diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/com/jogamp/opencl/demos/hellojocl/HelloJOCL.java | 111 |
1 files changed, 64 insertions, 47 deletions
diff --git a/src/com/jogamp/opencl/demos/hellojocl/HelloJOCL.java b/src/com/jogamp/opencl/demos/hellojocl/HelloJOCL.java index 1daf890..70900eb 100644 --- a/src/com/jogamp/opencl/demos/hellojocl/HelloJOCL.java +++ b/src/com/jogamp/opencl/demos/hellojocl/HelloJOCL.java @@ -3,6 +3,7 @@ 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; @@ -11,6 +12,7 @@ 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 @@ -23,63 +25,78 @@ public class HelloJOCL { public static void main(String[] args) throws IOException { - int elementCount = 11444777; // Length of arrays to process - int localWorkSize = 256; // Local work size dimensions - int globalWorkSize = roundUp(localWorkSize, elementCount); // rounded up to the nearest multiple of the localWorkSize - - // set up + // set up (uses default CLPlatform and creates context for all devices) CLContext context = CLContext.create(); - - CLProgram program = context.createProgram(HelloJOCL.class.getResourceAsStream("VectorAdd.cl")).build(); - - CLBuffer<FloatBuffer> clBufferA = context.createFloatBuffer(globalWorkSize, READ_ONLY); - CLBuffer<FloatBuffer> clBufferB = context.createFloatBuffer(globalWorkSize, READ_ONLY); - CLBuffer<FloatBuffer> clBufferC = context.createFloatBuffer(globalWorkSize, WRITE_ONLY); - - out.println("used device memory: " - + (clBufferA.getCLSize()+clBufferB.getCLSize()+clBufferC.getCLSize())/1000000 +"MB"); - - // fill read 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 functon with the name 'VectorAdd' - // and map the buffers to its input parameters. - CLKernel kernel = program.createCLKernel("VectorAdd"); - kernel.putArgs(clBufferA, clBufferB, clBufferC).putArg(elementCount); - - // create command queue on fastest device. - CLCommandQueue queue = context.getMaxFlopsDevice().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) - .put1DRangeKernel(kernel, 0, globalWorkSize, 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.getBuffer().get() + ", "); - out.println("...; " + clBufferC.getBuffer().remaining() + " more"); - - out.println("computation took: "+(time/1000000)+"ms"); + 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<FloatBuffer> clBufferA = context.createFloatBuffer(globalWorkSize, READ_ONLY); + CLBuffer<FloatBuffer> clBufferB = context.createFloatBuffer(globalWorkSize, READ_ONLY); + CLBuffer<FloatBuffer> 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 final void fillBuffer(FloatBuffer buffer, int seed) { + 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 final int roundUp(int groupSize, int globalSize) { + private static int roundUp(int groupSize, int globalSize) { int r = globalSize % groupSize; if (r == 0) { return globalSize; |