**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.

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 OpenCLβ’ 2.0 Driver | macOS's built-in OpenCL SDK |

Intel or AMD CPU | POCL | POCL | POCL |

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

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) {
"natives-macos"
} else if (isLinux) {
"natives-linux"
} else if (isWin) {
"natives-windows"
} 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.

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`

,

`println(my2DArray)`

then the output should be

```
[[1.0,2.0,3.0],[4.0,5.0,6.0]]
```

You can also print the sizes of each dimension using the `shape`

method.

```
// Output 2 because my2DArray is a 2D array.
println(my2DArray.shape.length)
// 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.

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 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]]
```

`Tensor`

s in Compute.scala are immutable and lazy-evaluated. All operators that create `Tensor`

s 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 `Tensor`

s, including `a`

, `b`

, `c`

and `result`

are small JVM objects and no computation is performed up to now.

`println(result.toString)`

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 `InlineTensor`

s, 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.

```
println(result.toString)
// The computation is performed, again
println(result.toString)
```

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.
println(cachedTensor.toString)
// The cache is reused. No device-side computation is performed.
println(cachedTensor.toString)
val tmp: InlineTensor = exp(cachedTensor)
// The cache for cachedTensor is reused, but the exponential function is performed.
println(tmp.toString)
// The cache for cachedTensor is reused, but the exponential function is performed, again.
println(tmp.toString)
} finally {
releaseCache.blockingAwait
}
// Crash because the data buffer has been released
println(releaseCache.toString)
```

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 `Tensor`

s along with the impure `doCache`

mechanism, we achieved the following goals:

- All
`Tensor`

s are pure. No data buffer is allocated when creating them. - The computation of
`Tensor`

s can be merged together, to minimize the number of intermediate data buffers and kernel programs. - The developers can create caches for
`Tensor`

s, as a determinate way to manage the life-cycle of resources.

`Tensor`

s 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
releaseWeight.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.

A `Tensor`

can be `split`

into small `Tensor`

s on the direction of a specific dimension.

For example, given a 3D tensor whose `shape`

is 2Γ3Γ4,

```
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 3Γ4 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]])
println(subtensors0)
```

When `split`

it at the dimension #1,

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

then the result should be a `Seq`

of three 2Γ4 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]])
println(subtensors1)
```

Then you can use arbitrary Scala collection functions on the `Seq`

of subtensors.

Multiple `Tensor`

s of the same `shape`

can be merged into a larger `Tensor`

via the `Tensor.join`

function.

Given a `Seq`

of three 2Γ2 `Tensor`

s,

```
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 `join`

ing 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]]]
println(merged.toString)
```

Generally, when `join`

ing *n* `Tensor`

s of shape *a*_{0}β
Γβ
*a*_{1}β
Γβ
*a*_{2}β
Γβ
β―β
Γβ
*a*_{i} , the shape of the result `Tensor`

is *a*_{0}β
Γβ
*a*_{1}β
Γβ
*a*_{2}β
Γβ
β―β
Γβ
*a*_{i}β
Γβ
*n*

By combining `split`

and `join`

, you can create complex computation in the following steps:

- Using
`split`

to create`Seq`

s from some of dimensions of`Tensor`

s. - Using Scala collection functions to manipulate
`Seq`

s. - 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 = columns2.map { column2: Tensor =>
(columns1 zip column2.split(0))
.map {
case (l: Tensor, r: Tensor) =>
l * r.broadcast(l.shape)
}
.reduce[Tensor](_ + _)
}
Tensor.join(resultColumns)
}
```

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 65536Γ8 and `matrix2`

is 8Γ8.

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.

- Compute.scala vs ND4J on an NVIDIA Titan X GPU (source code)
- Compute.scala on an AMD RX480 GPU (source code)

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.

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.