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Compute.scala is a Scala library for scientific computing with N-dimensional arrays in parallel on GPU, CPU and other devices. It will be the primary back-end of the incoming DeepLearning.scala 3.0, to address performance problems we encountered in DeepLearning.scala 2.0 with ND4J.

  • Compute.scala can dynamically merge multiple operators into one kernel program, which runs significantly faster when performing complex computation.
  • Compute.scala manages data buffers and other native resources in a determinate approach, consuming less memory and reducing the performance impact due to garbage collection.
  • All dimensional transformation operators (permute, broadcast, translate, etc) in Compute.scala are views, with no additional data buffer allocation.
  • N-dimensional arrays in Compute.scala can be split to JVM collections, which support higher-ordered functions like map / reduce, and still can run on GPU.

Getting started

System Requirements

Compute.scala is based on LWJGL 3's OpenCL binding, which supports AMD, NVIDIA and Intel's GPU and CPU on Linux, Windows and macOS.

Make sure you have met the following system requirements before using Compute.scala.

  • Linux, Windows or macOS
  • JDK 8
  • OpenCL runtime

The performance of Compute.scala varies with OpenCL runtimes. For best performance, install the OpenCL runtime according to the following table.

Linux Windows macOS
NVIDIA GPU NVIDIA GPU Driver NVIDIA GPU Driver macOS's built-in OpenCL SDK
AMD GPU AMDGPU-PRO Driver AMD OpenCL™ 2.0 Driver macOS's built-in OpenCL SDK

Especially, Compute.scala produces non-vectorized code, which needs POCL's auto-vectorization feature for best performance when running on CPU.

Project setup

The artifacts of Compute.scala is published on Maven central repository for Scala 2.11 and 2.12. Add the following settings to your build.sbt if you are using sbt.

libraryDependencies += "com.thoughtworks.compute" %% "cpu" % "latest.release"

libraryDependencies += "com.thoughtworks.compute" %% "gpu" % "latest.release"

// LWJGL OpenCL library
libraryDependencies += "org.lwjgl" % "lwjgl-opencl" % "latest.release"

// Platform dependent runtime of LWJGL core library
libraryDependencies += ("org.lwjgl" % "lwjgl" % "latest.release").jar().classifier {
  import scala.util.Properties._
  if (isMac) {
  } else if (isLinux) {
  } else if (isWin) {
  } else {
    throw new MessageOnlyException(s"lwjgl does not support $osName")

Check Compute.scala on Scaladex and LWJGL customize tool for settings for Maven, Gradle and other build tools.

Creating an N-dimensional array

Import types in gpu or cpu object according to the OpenCL runtime you want to use.

// For N-dimensional array on GPU
import com.thoughtworks.compute.gpu._
// For N-dimensional array on CPU
import com.thoughtworks.compute.cpu._

In Compute.scala, an N-dimensional array is typed as Tensor, which can be created from Seq or Array.

val my2DArray: Tensor = Tensor(Array(Seq(1.0f, 2.0f, 3.0f), Seq(4.0f, 5.0f, 6.0f)))

If you print out my2DArray,


then the output should be


You can also print the sizes of each dimension using the shape method.

// Output 2 because my2DArray is a 2D array.

// Output 2 because the size of first dimension of my2DArray is 2.
println(my2DArray.shape(0)) // 2

// Output 3 because the size of second dimension of my2DArray is 3.
println(my2DArray.shape(1)) // 3

So my2DArray is a 2D array of 2x3 size.

Scalar value

Note that a Tensor can be a zero dimensional array, which is simply a scalar value.

val scalar = Tensor(42.0f)
println(scalar.shape.length) // 0

Element-wise operators

Element-wise operators are performed for each element of in Tensor operands.

val plus100 = my2DArray + Tensor.fill(100.0f, Array(2, 3))

println(plus100) // Output [[101.0,102.0,103.0],[104.0,105.0,106.0]]



Tensors in Compute.scala are immutable and lazy-evaluated. All operators that create Tensors are pure, which allocate zero data buffer and not execute any time-consuming tasks. The actual computation is only performed when the final result is requested.

For example:

val a = Tensor(Seq(Seq(1.0f, 2.0f, 3.0f), Seq(4.0f, 5.0f, 6.0f)))
val b = Tensor(Seq(Seq(7.0f, 8.0f, 9.0f), Seq(10.0f, 11.0f, 12.0f)))
val c = Tensor(Seq(Seq(13.0f, 14.0f, 15.0f), Seq(16.0f, 17.0f, 18.0f)))

val result: InlineTensor = a * b + c

All the Tensors, including a, b, c and result are small JVM objects and no computation is performed up to now.


When result.toString is called, the Compute.scala compiles the expression a * b + c into one kernel program and execute it.

Both result and the temporary variable a * b are InlineTensors, indicating their computation can be inlined into a more complex kernel program. You can think of an InlineTensor as an @inline def method on device side.

This approach is faster than other libraries because we don't have to execute two kernels for multiplication and addition respectively.

Check the Scaladoc seeing which operators return InlineTensor or its subtype TransformedTensor, which can be inlined into a more complex kernel program as well.


By default, when result.toString is called more than once, the expression a * b + c is executed more than once.


// The computation is performed, again

Fortunately, we provides a doCache method to eagerly allocate data buffer for a CachedTensor.

import com.thoughtworks.future._
import com.thoughtworks.raii.asynchronous._

val Resource(cachedTensor, releaseCache) = result.doCache.acquire.blockingAwait

try {
  // The cache is reused. No device-side computation is performed.

  // The cache is reused. No device-side computation is performed.

  val tmp: InlineTensor = exp(cachedTensor)
  // The cache for cachedTensor is reused, but the exponential function is performed.

  // The cache for cachedTensor is reused, but the exponential function is performed, again.
} finally {

// Crash because the data buffer has been released

The data buffer allocated for cachedTensor is kept until releaseCache is performed.

You can think of a CachedTensor as a lazy val on device side.

By combining pure Tensors along with the impure doCache mechanism, we achieved the following goals:

  • All Tensors are pure. No data buffer is allocated when creating them.
  • The computation of Tensors can be merged together, to minimize the number of intermediate data buffers and kernel programs.
  • The developers can create caches for Tensors, as a determinate way to manage the life-cycle of resources.

Mutable variables

Tensors are immutable, but you can create mutable variables of cached tensor to workaround the limitation.

var Resource(weight, releaseWeight) = Tensor.random(Array(32, 32)).doCache.acquire.blockingAwait

while (true) {
  val Resource(newWeight, releaseNewWeight) = (weight * Tensor.random(Array(32, 32))).doCache.acquire.blockingAwait
  weight = newWeight
  releaseWeight = releaseNewWeight

Use this approach with caution. doCache should be only used for permanent data (e.g. the weights of a neural network). doCache is not designed for intermediate variables in a complex expression. A sophisticated Scala developer should be able to entirely avoid var and while in favor of recurisive functions.

Scala collection interoperability


A Tensor can be split into small Tensors on the direction of a specific dimension.

For example, given a 3D tensor whose shape is 2×3×4,

val my3DTensor = Tensor((0.0f until 24.0f by 1.0f).grouped(4).toSeq.grouped(3).toSeq)

val Array(2, 3, 4) = my3DTensor.shape

when split it at the dimension #0,

val subtensors0: Seq[Tensor] = my3DTensor.split(dimension = 0)

then the result should be a Seq of two 3×4 tensors.

// Output: TensorSeq([[0.0,1.0,2.0,3.0],[4.0,5.0,6.0,7.0],[8.0,9.0,10.0,11.0]], [[12.0,13.0,14.0,15.0],[16.0,17.0,18.0,19.0],[20.0,21.0,22.0,23.0]])

When split it at the dimension #1,

val subtensors1: Seq[Tensor] = my3DTensor.split(dimension = 1)

then the result should be a Seq of three 2×4 tensors.

// Output: TensorSeq([[0.0,1.0,2.0,3.0],[12.0,13.0,14.0,15.0]], [[4.0,5.0,6.0,7.0],[16.0,17.0,18.0,19.0]], [[8.0,9.0,10.0,11.0],[20.0,21.0,22.0,23.0]])

Then you can use arbitrary Scala collection functions on the Seq of subtensors.


Multiple Tensors of the same shape can be merged into a larger Tensor via the Tensor.join function.

Given a Seq of three 2×2 Tensors,

val mySubtensors: Seq[Tensor] = Seq(
  Tensor(Seq(Seq(1.0f, 2.0f), Seq(3.0f, 4.0f))),
  Tensor(Seq(Seq(5.0f, 6.0f), Seq(7.0f, 8.0f))),
  Tensor(Seq(Seq(9.0f, 10.0f), Seq(11.0f, 12.0f))),

when joining them,

val merged: Tensor = Tensor.join(mySubtensors)

then the result should be a 2x2x3 Tensor.

// Output: [[[1.0,5.0,9.0],[2.0,6.0,10.0]],[[3.0,7.0,11.0],[4.0,8.0,12.0]]]

Generally, when joining n Tensors of shape a0 × a1 × a2 ×  ⋯ × ai , the shape of the result Tensor is a0 × a1 × a2 ×  ⋯ × ai × n

Case study: fast matrix multiplication via split and join

By combining split and join, you can create complex computation in the following steps:

  1. Using split to create Seqs from some of dimensions of Tensors.
  2. Using Scala collection functions to manipulate Seqs.
  3. Using join to merge transformed Seq back to Tensor.

For example, you can implement matrix multiplication in this style.

def matrixMultiply1(matrix1: Tensor, matrix2: Tensor): Tensor = {
  val columns1 = matrix1.split(1)
  val columns2 = matrix2.split(1)
  val resultColumns = { column2: Tensor =>
    (columns1 zip column2.split(0))
      .map {
        case (l: Tensor, r: Tensor) =>
          l * r.broadcast(l.shape)
      .reduce[Tensor](_ + _)

You can imagine the Scala collection function calls as the code generator of the kernel program, thus the loop running in Scala collection will finally become an unrolled loop in the kernel program.

The above matrixMultiply1 will create a kernel program that contains an unrolled loop of each row and column of matrix2. Thus it runs very fast when matrix1 is big and matrix2 is small. Our benchmark shows that the above matrixMultiply1 runs even faster than ND4J's cuBLAS back-end, on a Titan X GPU, when matrix1 is 65536×8 and matrix2 is 8×8.

You can also create another version of matrix multiplication, which only unrolls the loop of each row of matrix2.

def matrixMultiply2(matrix1: Tensor, matrix2: Tensor): Tensor = {
  val Array(i, j) = matrix1.shape
  val Array(`j`, k) = matrix2.shape
  val broadcastMatrix1 = matrix1.broadcast(Array(i, j, k))
  val broadcastMatrix2 = matrix2.reshape(Array(1, j, k)).broadcast(Array(i, j, k))
  val product = broadcastMatrix1 * broadcastMatrix2
  product.split(1).reduce[Tensor](_ + _)

matrixMultiply2 will run faster than matrixMultiply1 when matrix1 is small.

A sophisticated matrix multiplication should dynamically switch the two implementations according to matrix size.

val UnrollThreshold = 4000

def matrixMultiply(matrix1: Tensor, matrix2: Tensor): Tensor = {
  if (matrix1.shape.head >= UnrollThreshold) {
    matrixMultiply1(matrix1, matrix2)
  } else {
    matrixMultiply2(matrix1, matrix2)

The final version of matrixMultiply will have good performance for both small and big matrixes.


We created some benchmarks for Compute.scala and ND4J on NVIDIA and AMD GPU in an immutable style.

Some information can be found in the benchmark result:

  • Apparently, Compute.scala supports both NVIDIA GPU and AMD GPU, while ND4J does not support AMD GPU.
  • Compute.scala is faster than ND4J when performing complex expressions.
  • Compute.scala is faster than ND4J on large arrays.
  • ND4J is faster than Compute.scala when performing one simple primary operation on small arrays.
  • ND4J's permute and broadcast are extremely slow, causing very low score in the convolution benchmark.

Note that the above result of ND4J is not the same as the performance in Deeplearning4j, because Deeplearning4j uses ND4J in a mutable style (i.e. a *= b; a += c instead of a * b + c) and ND4J has some undocumented optimizions for permute and broadcast when they are invoked with some special parameters from Deeplearning4j.

Future work

Now this project is only a minimum viable product. Many important features are still under development:

  • Support tensors of elements other than single-precision floating-point (#104).
  • Add more OpenCL math functions (#101).
  • Further optimization of performance (#62, #103).
  • Other back-ends (CUDA, Vulkan Compute).

Contribution is welcome. Check good first issues to start hacking.