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mratsim / weave

A state-of-the-art multithreading runtime: message-passing based, fast, scalable, ultra-low overhead
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data-parallelism fork-join message-passing multithreading openmp parallelism runtime scheduler task-parallelism task-scheduler threadpool work-stealing

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Weave, a state-of-the-art multithreading runtime

Github Actions CI\ License: Apache License: MIT Stability: experimental

"Good artists borrow, great artists steal." -- Pablo Picasso

Weave (codenamed "Project Picasso") is a multithreading runtime for the Nim programming language.

It is continuously tested on Linux, MacOS and Windows for the following CPU architectures: x86, x86_64 and ARM64 with the C and C++ backends.

Weave aims to provide a composable, high-performance, ultra-low overhead and fine-grained parallel runtime that frees developers from the common worries of "are my tasks big enough to be parallelized?", "what should be my grain size?", "what if the time they take is completely unknown or different?" or "is parallel-for worth it if it's just a matrix addition? On what CPUs? What if it's exponentiation?".

Thorough benchmarks track Weave performance against industry standard runtimes in C/C++/Cilk language on both Task parallelism and Data parallelism with a variety of workloads:

Benchmarks are issued from recursive tree algorithms, finance, linear algebra and High Performance Computing, game simulations. In particular Weave displays as low as 3x to 10x less overhead than Intel TBB and GCC OpenMP on overhead-bound benchmarks.

At implementation level, Weave unique feature is being-based on Message-Passing instead of being based on traditional work-stealing with shared-memory deques.

⚠️ Disclaimer:

Only 1 out of 2 complex synchronization primitives was formally verified to be deadlock-free. They were not submitted to an additional data race detection tool to ensure proper implementation.

Furthermore worker threads are state-machines and were not formally verified either.

Weave does limit synchronization to only simple SPSC and MPSC channels which greatly reduces the potential bug surface.

Installation

Weave can be simply installed with

nimble install weave

or for the devel version

nimble install weave@#master

Weave requires at least Nim v1.2.0

Changelog

The latest changes are available in the changelog.md file.

Demos

A raytracing demo is available, head over to demos/raytracing.

ray_trace_300samples_nim_threaded.png

Table of Contents

API

Task parallelism

Weave provides a simple API based on spawn/sync which works like async/await for IO-based futures.

The traditional parallel recursive Fibonacci would be written like this:

import weave

proc fib(n: int): int =
  # int64 on x86-64
  if n < 2:
    return n

  let x = spawn fib(n-1)
  let y = fib(n-2)

  result = sync(x) + y

proc main() =
  var n = 20

  init(Weave)
  let f = fib(n)
  exit(Weave)

  echo f

main()

Data parallelism

Weave provides nestable parallel for loop.

A nested matrix transposition would be written like this:

import weave

func initialize(buffer: ptr UncheckedArray[float32], len: int) =
  for i in 0 ..< len:
    buffer[i] = i.float32

proc transpose(M, N: int, bufIn, bufOut: ptr UncheckedArray[float32]) =
  ## Transpose a MxN matrix into a NxM matrix with nested for loops

  parallelFor j in 0 ..< N:
    captures: {M, N, bufIn, bufOut}
    parallelFor i in 0 ..< M:
      captures: {j, M, N, bufIn, bufOut}
      bufOut[j*M+i] = bufIn[i*N+j]

proc main() =
  let M = 200
  let N = 2000

  let input = newSeq[float32](M*N)
  # We can't work with seq directly as it's managed by GC, take a ptr to the buffer.
  let bufIn = cast[ptr UncheckedArray[float32]](input[0].unsafeAddr)
  bufIn.initialize(M*N)

  var output = newSeq[float32](N*M)
  let bufOut = cast[ptr UncheckedArray[float32]](output[0].addr)

  init(Weave)
  transpose(M, N, bufIn, bufOut)
  exit(Weave)

main()

Strided loops

You might want to use loops with a non unit-stride, this can be done with the following syntax.

import weave

init(Weave)

# expandMacros:
parallelForStrided i in 0 ..< 100, stride = 30:
  parallelForStrided j in 0 ..< 200, stride = 60:
    captures: {i}
    log("Matrix[%d, %d] (thread %d)\n", i, j, myID())

exit(Weave)

Complete list

We separate the list depending on the threading context

Root thread

The root thread is the thread that started the Weave runtime. It has special privileges.

Weave worker thread

A worker thread is automatically created per (logical) core on the machine. The root thread is also a worker thread. Worker threads are tuned to maximize throughput of computational tasks.

The max number of worker threads can be configured by the environment variable WEAVE_NUM_THREADS and default to your number of logical cores (including HyperThreading). Weave uses Nim's countProcessors() in std/cpuinfo

Foreign thread & Background service (experimental)

Weave can also be run as a background service and process jobs similar to the Executor concept in C++. Jobs will be processed in FIFO order.

Experimental: The distinction between spawn/sync on a Weave thread and submit/waitFor on a foreign thread may be removed in the future.

A background service can be started with either:

with thr an uninitialized Thread[void] or Thread[ptr Atomic[bool]]

Then the foreign thread should call:

and for shutdown

Once setup, a foreign thread can submit jobs via:

Within a job, tasks can be spawned and parallel for constructs can be used.

If runInBackground() does not provide fine enough control, a Weave background event loop can be customized using the following primitive:

For example:

proc runUntil*(_: typedesc[Weave], signal: ptr Atomic[bool]) =
  ## Start a Weave event loop until signal is true on the current thread.
  ## It wakes-up on job submission, handles multithreaded load balancing,
  ## help process tasks
  ## and spin down when there is no work anymore.
  preCondition: not signal.isNil
  while not signal[].load(moRelaxed):
    processAllandTryPark(Weave)
  syncRoot(Weave)

proc runInBackground*(
       _: typedesc[Weave],
       signalShutdown: ptr Atomic[bool]
     ): Thread[ptr Atomic[bool]] =
  ## Start the Weave runtime on a background thread.
  ## It wakes-up on job submissions, handles multithreaded load balancing,
  ## help process tasks
  ## and spin down when there is no work anymore.
  proc eventLoop(shutdown: ptr Atomic[bool]) {.thread.} =
    init(Weave)
    Weave.runUntil(shutdown)
    exit(Weave)
  result.createThread(eventLoop, signalShutdown)

Platforms supported

Weave supports all platforms with pthread and Windows. Missing pthread functionality may be emulated or unused. For example on MacOS, the pthread implementation does not expose barrier functionality or affinity settings.

C++ compilation

The syncScope feature will not compile correctly in C++ mode if it is used in a for loop. Upstream: https://github.com/nim-lang/Nim/issues/14118

Windows 32-bit

Windows 32-bit targets cannot use the MinGW compiler as it is missing support for EnterSynchronizationBarrier. MSVC should work instead.

Resource-restricted devices

Weave uses a flexible and efficient memory subsystem that has been optimized for a wide range of hardware: low power Raspberry Pi, phones, laptops, desktops and 30+ cores workstations. It currently assumes by default that 16KB at least are available on your hardware for a memory pool and that this memory pool can grow as needed. This can be tuned with -d:WV_MemArenaSize=2048 to have the base pool use 2KB for example. The pool size should be a multiple of 256 bytes. PRs to improve support of very restricted devices are welcome.

Backoff mechanism

A Backoff mechanism is enabled by default. It allows workers with no tasks to sleep instead of spinning aimlessly and burning CPU cycles.

It can be disabled with -d:WV_Backoff=off.

Weave using all CPUs

Weave multithreading is cooperative, idle threads send steal requests instead of actively stealing in other workers queue. This is called "work-requesting" in the literature as opposed to "work-stealing".

This means that a thread sleeping or stuck in a long computation may starve other threads and they will spin burning CPU cycles.

We call the root thread the thread that called init(Weave)

Experimental features

Experimental features might see API and/or implementation changes.

For example both parallelForStaged and parallelReduce allow reductions but parallelForStaged is more flexible, it however requires explicit use of locks and/or atomics.

LazyFlowvars may be enabled by default for certain sizes or if escape analysis become possible or if we prevent Flowvar from escaping their scope.

Data parallelism (experimental features)

Awaitable loop

Loops can be awaited. Awaitable loops return a normal Flowvar.

This blocks the thread that spawned the parallel loop from continuing until the loop is resolved. The thread does not stay idle and will steal and run other tasks while being blocked.

Calling sync on the awaitable loop Flowvar will return true for the last thread to exit the loop and false for the others.

⚠️ This is not a barrier: if that loop spawns tasks (including via a nested loop) and exits, the thread will continue, it will not wait for the grandchildren tasks to be finished. Use a syncScope section to wait on all tasks and descendants including grandchildren.

import weave

init(Weave)

# expandMacros:
parallelFor i in 0 ..< 10:
  awaitable: iLoop
  echo "iteration: ", i

let wasLastThread = sync(iLoop)
echo wasLastThread

exit(Weave)

Parallel For Staged

Weave provides a parallelForStaged construct with supports for thread-local prologue and epilogue.

A parallel sum would look like this:

proc sumReduce(n: int): int =
  let res = result.addr # For mutation we need to capture the address.

  parallelForStaged i in 0 .. n:
    captures: {res}
    awaitable: iLoop
    prologue:
      var localSum = 0
    loop:
      localSum += i
    epilogue:
      echo "Thread ", getThreadID(Weave), ": localsum = ", localSum
      res[].atomicInc(localSum)

  let wasLastThread = sync(iLoop)

init(Weave)
let sum1M = sumReduce(1000000)
echo "Sum reduce(0..1000000): ", sum1M
doAssert sum1M == 500_000_500_000
exit(Weave)

parallelForStagedStrided is also provided.

Parallel Reduction

Weave provides a parallel reduction construct that avoids having to use explicit synchronization like atomics or locks but instead uses Weave sync(Flowvar) under-the-hood.

Syntax is the following:

proc sumReduce(n: int): int =
  var waitableSum: Flowvar[int]

  # expandMacros:
  parallelReduceImpl i in 0 .. n, stride = 1:
    reduce(waitableSum):
      prologue:
        var localSum = 0
      fold:
        localSum += i
      merge(remoteSum):
        localSum += sync(remoteSum)
      return localSum

  result = sync(waitableSum)

init(Weave)
let sum1M = sumReduce(1000000)
echo "Sum reduce(0..1000000): ", sum1M
doAssert sum1M == 500_000_500_000
exit(Weave)

In the future the waitableSum will probably be not required to be declared beforehand. Or parallel reduce might be removed to only keep parallelForStaged.

Dataflow parallelism

Dataflow parallelism allows expressing fine-grained data dependencies between tasks. Concretely a task is delayed until all its dependencies are met and once met, it is triggered immediately.

This allows precise specification of data producer-consumer relationships.

In contrast, classic task parallelism can only express control-flow dependencies (i.e. parent-child function calls relationships) and classic tasks are eagerly scheduled.

In the literature, it is also called:

Tagged experimental as the API and its implementation are unique compared to other libraries/language-extensions. Feedback welcome.

No specific ordering is required between calling the event producer and its consumer(s).

Dependencies are expressed by a handle called FlowEvent. An flow event can express either a single dependency, initialized with newFlowEvent() or a dependencies on parallel for loop iterations, initialized with newFlowEvent(start, exclusiveStop, stride)

To await on a single event pass it to spawnOnEvent or the parallelFor invocation. To await on an iteration, pass a tuple:

Delayed computation with single dependencies

import weave

proc echoA(eA: FlowEvent) =
  echo "Display A, sleep 1s, create parallel streams 1 and 2"
  sleep(1000)
  eA.trigger()

proc echoB1(eB1: FlowEvent) =
  echo "Display B1, sleep 1s"
  sleep(1000)
  eB1.trigger()

proc echoB2() =
  echo "Display B2, exit stream"

proc echoC1() =
  echo "Display C1, exit stream"

proc main() =
  echo "Dataflow parallelism with single dependency"
  init(Weave)
  let eA = newFlowEvent()
  let eB1 = newFlowEvent()
  spawnOnEvent eB1, echoC1()
  spawnOnEvent eA, echoB2()
  spawnOnEvent eA, echoB1(eB1)
  spawn echoA(eA)
  exit(Weave)

main()

Delayed computation with multiple dependencies

import weave

proc echoA(eA: FlowEvent) =
  echo "Display A, sleep 1s, create parallel streams 1 and 2"
  sleep(1000)
  eA.trigger()

proc echoB1(eB1: FlowEvent) =
  echo "Display B1, sleep 1s"
  sleep(1000)
  eB1.trigger()

proc echoB2(eB2: FlowEvent) =
  echo "Display B2, no sleep"
  eB2.trigger()

proc echoC12() =
  echo "Display C12, exit stream"

proc main() =
  echo "Dataflow parallelism with multiple dependencies"
  init(Weave)
  let eA = newFlowEvent()
  let eB1 = newFlowEvent()
  let eB2 = newFlowEvent()
  spawnOnEvents eB1, eB2, echoC12()
  spawnOnEvent eA, echoB2(eB2)
  spawnOnEvent eA, echoB1(eB1)
  spawn echoA(eA)
  exit(Weave)

main()

Delayed loop computation

You can combine data parallelism and dataflow parallelism.

Currently parallel loops only support one dependency (single, fixed iteration or range iteration).

Here is an example with a range iteration dependency. Note: when sleeping threads are unresponsive, meaning a sleeping thread cannot schedule other ready tasks.

import weave

proc main() =
  init(Weave)

  let eA = newFlowEvent(0, 10, 1)
  let pB = newFlowEvent(0, 10, 1)

  parallelFor i in 0 ..< 10:
    captures: {eA}
    sleep(i * 10)
    eA.trigger(i)
    echo "Step A - stream ", i, " at ", i * 10, " ms"

  parallelFor i in 0 ..< 10:
    dependsOn: (eA, i)
    captures: {pB}
    sleep(i * 10)
    pB.trigger(i)
    echo "Step B - stream ", i, " at ", 2 * i * 10, " ms"

  parallelFor i in 0 ..< 10:
    dependsOn: (pB, i)
    sleep(i * 10)
    echo "Step C - stream ", i, " at ", 3 * i * 10, " ms"

  exit(Weave)

main()

Lazy Allocation of Flowvars

Flowvars can be lazily allocated, this reduces overhead by at least 2x on very fine-grained tasks like Fibonacci or Depth-First-Search that may spawn trillions of tasks in less than a couple hundreds of milliseconds. This can be enabled with -d:WV_LazyFlowvar.

⚠️ This only works for Flowvar of a size up to your machine word size (int64, float64, pointer on 64-bit machines) ⚠️ Flowvars cannot be returned in that mode, you will at best trigger stack smashing protection or crash

Limitations

Weave has not been tested with GC-ed types. Pass a pointer around or use Nim channels which are GC-aware. If it works, a heads-up would be valuable.

This might improve with Nim ARC/newruntime.

Statistics

Curious minds can access the low-level runtime statistic with the flag -d:WV_metrics which will give you the information on number of tasks executed, steal requests sent, etc.

Very curious minds can also enable high resolution timers with -d:WV_metrics -d:WV_profile -d:CpuFreqMhz=3000 assuming you have a 3GHz CPU.

The timers will give you in this order:

Time spent running tasks, Time spent recv/send steal requests, Time spent recv/send tasks, Time spent caching tasks, Time spent idle, Total

Tuning

A number of configuration options are available in weave/config.nim.

In particular:

Unique features

Weave provides an unique scheduler with the following properties:

The "Project Picasso" RFC is available for discussion in Nim RFC #160 or in the (potentially outdated) picasso_RFC.md file

Research

Weave is based on the research by Andreas Prell. You can read his PhD Thesis or access his C implementation.

Several enhancements were built into Weave, in particular:

License

Licensed and distributed under either of

or

at your option. These files may not be copied, modified, or distributed except according to those terms.