forked from luck/tmp_suning_uos_patched
c97099b0f2
Extend bench framework with ability to have benchmark-provided child argument parser for custom benchmark-specific parameters. This makes bench generic code modular and independent from any specific benchmark. Also implement a set of benchmarks for new BPF ring buffer and existing perf buffer. 4 benchmarks were implemented: 2 variations for each of BPF ringbuf and perfbuf:, - rb-libbpf utilizes stock libbpf ring_buffer manager for reading data; - rb-custom implements custom ring buffer setup and reading code, to eliminate overheads inherent in generic libbpf code due to callback functions and the need to update consumer position after each consumed record, instead of batching updates (due to pessimistic assumption that user callback might take long time and thus could unnecessarily hold ring buffer space for too long); - pb-libbpf uses stock libbpf perf_buffer code with all the default settings, though uses higher-performance raw event callback to minimize unnecessary overhead; - pb-custom implements its own custom consumer code to minimize any possible overhead of generic libbpf implementation and indirect function calls. All of the test support default, no data notification skipped, mode, as well as sampled mode (with --rb-sampled flag), which allows to trigger epoll notification less frequently and reduce overhead. As will be shown, this mode is especially critical for perf buffer, which suffers from high overhead of wakeups in kernel. Otherwise, all benchamrks implement similar way to generate a batch of records by using fentry/sys_getpgid BPF program, which pushes a bunch of records in a tight loop and records number of successful and dropped samples. Each record is a small 8-byte integer, to minimize the effect of memory copying with bpf_perf_event_output() and bpf_ringbuf_output(). Benchmarks that have only one producer implement optional back-to-back mode, in which record production and consumption is alternating on the same CPU. This is the highest-throughput happy case, showing ultimate performance achievable with either BPF ringbuf or perfbuf. All the below scenarios are implemented in a script in benchs/run_bench_ringbufs.sh. Tests were performed on 28-core/56-thread Intel Xeon CPU E5-2680 v4 @ 2.40GHz CPU. Single-producer, parallel producer ================================== rb-libbpf 12.054 ± 0.320M/s (drops 0.000 ± 0.000M/s) rb-custom 8.158 ± 0.118M/s (drops 0.001 ± 0.003M/s) pb-libbpf 0.931 ± 0.007M/s (drops 0.000 ± 0.000M/s) pb-custom 0.965 ± 0.003M/s (drops 0.000 ± 0.000M/s) Single-producer, parallel producer, sampled notification ======================================================== rb-libbpf 11.563 ± 0.067M/s (drops 0.000 ± 0.000M/s) rb-custom 15.895 ± 0.076M/s (drops 0.000 ± 0.000M/s) pb-libbpf 9.889 ± 0.032M/s (drops 0.000 ± 0.000M/s) pb-custom 9.866 ± 0.028M/s (drops 0.000 ± 0.000M/s) Single producer on one CPU, consumer on another one, both running at full speed. Curiously, rb-libbpf has higher throughput than objectively faster (due to more lightweight consumer code path) rb-custom. It appears that faster consumer causes kernel to send notifications more frequently, because consumer appears to be caught up more frequently. Performance of perfbuf suffers from default "no sampling" policy and huge overhead that causes. In sampled mode, rb-custom is winning very significantly eliminating too frequent in-kernel wakeups, the gain appears to be more than 2x. Perf buffer achieves even more impressive wins, compared to stock perfbuf settings, with 10x improvements in throughput with 1:500 sampling rate. The trade-off is that with sampling, application might not get next X events until X+1st arrives, which is not always acceptable. With steady influx of events, though, this shouldn't be a problem. Overall, single-producer performance of ring buffers seems to be better no matter the sampled/non-sampled modes, but it especially beats ring buffer without sampling due to its adaptive notification approach. Single-producer, back-to-back mode ================================== rb-libbpf 15.507 ± 0.247M/s (drops 0.000 ± 0.000M/s) rb-libbpf-sampled 14.692 ± 0.195M/s (drops 0.000 ± 0.000M/s) rb-custom 21.449 ± 0.157M/s (drops 0.000 ± 0.000M/s) rb-custom-sampled 20.024 ± 0.386M/s (drops 0.000 ± 0.000M/s) pb-libbpf 1.601 ± 0.015M/s (drops 0.000 ± 0.000M/s) pb-libbpf-sampled 8.545 ± 0.064M/s (drops 0.000 ± 0.000M/s) pb-custom 1.607 ± 0.022M/s (drops 0.000 ± 0.000M/s) pb-custom-sampled 8.988 ± 0.144M/s (drops 0.000 ± 0.000M/s) Here we test a back-to-back mode, which is arguably best-case scenario both for BPF ringbuf and perfbuf, because there is no contention and for ringbuf also no excessive notification, because consumer appears to be behind after the first record. For ringbuf, custom consumer code clearly wins with 21.5 vs 16 million records per second exchanged between producer and consumer. Sampled mode actually hurts a bit due to slightly slower producer logic (it needs to fetch amount of data available to decide whether to skip or force notification). Perfbuf with wakeup sampling gets 5.5x throughput increase, compared to no-sampling version. There also doesn't seem to be noticeable overhead from generic libbpf handling code. Perfbuf back-to-back, effect of sample rate =========================================== pb-sampled-1 1.035 ± 0.012M/s (drops 0.000 ± 0.000M/s) pb-sampled-5 3.476 ± 0.087M/s (drops 0.000 ± 0.000M/s) pb-sampled-10 5.094 ± 0.136M/s (drops 0.000 ± 0.000M/s) pb-sampled-25 7.118 ± 0.153M/s (drops 0.000 ± 0.000M/s) pb-sampled-50 8.169 ± 0.156M/s (drops 0.000 ± 0.000M/s) pb-sampled-100 8.887 ± 0.136M/s (drops 0.000 ± 0.000M/s) pb-sampled-250 9.180 ± 0.209M/s (drops 0.000 ± 0.000M/s) pb-sampled-500 9.353 ± 0.281M/s (drops 0.000 ± 0.000M/s) pb-sampled-1000 9.411 ± 0.217M/s (drops 0.000 ± 0.000M/s) pb-sampled-2000 9.464 ± 0.167M/s (drops 0.000 ± 0.000M/s) pb-sampled-3000 9.575 ± 0.273M/s (drops 0.000 ± 0.000M/s) This benchmark shows the effect of event sampling for perfbuf. Back-to-back mode for highest throughput. Just doing every 5th record notification gives 3.5x speed up. 250-500 appears to be the point of diminishing return, with almost 9x speed up. Most benchmarks use 500 as the default sampling for pb-raw and pb-custom. Ringbuf back-to-back, effect of sample rate =========================================== rb-sampled-1 1.106 ± 0.010M/s (drops 0.000 ± 0.000M/s) rb-sampled-5 4.746 ± 0.149M/s (drops 0.000 ± 0.000M/s) rb-sampled-10 7.706 ± 0.164M/s (drops 0.000 ± 0.000M/s) rb-sampled-25 12.893 ± 0.273M/s (drops 0.000 ± 0.000M/s) rb-sampled-50 15.961 ± 0.361M/s (drops 0.000 ± 0.000M/s) rb-sampled-100 18.203 ± 0.445M/s (drops 0.000 ± 0.000M/s) rb-sampled-250 19.962 ± 0.786M/s (drops 0.000 ± 0.000M/s) rb-sampled-500 20.881 ± 0.551M/s (drops 0.000 ± 0.000M/s) rb-sampled-1000 21.317 ± 0.532M/s (drops 0.000 ± 0.000M/s) rb-sampled-2000 21.331 ± 0.535M/s (drops 0.000 ± 0.000M/s) rb-sampled-3000 21.688 ± 0.392M/s (drops 0.000 ± 0.000M/s) Similar benchmark for ring buffer also shows a great advantage (in terms of throughput) of skipping notifications. Skipping every 5th one gives 4x boost. Also similar to perfbuf case, 250-500 seems to be the point of diminishing returns, giving roughly 20x better results. Keep in mind, for this test, notifications are controlled manually with BPF_RB_NO_WAKEUP and BPF_RB_FORCE_WAKEUP. As can be seen from previous benchmarks, adaptive notifications based on consumer's positions provides same (or even slightly better due to simpler load generator on BPF side) benefits in favorable back-to-back scenario. Over zealous and fast consumer, which is almost always caught up, will make thoughput numbers smaller. That's the case when manual notification control might prove to be extremely beneficial. Ringbuf back-to-back, reserve+commit vs output ============================================== reserve 22.819 ± 0.503M/s (drops 0.000 ± 0.000M/s) output 18.906 ± 0.433M/s (drops 0.000 ± 0.000M/s) Ringbuf sampled, reserve+commit vs output ========================================= reserve-sampled 15.350 ± 0.132M/s (drops 0.000 ± 0.000M/s) output-sampled 14.195 ± 0.144M/s (drops 0.000 ± 0.000M/s) BPF ringbuf supports two sets of APIs with various usability and performance tradeoffs: bpf_ringbuf_reserve()+bpf_ringbuf_commit() vs bpf_ringbuf_output(). This benchmark clearly shows superiority of reserve+commit approach, despite using a small 8-byte record size. Single-producer, consumer/producer competing on the same CPU, low batch count ============================================================================= rb-libbpf 3.045 ± 0.020M/s (drops 3.536 ± 0.148M/s) rb-custom 3.055 ± 0.022M/s (drops 3.893 ± 0.066M/s) pb-libbpf 1.393 ± 0.024M/s (drops 0.000 ± 0.000M/s) pb-custom 1.407 ± 0.016M/s (drops 0.000 ± 0.000M/s) This benchmark shows one of the worst-case scenarios, in which producer and consumer do not coordinate *and* fight for the same CPU. No batch count and sampling settings were able to eliminate drops for ringbuffer, producer is just too fast for consumer to keep up. But ringbuf and perfbuf still able to pass through quite a lot of messages, which is more than enough for a lot of applications. Ringbuf, multi-producer contention ================================== rb-libbpf nr_prod 1 10.916 ± 0.399M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 2 4.931 ± 0.030M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 3 4.880 ± 0.006M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 4 3.926 ± 0.004M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 8 4.011 ± 0.004M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 12 3.967 ± 0.016M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 16 2.604 ± 0.030M/s (drops 0.001 ± 0.002M/s) rb-libbpf nr_prod 20 2.233 ± 0.003M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 24 2.085 ± 0.015M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 28 2.055 ± 0.004M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 32 1.962 ± 0.004M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 36 2.089 ± 0.005M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 40 2.118 ± 0.006M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 44 2.105 ± 0.004M/s (drops 0.000 ± 0.000M/s) rb-libbpf nr_prod 48 2.120 ± 0.058M/s (drops 0.000 ± 0.001M/s) rb-libbpf nr_prod 52 2.074 ± 0.024M/s (drops 0.007 ± 0.014M/s) Ringbuf uses a very short-duration spinlock during reservation phase, to check few invariants, increment producer count and set record header. This is the biggest point of contention for ringbuf implementation. This benchmark evaluates the effect of multiple competing writers on overall throughput of a single shared ringbuffer. Overall throughput drops almost 2x when going from single to two highly-contended producers, gradually dropping with additional competing producers. Performance drop stabilizes at around 20 producers and hovers around 2mln even with 50+ fighting producers, which is a 5x drop compared to non-contended case. Good kernel implementation in kernel helps maintain decent performance here. Note, that in the intended real-world scenarios, it's not expected to get even close to such a high levels of contention. But if contention will become a problem, there is always an option of sharding few ring buffers across a set of CPUs. Signed-off-by: Andrii Nakryiko <andriin@fb.com> Signed-off-by: Daniel Borkmann <daniel@iogearbox.net> Link: https://lore.kernel.org/bpf/20200529075424.3139988-5-andriin@fb.com Signed-off-by: Alexei Starovoitov <ast@kernel.org> |
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README |
Linux kernel ============ There are several guides for kernel developers and users. These guides can be rendered in a number of formats, like HTML and PDF. Please read Documentation/admin-guide/README.rst first. In order to build the documentation, use ``make htmldocs`` or ``make pdfdocs``. The formatted documentation can also be read online at: https://www.kernel.org/doc/html/latest/ There are various text files in the Documentation/ subdirectory, several of them using the Restructured Text markup notation. Please read the Documentation/process/changes.rst file, as it contains the requirements for building and running the kernel, and information about the problems which may result by upgrading your kernel.