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+package com.mbien.opencl.demos.hellojocl;
+
+import com.mbien.opencl.CL;
+import com.mbien.opencl.CLBuffer;
+import com.mbien.opencl.CLCommandQueue;
+import com.mbien.opencl.CLContext;
+import com.mbien.opencl.CLKernel;
+import com.mbien.opencl.CLProgram;
+import java.io.IOException;
+import java.nio.ByteBuffer;
+import java.util.Random;
+
+import static java.lang.System.*;
+import static com.sun.gluegen.runtime.BufferFactory.*;
+
+/**
+ * Hello Java OpenCL example. Adds all elements of buffer A to buffer B
+ * and stores the result in buffer C.<br/>
+ * 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 {
+
+ int elementCount = 11444777; // Length of float arrays to process
+ int localWorkSize = 256; // set and log Global and Local work size dimensions
+ int globalWorkSize = roundUp(localWorkSize, elementCount); // rounded up to the nearest multiple of the localWorkSize
+
+ // set up
+ CLContext context = CLContext.create();
+
+ CLProgram program = context.createProgram(HelloJOCL.class.getResourceAsStream("VectorAdd.cl")).build();
+
+ CLBuffer clBufferA = context.createBuffer(CL.CL_MEM_READ_ONLY, globalWorkSize*SIZEOF_INT);
+ CLBuffer clBufferB = context.createBuffer(CL.CL_MEM_READ_ONLY, globalWorkSize*SIZEOF_INT);
+ CLBuffer clBufferC = context.createBuffer(CL.CL_MEM_WRITE_ONLY, globalWorkSize*SIZEOF_INT);
+
+ out.println("used device memory: "
+ + (clBufferA.buffer.capacity()+clBufferB.buffer.capacity()+clBufferC.buffer.capacity())/1000000 +"MB");
+
+ // fill read buffers with random numbers (just to have test data; seed is fixed -> results will not change between).
+ fillBuffer(clBufferA.buffer, 12345);
+ fillBuffer(clBufferB.buffer, 67890);
+
+ // get a reference to the kernel functon with the name 'VectorAdd' and map the buffers to its input parameters.
+ CLKernel kernel = program.getCLKernels().get("VectorAdd");
+ kernel.setArg(0, SIZEOF_LONG, clBufferA)
+ .setArg(1, SIZEOF_LONG, clBufferB)
+ .setArg(2, SIZEOF_LONG, clBufferC)
+ .setArg(3, SIZEOF_INT, elementCount);
+
+ // create command queue on first device.
+ CLCommandQueue queue = context.getCLDevices()[0].createCommandQueue();
+
+ // asynchronous write of data to GPU device, blocking read later to get the computed results back.
+ long time = nanoTime();
+ queue.putWriteBuffer(clBufferA, false)
+ .putWriteBuffer(clBufferB, false)
+ .putNDRangeKernel(kernel, 1, null, new long[]{ globalWorkSize }, new long[]{ localWorkSize })
+ .putReadBuffer(clBufferC, true);
+ time = nanoTime() - time;
+
+ // cleanup all resources associated with this context.
+ context.release();
+
+ // 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.buffer.getInt() + ", ");
+ out.println("...; " + clBufferC.buffer.remaining()/SIZEOF_INT + " more");
+
+ System.out.println("computation took: "+(time/1000000)+"ms");
+
+ }
+
+ public static final void fillBuffer(ByteBuffer buffer, int seed) {
+
+ Random rnd = new Random(seed);
+
+ while(buffer.remaining() != 0)
+ buffer.putInt(rnd.nextInt());
+
+ buffer.rewind();
+ }
+
+ public static final int roundUp(int groupSize, int globalSize) {
+ int r = globalSize % groupSize;
+ if (r == 0) {
+ return globalSize;
+ } else {
+ return globalSize + groupSize - r;
+ }
+ }
+
+}