This Truffle language exposes GPUs to the polyglot GraalVM. The goal is to

1. make data exchange between the host language and the GPU efficient without burdening the programmer.

2. allow programmers to invoke existing GPU kernels from their host language.

Supported and tested GraalVM languages:

• Python
• JavaScript/NodeJS
• Ruby
• R
• Java
• C and Rust through the Graal Sulong Component

A description of grCUDA and its the features can be found in the grCUDA documentation.

The bindings documentation contains a tutorial that shows how to bind precompiled kernels to callables, compile and launch kernels.

Additional Information:

• grCUDA: A Polyglot Language Binding for CUDA in GraalVM. NVIDIA Developer Blog, November 2019.
• grCUDA: A Polyglot Language Binding. Presentation at Oracle CodeOne 2019, September 2019.
• Simplifying GPU Access. Presentation at NVIDIA GTC 2020, March 2020.

grCUDA can be used in the binaries of the GraalVM languages (lli, graalpython, js, R, and ruby). The JAR file containing grCUDA must be appended to the classpath or copied into jre/languages/grcuda of the Graal installation. Note that --jvm and --polyglot must be specified in both cases as well.

The following example shows how to create a GPU kernel and two device arrays in JavaScript (NodeJS) and invoke the kernel:

// build kernel from CUDA C/C++ source code
const kernelSource = `
__global__ void increment(int *arr, int n) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  if (idx < n) {
    arr[idx] += 1;
  }
}`
const cu = Polyglot.eval('grcuda', 'CU') // get grCUDA namespace object
const incKernel = cu.buildkernel(
  kernelSource, // CUDA kernel source code string
  'increment', // kernel name
  'pointer, sint32') // kernel signature

// allocate device array
const numElements = 100
const deviceArray = cu.DeviceArray('int', numElements)
for (let i = 0; i < numElements; i++) {
  deviceArray[i] = i // ... and initialize on the host
}
// launch kernel in grid of 1 block with 128 threads
incKernel(1, 128)(deviceArray, numElements)

// print elements from updated array
for (const element of deviceArray) {
  console.log(element)
}

$GRAALVM_DIR/bin/node --polyglot --jvm example.js
1
2
...
100

The next example shows how to launch an existing compiled GPU kernel from Python. The CUDA kernel

__global__ void increment(int *arr, int n) {
  auto idx = blockIdx.x * blockDim.x + threadIdx.x;
  if (idx < n) {
    arr[idx] += 1;
  }
}

is compiled using nvcc --cubin into a cubin file. The kernel function can be loaded from the cubin and bound to a callable object in the host language, here Python.

import polyglot

num_elements = 100
cu = polyglot.eval(language='grcuda', string='CU')
device_array = cu.DeviceArray('int', num_elements)
for i in range(num_elements):
  device_array[i] = i

# bind to kernel from binary
inc_kernel = cu.bindkernel('kernel.cubin',
  'cxx increment(arr: inout pointer sint32, n: sint32)')

# launch kernel as 1 block with 128 threads
inc_kernel(1, 128)(device_array, num_elements)

for i in range(num_elements):
  print(device_array[i])

nvcc --cubin  --generate-code arch=compute_75,code=sm_75 kernel.cu
$GRAALVM_DIR/bin/graalpython --polyglot --jvm example.py
1
2
...
100

For more details on how to invoke existing GPU kernels, see the Documentation on polyglot kernel launches.

grCUDA can be downloaded as a binary JAR from grcuda/releases and manually copied into a GraalVM installation.

1. Download GraalVM CE 20.0.0 for Linux graalvm-ce-java8-linux-amd64-20.0.0.tar.gz from GitHub and untar it in your installation directory.

   cd <your installation directory>
   tar xfz graalvm-ce-java8-linux-amd64-20.0.0.tar.gz
   export GRAALVM_DIR=`pwd`/graalvm-ce-java8-20.0.0

2. Download the grCUDA JAR from grcuda/releases

   cd $GRAALVM_DIR/jre/languages
   mkdir grcuda
   cp <download folder>/grcuda-0.1.0.jar grcuda

3. Test grCUDA in Node.JS from GraalVM.

   cd $GRAALVM_DIR/bin
   ./node --jvm --polyglot
   > arr = Polyglot.eval('grcuda', 'int[5]')
   [Array: null prototype] [ 0, 0, 0, 0, 0 ]

4. Download other GraalVM languages.

   cd $GRAAL_VM/bin
   ./gu available
   ./gu install python
   ./gu install R
   ./gu install ruby

grCUDA requires the mx build tool. Clone the mx repository and add the directory into $PATH, such that the mx can be invoked from the command line.

Build grCUDA and the unit tests:

cd <directory containing this README>
mx build

Note that this will also checkout the graal repository.

To run unit tests:

mx unittest com.nvidia