- added example05 for clfft real in-place transform

- added readme to example04 for installing clfft

example05/main.c

@@ -0,0 +1,162 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <clFFT.h>
+ 
+const char *kernelSource =
+"#pragma OPENCL EXTENSION cl_khr_fp64 : enable  \n" \
+"__kernel void mult(__global double *v) {       \n" \
+"    int id, v_re, v_im;       \n" \
+"    id   = get_global_id(0);  \n" \
+"    v_re = 2*id;              \n" \
+"    v_im = v_re + 1;          \n" \
+"                              \n" \
+"    v[v_re] = 2*v[v_re];      \n" \
+"    v[v_im] = 4*v[v_im];      \n" \
+"}                             \n" \
+"\n" ;
+
+
+int roundUpToNearest(int x, int n) {
+    /* Rounds x UP to nearest multiple of n. */
+    int x_rem = x % n;
+    if (x_rem == 0)
+        return x;
+
+    return x + (n - x_rem);
+}
+
+ 
+int main( int argc, char* argv[] ) {
+    /* This setup is a bit tricky. Since we're doing a real transform, CLFFT
+     * requires N+2 elements in the array. This is because only N/2 + 1 numbers
+     * are calculated, and since each number is complex, it requires 2 elements
+     * for space.
+     *
+     * To avoid warp divergence, we want to avoid any conditionals in the
+     * kernel. Thus we cannot check to see if the thread ID is even or odd to
+     * act on a real number or imaginary number. To do this, one thread should
+     * handle one complex number (one real, one imag), i.e. ID_j should handle
+     * array elements j, j+1.
+     *
+     * But we also need the number of global items to be a multiple of 32 (warp
+     * size). What we can do, for example, N = 128, is pad it by 2 (130),
+     * divide it by 2 (65), round that UP to the nearest 32 (96), multiply that
+     * by 2 (192). The kernel will operate on zeros, but it should be faster
+     * than the scenario with warp divergence. */
+
+    unsigned int N = 128;
+    unsigned int N_pad = 2*roundUpToNearest( (N+2)/2, 32 );
+    size_t N_bytes = N_pad * sizeof(double);
+
+    // openCL declarations
+    cl_platform_id platform;
+    cl_device_id device_id;
+    cl_context context; 
+    cl_command_queue queue;
+    cl_program program;
+    cl_kernel k_mult;
+
+    // clFFT declarations
+    clfftPlanHandle planHandleForward, planHandleBackward;
+    clfftDim dim = CLFFT_1D;
+    size_t clLengths[1] = {N};
+    clfftSetupData fftSetup;
+    clfftInitSetupData(&fftSetup);
+    clfftSetup(&fftSetup);
+ 
+    // host version of v
+    double *h_v;  // real & imaginary parts
+    h_v = (double*) malloc(N_bytes);
+ 
+    // initialize v on host
+    int i;
+    for (i = 0; i < N; i++) {
+        h_v[i] = i;
+    }
+
+    // global & local number of threads
+    size_t globalSize, localSize;
+    globalSize = N_pad / 2;
+    localSize = 32;
+
+    // setup OpenCL stuff 
+    cl_int err;
+    err = clGetPlatformIDs(1, &platform, NULL);
+    err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 1, &device_id, NULL);
+    context = clCreateContext(0, 1, &device_id, NULL, NULL, &err);
+    queue = clCreateCommandQueue(context, device_id, 0, &err);
+    program = clCreateProgramWithSource(context, 1, (const char **) & kernelSource, NULL, &err);
+ 
+    // Build the program executable 
+    err = clBuildProgram(program, 0, NULL, NULL, NULL, NULL);
+    if (err != CL_SUCCESS) {
+        printf("building program failed\n");
+        if (err == CL_BUILD_PROGRAM_FAILURE) {
+            size_t log_size;
+            clGetProgramBuildInfo(program, device_id, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
+            char *log = (char *) malloc(log_size);
+            clGetProgramBuildInfo(program, device_id, CL_PROGRAM_BUILD_LOG, log_size, log, NULL);
+            printf("%s\n", log);
+        }
+    }
+    k_mult = clCreateKernel(program, "mult", &err);
+ 
+    // create arrays on host and write them
+    cl_mem d_v;
+    d_v = clCreateBuffer(context, CL_MEM_READ_WRITE, N_bytes, NULL, NULL);
+    err = clEnqueueWriteBuffer(queue, d_v, CL_TRUE, 0, N_bytes, h_v, 0, NULL, NULL);
+
+    // create forward plan and set its params
+    clfftCreateDefaultPlan(&planHandleForward, context, dim, clLengths);
+    clfftSetPlanPrecision(planHandleForward, CLFFT_DOUBLE);
+    clfftSetLayout(planHandleForward, CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED);
+    clfftSetResultLocation(planHandleForward, CLFFT_INPLACE);
+    clfftBakePlan(planHandleForward, 1, &queue, NULL, NULL);
+
+    // create backward plan and set its params
+    clfftCreateDefaultPlan(&planHandleBackward, context, dim, clLengths);
+    clfftSetPlanPrecision(planHandleBackward, CLFFT_DOUBLE);
+    clfftSetLayout(planHandleBackward, CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL);
+    clfftSetResultLocation(planHandleBackward, CLFFT_INPLACE);
+    clfftBakePlan(planHandleBackward, 1, &queue, NULL, NULL);
+
+    // set all of ze kernel args...
+    err  = clSetKernelArg(k_mult, 0, sizeof(cl_mem), &d_v);
+ 
+    // FFT data, apply psi, IFFT data
+    clfftEnqueueTransform(planHandleForward, CLFFT_FORWARD, 1, &queue, 0, NULL, NULL, &d_v, NULL, NULL);
+    clFinish(queue);
+ 
+     err = clEnqueueNDRangeKernel(queue, k_mult, 1, NULL, &globalSize, &localSize, 0, NULL, NULL);
+     clFinish(queue);
+
+    //clfftEnqueueTransform(planHandleBackward, CLFFT_BACKWARD, 1, &queue, 0, NULL, NULL, &d_v, NULL, NULL);
+    clFinish(queue);
+
+    // transfer back
+    clEnqueueReadBuffer(queue, d_v, CL_TRUE, 0, N_bytes, h_v, 0, NULL, NULL );
+    clFinish(queue);
+ 
+    printf("[  ");
+    for (i=0; i<N; i++)
+        printf("%f ", h_v[i]);
+    printf("]\n");
+
+    // release clFFT stuff
+    clfftDestroyPlan( &planHandleForward );
+    clfftDestroyPlan( &planHandleBackward );
+    clfftTeardown();
+ 
+    // release OpenCL resources
+    clReleaseMemObject(d_v);
+    clReleaseProgram(program);
+    clReleaseKernel(k_mult);
+    clReleaseCommandQueue(queue);
+    clReleaseContext(context);
+ 
+    //release host memory
+    free(h_v);
+ 
+    return 0;
+}

example05/main.py

@@ -0,0 +1,13 @@
+import numpy as np
+import scipy.fftpack as fft
+
+N = 128
+v = np.arange(N)
+
+v_fft = fft.fft(v)
+
+v_fft_altered = 2*v_fft.real + 4*v_fft.imag*1j
+
+v_final = fft.ifft(v_fft_altered)
+print v_final
+