package com.jogamp.opencl.demos.hellojocl; import com.jogamp.opencl.CLBuffer; import com.jogamp.opencl.CLCommandQueue; import com.jogamp.opencl.CLContext; import com.jogamp.opencl.CLDevice; import com.jogamp.opencl.CLKernel; import com.jogamp.opencl.CLProgram; import java.io.IOException; import java.nio.FloatBuffer; import java.util.Random; import static java.lang.System.*; import static com.jogamp.opencl.CLMemory.Mem.*; import static java.lang.Math.*; /** * Hello Java OpenCL example. Adds all elements of buffer A to buffer B * and stores the result in buffer C.

* Sample was inspired by the Nvidia VectorAdd example written in C/C++ * which is bundled in the Nvidia OpenCL SDK. * @author Michael Bien */ public class HelloJOCL { public static void main(String[] args) throws IOException { // set up (uses default CLPlatform and creates context for all devices) CLContext context = CLContext.create(); out.println("created "+context); // always make sure to release the context under all circumstances // not needed for this particular sample but recommented try{ // select fastest device CLDevice device = context.getMaxFlopsDevice(); out.println("using "+device); // create command queue on device. CLCommandQueue queue = device.createCommandQueue(); int elementCount = 1444477; // Length of arrays to process int localWorkSize = min(device.getMaxWorkGroupSize(), 256); // Local work size dimensions int globalWorkSize = roundUp(localWorkSize, elementCount); // rounded up to the nearest multiple of the localWorkSize // load sources, create and build program CLProgram program = context.createProgram(HelloJOCL.class.getResourceAsStream("VectorAdd.cl")).build(); // A, B are input buffers, C is for the result CLBuffer clBufferA = context.createFloatBuffer(globalWorkSize, READ_ONLY); CLBuffer clBufferB = context.createFloatBuffer(globalWorkSize, READ_ONLY); CLBuffer clBufferC = context.createFloatBuffer(globalWorkSize, WRITE_ONLY); out.println("used device memory: " + (clBufferA.getCLSize()+clBufferB.getCLSize()+clBufferC.getCLSize())/1000000 +"MB"); // fill input buffers with random numbers // (just to have test data; seed is fixed -> results will not change between runs). fillBuffer(clBufferA.getBuffer(), 12345); fillBuffer(clBufferB.getBuffer(), 67890); // get a reference to the kernel function with the name 'VectorAdd' // and map the buffers to its input parameters. CLKernel kernel = program.createCLKernel("VectorAdd"); kernel.putArgs(clBufferA, clBufferB, clBufferC).putArg(elementCount); // asynchronous write of data to GPU device, // followed by blocking read to get the computed results back. long time = nanoTime(); queue.putWriteBuffer(clBufferA, false) .putWriteBuffer(clBufferB, false) .put1DRangeKernel(kernel, 0, globalWorkSize, localWorkSize) .putReadBuffer(clBufferC, true); time = nanoTime() - time; // print first few elements of the resulting buffer to the console. out.println("a+b=c results snapshot: "); for(int i = 0; i < 10; i++) out.print(clBufferC.getBuffer().get() + ", "); out.println("...; " + clBufferC.getBuffer().remaining() + " more"); out.println("computation took: "+(time/1000000)+"ms"); }finally{ // cleanup all resources associated with this context. context.release(); } } private static void fillBuffer(FloatBuffer buffer, int seed) { Random rnd = new Random(seed); while(buffer.remaining() != 0) buffer.put(rnd.nextFloat()*100); buffer.rewind(); } private static int roundUp(int groupSize, int globalSize) { int r = globalSize % groupSize; if (r == 0) { return globalSize; } else { return globalSize + groupSize - r; } } }