: 2.74MiB
add_del_on_diff_cpu per-prod-op: 21.25 ± 0.24k/s, avg mem: 17.50 ± 3.32MiB, peak mem: 28.87MiB
Acked-by: John Fastabend <john.fastabend@gmail.com>
Signed-off-by: Hou Tao <houtao1@huawei.com>
Link: https://lore.kernel.org/r/20230704025039.938914-1-houtao@huaweicloud.com
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Considering that only bench_ringbufs.c supports consumer, just set the
default value of consumer_cnt as 0. After that, update the validity
check of consumer_cnt, remove unused consumer_thread code snippets and
set consumer_cnt as 1 in run_bench_ringbufs.sh accordingly.
Signed-off-by: Hou Tao <houtao1@huawei.com>
Link: https://lore.kernel.org/r/20230613080921.1623219-5-houtao@huaweicloud.com
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
The fork function in gcc is considered a built in function due to
being used by libgcov when building with gnu extensions.
Rename fork to sched_process_fork to prevent this conflict.
See details:
d1c3882392https://gcc.gnu.org/bugzilla/show_bug.cgi?id=82457
Fixes the following error:
In file included from progs/bench_local_storage_create.c:6:
progs/bench_local_storage_create.c:43:14: error: conflicting types for
built-in function 'fork'; expected 'int(void)'
[-Werror=builtin-declaration-mismatch]
43 | int BPF_PROG(fork, struct task_struct *parent, struct
task_struct *child)
| ^~~~
Fixes: cbe9d93d58 ("selftests/bpf: Add bench for task storage creation")
Signed-off-by: James Hilliard <james.hilliard1@gmail.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20230331075848.1642814-1-james.hilliard1@gmail.com
This patch tests how many kmallocs is needed to create and free
a batch of UDP sockets and each socket has a 64bytes bpf storage.
It also measures how fast the UDP sockets can be created.
The result is from my qemu setup.
Before bpf_mem_cache_alloc/free:
./bench -p 1 local-storage-create
Setting up benchmark 'local-storage-create'...
Benchmark 'local-storage-create' started.
Iter 0 ( 73.193us): creates 213.552k/s (213.552k/prod), 3.09 kmallocs/create
Iter 1 (-20.724us): creates 211.908k/s (211.908k/prod), 3.09 kmallocs/create
Iter 2 ( 9.280us): creates 212.574k/s (212.574k/prod), 3.12 kmallocs/create
Iter 3 ( 11.039us): creates 213.209k/s (213.209k/prod), 3.12 kmallocs/create
Iter 4 (-11.411us): creates 213.351k/s (213.351k/prod), 3.12 kmallocs/create
Iter 5 ( -7.915us): creates 214.754k/s (214.754k/prod), 3.12 kmallocs/create
Iter 6 ( 11.317us): creates 210.942k/s (210.942k/prod), 3.12 kmallocs/create
Summary: creates 212.789 ± 1.310k/s (212.789k/prod), 3.12 kmallocs/create
After bpf_mem_cache_alloc/free:
./bench -p 1 local-storage-create
Setting up benchmark 'local-storage-create'...
Benchmark 'local-storage-create' started.
Iter 0 ( 68.265us): creates 243.984k/s (243.984k/prod), 1.04 kmallocs/create
Iter 1 ( 30.357us): creates 238.424k/s (238.424k/prod), 1.04 kmallocs/create
Iter 2 (-18.712us): creates 232.963k/s (232.963k/prod), 1.04 kmallocs/create
Iter 3 (-15.885us): creates 238.879k/s (238.879k/prod), 1.04 kmallocs/create
Iter 4 ( 5.590us): creates 237.490k/s (237.490k/prod), 1.04 kmallocs/create
Iter 5 ( 8.577us): creates 237.521k/s (237.521k/prod), 1.04 kmallocs/create
Iter 6 ( -6.263us): creates 238.508k/s (238.508k/prod), 1.04 kmallocs/create
Summary: creates 237.298 ± 2.198k/s (237.298k/prod), 1.04 kmallocs/create
Signed-off-by: Martin KaFai Lau <martin.lau@kernel.org>
Link: https://lore.kernel.org/r/20230308065936.1550103-18-martin.lau@linux.dev
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Add a new benchmark which measures hashmap lookup operations speed. A user can
control the following parameters of the benchmark:
* key_size (max 1024): the key size to use
* max_entries: the hashmap max entries
* nr_entries: the number of entries to insert/lookup
* nr_loops: the number of loops for the benchmark
* map_flags The hashmap flags passed to BPF_MAP_CREATE
The BPF program performing the benchmarks calls two nested bpf_loop:
bpf_loop(nr_loops/nr_entries)
bpf_loop(nr_entries)
bpf_map_lookup()
So the nr_loops determines the number of actual map lookups. All lookups are
successful.
Example (the output is generated on a AMD Ryzen 9 3950X machine):
for nr_entries in `seq 4096 4096 65536`; do echo -n "$((nr_entries*100/65536))% full: "; sudo ./bench -d2 -a bpf-hashmap-lookup --key_size=4 --nr_entries=$nr_entries --max_entries=65536 --nr_loops=1000000 --map_flags=0x40 | grep cpu; done
6% full: cpu01: lookup 50.739M ± 0.018M events/sec (approximated from 32 samples of ~19ms)
12% full: cpu01: lookup 47.751M ± 0.015M events/sec (approximated from 32 samples of ~20ms)
18% full: cpu01: lookup 45.153M ± 0.013M events/sec (approximated from 32 samples of ~22ms)
25% full: cpu01: lookup 43.826M ± 0.014M events/sec (approximated from 32 samples of ~22ms)
31% full: cpu01: lookup 41.971M ± 0.012M events/sec (approximated from 32 samples of ~23ms)
37% full: cpu01: lookup 41.034M ± 0.015M events/sec (approximated from 32 samples of ~24ms)
43% full: cpu01: lookup 39.946M ± 0.012M events/sec (approximated from 32 samples of ~25ms)
50% full: cpu01: lookup 38.256M ± 0.014M events/sec (approximated from 32 samples of ~26ms)
56% full: cpu01: lookup 36.580M ± 0.018M events/sec (approximated from 32 samples of ~27ms)
62% full: cpu01: lookup 36.252M ± 0.012M events/sec (approximated from 32 samples of ~27ms)
68% full: cpu01: lookup 35.200M ± 0.012M events/sec (approximated from 32 samples of ~28ms)
75% full: cpu01: lookup 34.061M ± 0.009M events/sec (approximated from 32 samples of ~29ms)
81% full: cpu01: lookup 34.374M ± 0.010M events/sec (approximated from 32 samples of ~29ms)
87% full: cpu01: lookup 33.244M ± 0.011M events/sec (approximated from 32 samples of ~30ms)
93% full: cpu01: lookup 32.182M ± 0.013M events/sec (approximated from 32 samples of ~31ms)
100% full: cpu01: lookup 31.497M ± 0.016M events/sec (approximated from 32 samples of ~31ms)
Signed-off-by: Anton Protopopov <aspsk@isovalent.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20230213091519.1202813-8-aspsk@isovalent.com
The "local-storage-tasks-trace" benchmark has a `--quiet` option. Move it to
the list of common options, so that the main code and other benchmarks can use
(new) env.quiet variable. Patch the run_bench_local_storage_rcu_tasks_trace.sh
helper script accordingly.
Signed-off-by: Anton Protopopov <aspsk@isovalent.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20230213091519.1202813-6-aspsk@isovalent.com
The benchs/bench_bpf_hashmap_full_update.c doesn't set a custom argp,
so it shouldn't include the <argp.h> header.
Signed-off-by: Anton Protopopov <aspsk@isovalent.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20230213091519.1202813-5-aspsk@isovalent.com
To parse command line the bench utility uses the argp_parse() function. This
function takes as an argument a parent 'struct argp' structure which defines
common command line options and an array of children 'struct argp' structures
which defines additional command line options for particular benchmarks. This
implementation doesn't allow benchmarks to share option names, e.g., if two
benchmarks want to use, say, the --option option, then only one of them will
succeed (the first one encountered in the array). This will be convenient if
same option names could be used in different benchmarks (with the same
semantics, e.g., --nr_loops=N).
Fix this by calling the argp_parse() function twice. The first call is the same
as it was before, with all children argps, and helps to find the benchmark name
and to print a combined help message if anything is wrong. Given the name, we
can call the argp_parse the second time, but now the children array points only
to a correct benchmark thus always calling the correct parsers. (If there's no
a specific list of arguments, then only one call to argp_parse will be done.)
Signed-off-by: Anton Protopopov <aspsk@isovalent.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20230213091519.1202813-4-aspsk@isovalent.com
The hashmap_report_final callback function defined in the
benchs/bench_bpf_hashmap_full_update.c file should be static.
Signed-off-by: Anton Protopopov <aspsk@isovalent.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20230213091519.1202813-3-aspsk@isovalent.com
To call the bpf_hashmap_full_update benchmark, one should say:
bench bpf-hashmap-ful-update
The patch adds a missing 'l' to the benchmark name.
Signed-off-by: Anton Protopopov <aspsk@isovalent.com>
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/bpf/20230213091519.1202813-2-aspsk@isovalent.com
This benchmark measures grace period latency and kthread cpu usage of
RCU Tasks Trace when many processes are creating/deleting BPF
local_storage. Intent here is to quantify improvement on these metrics
after Paul's recent RCU Tasks patches [0].
Specifically, fork 15k tasks which call a bpf prog that creates/destroys
task local_storage and sleep in a loop, resulting in many
call_rcu_tasks_trace calls.
To determine grace period latency, trace time elapsed between
rcu_tasks_trace_pregp_step and rcu_tasks_trace_postgp; for cpu usage
look at rcu_task_trace_kthread's stime in /proc/PID/stat.
On my virtualized test environment (Skylake, 8 cpus) benchmark results
demonstrate significant improvement:
BEFORE Paul's patches:
SUMMARY tasks_trace grace period latency avg 22298.551 us stddev 1302.165 us
SUMMARY ticks per tasks_trace grace period avg 2.291 stddev 0.324
AFTER Paul's patches:
SUMMARY tasks_trace grace period latency avg 16969.197 us stddev 2525.053 us
SUMMARY ticks per tasks_trace grace period avg 1.146 stddev 0.178
Note that since these patches are not in bpf-next benchmarking was done
by cherry-picking this patch onto rcu tree.
[0] https://lore.kernel.org/rcu/20220620225402.GA3842369@paulmck-ThinkPad-P17-Gen-1/
Signed-off-by: Dave Marchevsky <davemarchevsky@fb.com>
Signed-off-by: Daniel Borkmann <daniel@iogearbox.net>
Acked-by: Paul E. McKenney <paulmck@kernel.org>
Acked-by: Martin KaFai Lau <kafai@fb.com>
Link: https://lore.kernel.org/bpf/20220705190018.3239050-1-davemarchevsky@fb.com
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>
Add benchmark for hash_map to reproduce the worst case
that non-stop update when map's free is zero.
Just like this:
./run_bench_bpf_hashmap_full_update.sh
Setting up benchmark 'bpf-hashmap-ful-update'...
Benchmark 'bpf-hashmap-ful-update' started.
1:hash_map_full_perf 555830 events per sec
...
Signed-off-by: Feng Zhou <zhoufeng.zf@bytedance.com>
Link: https://lore.kernel.org/r/20220610023308.93798-3-zhoufeng.zf@bytedance.com
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Fix how selftests determine relative offset of a function that is
uprobed. Previously, there was an assumption that uprobed function is
always in the first executable region, which is not always the case
(libbpf CI hits this case now). So get_base_addr() approach in isolation
doesn't work anymore. So teach get_uprobe_offset() to determine correct
memory mapping and calculate uprobe offset correctly.
While at it, I merged together two implementations of
get_uprobe_offset() helper, moving powerpc64-specific logic inside (had
to add extra {} block to avoid unused variable error for insn).
Also ensured that uprobed functions are never inlined, but are still
static (and thus local to each selftest), by using a no-op asm volatile
block internally. I didn't want to keep them global __weak, because some
tests use uprobe's ref counter offset (to test USDT-like logic) which is
not compatible with non-refcounted uprobe. So it's nicer to have each
test uprobe target local to the file and guaranteed to not be inlined or
skipped by the compiler (which can happen with static functions,
especially if compiling selftests with -O2).
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Link: https://lore.kernel.org/r/20220126193058.3390292-1-andrii@kernel.org
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
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
Add benchmark to measure overhead of uprobes and uretprobes. Also have
a baseline (no uprobe attached) benchmark.
On my dev machine, baseline benchmark can trigger 130M user_target()
invocations. When uprobe is attached, this falls to just 700K. With
uretprobe, we get down to 520K:
$ sudo ./bench trig-uprobe-base -a
Summary: hits 131.289 ± 2.872M/s
# UPROBE
$ sudo ./bench -a trig-uprobe-without-nop
Summary: hits 0.729 ± 0.007M/s
$ sudo ./bench -a trig-uprobe-with-nop
Summary: hits 1.798 ± 0.017M/s
# URETPROBE
$ sudo ./bench -a trig-uretprobe-without-nop
Summary: hits 0.508 ± 0.012M/s
$ sudo ./bench -a trig-uretprobe-with-nop
Summary: hits 0.883 ± 0.008M/s
So there is almost 2.5x performance difference between probing nop vs
non-nop instruction for entry uprobe. And 1.7x difference for uretprobe.
This means that non-nop uprobe overhead is around 1.4 microseconds for uprobe
and 2 microseconds for non-nop uretprobe.
For nop variants, uprobe and uretprobe overhead is down to 0.556 and
1.13 microseconds, respectively.
For comparison, just doing a very low-overhead syscall (with no BPF
programs attached anywhere) gives:
$ sudo ./bench trig-base -a
Summary: hits 4.830 ± 0.036M/s
So uprobes are about 2.67x slower than pure context switch.
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Signed-off-by: Daniel Borkmann <daniel@iogearbox.net>
Link: https://lore.kernel.org/bpf/20211116013041.4072571-1-andrii@kernel.org
When using clang to build selftests with LLVM=1 in make commandline,
I hit the following compiler warning:
benchs/bench_bloom_filter_map.c:84:46: warning: result of comparison of constant 256
with expression of type '__u8' (aka 'unsigned char') is always false
[-Wtautological-constant-out-of-range-compare]
if (args.value_size < 2 || args.value_size > 256) {
~~~~~~~~~~~~~~~ ^ ~~~
The reason is arg.vaue_size has type __u8, so comparison "args.value_size > 256"
is always false.
This patch fixed the issue by doing proper comparison before assigning the
value to args.value_size. The patch also fixed the same issue in two
other places.
Signed-off-by: Yonghong Song <yhs@fb.com>
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Link: https://lore.kernel.org/bpf/20211112204838.3579953-1-yhs@fb.com
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
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
Turn ony libbpf 1.0 mode. Fix all the explicit IS_ERR checks that now will be
broken because libbpf returns NULL on error (and sets errno). Fix
ASSERT_OK_PTR and ASSERT_ERR_PTR to work for both old mode and new modes and
use them throughout selftests. This is trivial to do by using
libbpf_get_error() API that all libbpf users are supposed to use, instead of
IS_ERR checks.
A bunch of checks also did explicit -1 comparison for various fd-returning
APIs. Such checks are replaced with >= 0 or < 0 cases.
There were also few misuses of bpf_object__find_map_by_name() in test_maps.
Those are fixed in this patch as well.
Signed-off-by: Andrii Nakryiko <andrii@kernel.org>
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
Acked-by: John Fastabend <john.fastabend@gmail.com>
Acked-by: Toke Høiland-Jørgensen <toke@redhat.com>
Link: https://lore.kernel.org/bpf/20210525035935.1461796-3-andrii@kernel.org
