package multicore-bench

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Framework for writing multicore benchmark executables to run on current-bench


Dune Dependency






Published: 01 Jul 2024


API reference · Benchmarks


Multicore bench is a framework for writing multicore benchmark executables to run locally on your computer and on current-bench.

Benchmarking multicore algorithms tends to a require certain amount of setup, such as spawning domains, synchronizing them before work, timing the work, collecting the times, and joining domains, that this framework tries to take care of for you as conveniently as possible. Furthermore, benchmarking multicore algorithms in OCaml also involves a number of pitfalls related to how the OCaml runtime works. For example, when only a single domain is running, several operations provided by the OCaml runtime use specialized implementations that take advantage of the fact that there is only a single domain running. In most cases, when trying to benchmark multicore algorithms, you don't actually want to measure those specialized runtime implementations.

The design of multicore bench is considered experimental. We are planning to improve the design along with current-bench in the future to allow more useful benchmarking experience.

Crash course to current-bench

Note that, at the time of writing this, current-bench is work in progress and does not accept enrollment for community projects. However, assuming you have access to it, to run multicore benchmarks with current-bench a number of things need to be setup:

  • You will need a Makefile with a bench target at the root of the project. The current-bench service will run your benchmarks through that.

  • You likely also want to have a bench.Dockerfile and .dockerignore at the root of the project. Make sure that the Dockerfile is layered such that it will pickup opam updates when desired while also avoiding unnecessary work during rebuilds.

  • You will also need the benchmarks and that is where this framework may help. You can find examples of multicore benchmarks from the Saturn, Kcas, and Picos projects and from the bench directory of this repository.

For multicore benchmarks you will also need to have current-bench configured to use a multicore machine, which currently needs to be done by the current-bench maintainers.

Example: Benchmarking Atomic.incr under contention

Let's look at a simple example with detailed comments of how one might benchmark Atomic.incr under contention.

Note that this example is written here as a MDX document or test. Normally you would write a benchmark as a command line executable and would likely compile it in release mode with a native compiler.

We first open the Multicore_bench module:

# open Multicore_bench

This brings into scope multiple modules including Suite, Util, Times, and Cmd that we used below.

Typically one would divide a benchmark executable into benchmark suites for different algorithms and data structures. To illustrate that pattern, let's create a module Bench_atomic for our benchmarks suite on atomics:

# module Bench_atomic : sig
    (* The entrypoint to a suite is basically a function.  There is a type
       alias for the signature. *)
    val run_suite : Suite.t
  end = struct
    (* [run_one] runs a single benchmark with the given budget and number of
       domains. *)
    let run_one ~budgetf ~n_domains () =
      (* We scale the number of operations using [Util.iter_factor], which
         depends on various factors such as whether we are running on a 32- or
         64-bit machine, using a native or bytecode compiler, and whether we are
         running on multicore OCaml.  The idea is to make it possible to use the
         benchmark executable as a test that can be run even on slow CI
         machines. *)
      let n = 10 * Util.iter_factor in

      (* In this example, [atomic] is the data structure we are benchmarking. *)
      let atomic =
        Atomic.make 0
        |> Multicore_magic.copy_as_padded
        (* We explicitly pad the [atomic] to avoid false sharing.  With false
           sharing measurements are likely to have a lot of noise that makes
           it difficult to get useful results. *)

      (* We store the number of operations to perform in an atomic.  The idea
         is that we want all the workers or domains to work at the same time
         as much as possible, because we want to measure performance under
         contention.  So, instead of e.g. simply having each domain run a
         fixed count loop, which could lead to some domains finishing well
         before others, we let the number of operations performed by each domain
         vary. *)
      let n_ops_to_do =
        Atomic.make 0
        |> Multicore_magic.copy_as_padded
        (* We also explicitly pad the number of ops to avoid false sharing. *)

      (* [init] is called on each domain before [work].  The return value of
         [init] is passed to [work]. *)
      let init _domain_index =
        (* It doesn't matter that we set the atomic multiple times.  We could
           also use a [before] callback to do setup before [work]. *)
        Atomic.set n_ops_to_do n

      (* [work] is called on each domain and the time it takes is recorded.
         The second argument comes from [init]. *)
      let work _domain_index () =
        (* Because we are benchmarking operations that take a very small amount
           of time, we run our own loop to perform the operations.  This has
           pros and cons.  One con is that the loop overhead will be part of the
           measurement, which is something to keep in mind when interpreting the
           results.  One pro is that this gives more flexibility in various
           ways. *)
        let rec work () =
          (* We try to allocate some number of operations to perform. *)
          let n = Util.alloc n_ops_to_do in
          (* If we got zero, then we should stop. *)
          if n <> 0 then begin
            (* Otherwise we perform the operations and try again. *)
            for _=1 to n do
              Atomic.incr atomic
            work ()
        work ()

      (* [config] is a name for the configuration of the benchmark.  In this
         case we distinguish by the number of workers or domains. *)
      let config =
        Printf.sprintf "%d worker%s" n_domains
          (if n_domains = 1 then "" else "s")

      (* [Times.record] does the heavy lifting to spawn domains and measure
         the time [work] takes on them. *)
      let times = Times.record ~budgetf ~n_domains ~init ~work () in

      (* [Times.to_thruput_metrics] takes the measurements and produces both a
         metric for the time of a single operation and for the total thruput
         over all the domains. *)
      Times.to_thruput_metrics ~n ~singular:"incr" ~config times

    (* [run_suite] runs the benchmarks in this suite with the given budget. *)
    let run_suite ~budgetf =
      (* In this case we run the benchmark with various number of domains. We
         use [concat_map] to collect the results as a flat list of outputs. *)
      [ 1; 2; 4; 8 ]
      |> List.concat_map @@ fun n_domains ->
         run_one ~budgetf ~n_domains ()
module Bench_atomic : sig val run_suite : Suite.t end

We then collect all the suites into an association list. The association list has a name and entry point for each suite:

# let benchmarks = [
    ("Atomic", Bench_atomic.run_suite)
val benchmarks : (string * Suite.t) list = [("Atomic", <fun>)]

Usually the list of benchmarks is in the main module of the benchmark executable along with an invocation of

# ~benchmarks ~argv:[||] ()
  "results": [
      "name": "Atomic",
      "metrics": [
          "name": "time per incr/1 worker",
          "value": 11.791,
          "units": "ns",
          "trend": "lower-is-better",
          "description": "Time to process one incr",
          "#best": 9.250000000000002,
          "#mean": 12.149960000000002,
          "#median": 11.791,
          "#sd": 1.851061543655424,
          "#runs": 25
          "name": "incrs over time/1 worker",
          "value": 84.81044864727335,
          "units": "M/s",
          "trend": "higher-is-better",
          "description": "Total number of incrs processed",
          "#best": 108.1081081081081,
          "#mean": 84.25129565093134,
          "#median": 84.81044864727335,
          "#sd": 12.911113376793846,
          "#runs": 25
        // ...
- : unit = ()

By default interprets command line arguments from Sys.argv. Unlike what one would typically do, we explicitly specify ~argv:[||], because this code is being run through the MDX tool.

Note that the output above is just a sample. The timings are non-deterministic and will slightly vary from one run of the benchmark to another even on a single computer.

Dependencies (7)

  1. ocaml >= "4.13.0"
  2. domain_shims >= "0.1.0"
  3. yojson >= "2.1.0"
  4. mtime >= "2.0.0"
  5. multicore-magic >= "2.1.0"
  6. domain-local-await >= "1.0.1"
  7. dune >= "3.14"

Dev Dependencies (4)

  1. odoc >= "2.4.1" & with-doc
  2. sherlodoc >= "0.2" & with-doc
  3. mdx >= "2.4.0" & with-test
  4. backoff >= "0.1.0" & with-test

Used by (2)

  1. kcas_data >= "0.7.0"
  2. picos




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