This is a part of Node3D project.

This addon is ABI-compatible across Node.js versions. There is no compilation during npm i.

Node.js addon with OpenCL 1.2 bindings. This is not WebCL.

The API directly reflects the low-level OpenCL interface. There are minor changes similar to how WebGL is different from OpenGL.

• All cl* methods are available as cl.* starting lowercase, e.g: clCreateKernel -> cl.createKernel.
• All CL_* constants are available as cl.*, e.g.: CL_TRUE -> cl.TRUE.
• The CL resource pointers are wrapped in JS objects, such as TClPlatform, TClContext, TClEvent.
• For cl.enqueue*() methods, you can pass hasEvent = true, in that case a TClEvent is returned.
• The CL status is not returned, instead a JS exception is thrown in case of a CL error.

Most of the method arguments comply to the original C-style spec, some parameters are omitted due to JS specifics. For example, passing an array, you don't need to specify its length.

See TypeScript declarations for more details.

1. Import the module:
   import cl from 'opencl-raub';
2. Fetch the CL control objects:
   const { context, device } = cl.quickStart(); // see /index.js
   const queue = cl.createCommandQueue(context, device);
3. Prepare the data input/output buffers:
   const BUFFER_SIZE = 10;
   const BYTE_SIZE = BUFFER_SIZE * Uint32Array.BYTES_PER_ELEMENT;
   
   const arrayA = new Uint32Array(BUFFER_SIZE);
   const arrayB = new Uint32Array(BUFFER_SIZE);
   const arrayC = new Uint32Array(BUFFER_SIZE);
   
   for (let i = 0; i < BUFFER_SIZE; i++) {
   arrayA[i] = i;
   arrayB[i] = i * 2;
   }
   
   // Create buffer for arrayA and arrayB and copy host contents
   const bufferA = cl.createBuffer(context, cl.MEM_READ_ONLY, BYTE_SIZE);
   const bufferB = cl.createBuffer(context, cl.MEM_READ_ONLY, BYTE_SIZE);
   
   // Create buffer for arrayC to read results
   const bufferC = cl.createBuffer(context, cl.MEM_WRITE_ONLY, BYTE_SIZE);
4. Create a valid CL program, e.g. from source:
   const program = cl.createProgramWithSource(context, `
   __kernel
   void vadd(__global int *a, __global int *b, __global int *c, uint num) {
   size_t i = get_global_id(0);
   if (i < num) {
   c[i] = a[i] + b[i];
   }
   }
   `);
   cl.buildProgram(program);
5. Fetch and setup a kernel from within the program:
   // Create a kernel object
   const kernel = cl.createKernel(program, 'vadd');
   
   // Set kernel args
   cl.setKernelArg(kernel, 0, 'uint*', bufferA);
   cl.setKernelArg(kernel, 1, 'uint*', bufferB);
   cl.setKernelArg(kernel, 2, 'uint*', bufferC);
   cl.setKernelArg(kernel, 3, 'uint', BUFFER_SIZE);
6. Launch the kernel and then read the results:
   // Do the work
   cl.enqueueWriteBuffer(queue, bufferA, true, 0, BYTE_SIZE, arrayA);
   cl.enqueueWriteBuffer(queue, bufferB, true, 0, BYTE_SIZE, arrayB);
   cl.enqueueNDRangeKernel(queue, kernel, 1, null, [BUFFER_SIZE]);
   cl.enqueueReadBuffer(queue, bufferC, true, 0, BYTE_SIZE, arrayC);
7. See if it worked:
   console.log(`A = [${arrayA.join(', ')}]`);
   console.log(`B = [${arrayB.join(', ')}]`);
   console.log(`C = [${arrayC.join(', ')}]`);
8. Release the CL objects:
   cl.releaseCommandQueue(queue);
   cl.releaseKernel(kernel);
   cl.releaseProgram(program);
   cl.releaseMemObject(bufferA);
   cl.releaseMemObject(bufferB);
   cl.releaseMemObject(bufferC);

See examples for more details. The full code of the above example is available here.