229 lines
8.4 KiB
C++
229 lines
8.4 KiB
C++
#include <iostream>
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#include "CL/cl.hpp"
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#include <ctime>
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void compareResults (double CPUtime, double GPUtime, int trial) {
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double time_ratio = (CPUtime / GPUtime);
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std::cout << "VERSION " << trial << " -----------" << std::endl;
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std::cout << "CPU time: " << CPUtime << std::endl;
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std::cout << "GPU time: " << GPUtime << std::endl;
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std::cout << "GPU is ";
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if (time_ratio > 1)
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std::cout << time_ratio << " times faster!" << std::endl;
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else
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std::cout << (1/time_ratio) << " times slower :(" << std::endl;
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}
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double timeAddVectorsCPU(int n, int k) {
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// adds two vectors of size n, k times, returns total duration
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std::clock_t start;
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double duration;
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int A[n], B[n], C[n];
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for (int i=0; i<n; i++) {
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A[i] = i;
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B[i] = n-i;
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C[i] = 0;
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}
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start = std::clock();
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for (int i=0; i<k; i++) {
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for (int j=0; j<n; j++) {
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C[j] = A[j] + B[j];
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}
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}
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duration = (std::clock() - start) / (double) CLOCKS_PER_SEC;
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return duration;
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}
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int main() {
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// get all platforms (drivers), e.g. NVIDIA
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std::vector<cl::Platform> all_platforms;
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cl::Platform::get(&all_platforms);
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if (all_platforms.size()==0) {
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std::cout<<" No platforms found. Check OpenCL installation!\n";
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exit(1);
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}
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cl::Platform default_platform=all_platforms[0];
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// std::cout << "Using platform: "<<default_platform.getInfo<CL_PLATFORM_NAME>()<<"\n";
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// get default device (CPUs, GPUs) of the default platform
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std::vector<cl::Device> all_devices;
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default_platform.getDevices(CL_DEVICE_TYPE_ALL, &all_devices);
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if(all_devices.size()==0){
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std::cout<<" No devices found. Check OpenCL installation!\n";
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exit(1);
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}
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// use device[1] because that's a GPU; device[0] is the CPU
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cl::Device default_device=all_devices[1];
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// std::cout<< "Using device: "<<default_device.getInfo<CL_DEVICE_NAME>()<<"\n";
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cl::Context context({default_device});
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cl::Program::Sources sources;
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// calculates for each element; C = A + B
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std::string kernel_code=
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// is equivalent to the host's "time_add_vectors" function, except the
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// timing will be done on the host.
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" void kernel looped_add(global const int* v1, global const int* v2, global int* v3, "
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" global const int* constants) {"
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" int ID, Nthreads, n, k, ratio, start, stop;"
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" ID = get_global_id(0);"
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" Nthreads = get_global_size(0);"
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" n = constants[0];" // size of vectors
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" k = constants[1];" // number of loop iterations
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""
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" ratio = (n / Nthreads);" // elements per thread
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" start = ratio * ID;"
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" stop = ratio * (ID+1);"
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""
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" int i, j;" // will the compiler optimize this anyway? probably.
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" for (i=0; i<k; i++) {"
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" for (j=start; j<stop; j++)"
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" v3[j] = v1[j] + v2[j];"
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" }"
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" }"
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""
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" void kernel add(global const int* v1, global const int* v2, global int* v3, "
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" global const int* constants) {"
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" int ID, Nthreads, n, ratio, start, stop;"
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" ID = get_global_id(0);"
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" Nthreads = get_global_size(0);"
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" n = constants[0];"
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""
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" ratio = (n / Nthreads);"
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" start = ratio * ID;"
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" stop = ratio * (ID+1);"
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""
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" for (int i=start; i<stop; i++)"
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" v3[i] = v1[i] + v2[i];"
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" }"
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""
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" void kernel add_single(global const int* v1, global const int* v2, global int* v3, "
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" global const int* constants) { "
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" int k = constants[1];"
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" int ID = get_global_id(0);"
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" for (int i=0; i<k; i++)"
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" v3[ID] = v1[ID] + v2[ID];"
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" }";
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sources.push_back({kernel_code.c_str(), kernel_code.length()});
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cl::Program program(context, sources);
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if (program.build({default_device}) != CL_SUCCESS) {
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std::cout << "Error building: " << program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(default_device) << std::endl;
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exit(1);
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}
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int n, k, Nthreads;
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n = 128000; // size of vectors
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k = 1000; // number of loop iterations
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Nthreads = 128;
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int constants[2] = {n, k};
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// run the CPU code
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float CPUtime = timeAddVectorsCPU(n, k);
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// run some GPU code; this block allocates space, writes buffers, and then
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// adds the same two vectors multiple times -- i.e. it's equivalent to the
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// host (CPU) code above, but with some necessary overhead.
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cl::CommandQueue queue(context, default_device);
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cl::KernelFunctor add(cl::Kernel(program, "add"), queue, cl::NullRange, cl::NDRange(Nthreads), cl::NullRange);
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cl::Kernel looped_add = cl::Kernel(program, "looped_add");
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cl::Kernel add_single = cl::Kernel(program, "add_single");
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// construct vectors
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int A[n], B[n], C[n];
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for (int i=0; i<n; i++) {
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A[i] = i;
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B[i] = n - i - 1;
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C[i] = 0;
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}
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// start timer
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double GPUtime1;
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std::clock_t start_time;
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start_time = std::clock();
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// allocate space
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cl::Buffer buffer_A(context, CL_MEM_READ_WRITE, sizeof(int) * n);
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cl::Buffer buffer_B(context, CL_MEM_READ_WRITE, sizeof(int) * n);
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cl::Buffer buffer_C(context, CL_MEM_READ_WRITE, sizeof(int) * n);
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cl::Buffer buffer_constants(context, CL_MEM_READ_ONLY, sizeof(int) * 2);
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// push write commands to queue
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queue.enqueueWriteBuffer(buffer_A, CL_TRUE, 0, sizeof(int)*n, A);
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queue.enqueueWriteBuffer(buffer_B, CL_TRUE, 0, sizeof(int)*n, B);
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queue.enqueueWriteBuffer(buffer_constants, CL_TRUE, 0, sizeof(int)*2, constants);
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// RUN ZE KERNEL
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looped_add.setArg(0, buffer_A);
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looped_add.setArg(1, buffer_B);
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looped_add.setArg(2, buffer_C);
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looped_add.setArg(3, buffer_constants);
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queue.enqueueNDRangeKernel(looped_add, cl::NullRange, 32, cl::NullRange);
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// read result from GPU to here; including for the sake of timing
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queue.enqueueReadBuffer(buffer_C, CL_TRUE, 0, sizeof(int)*n, C);
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GPUtime1 = (std::clock() - start_time) / (double) CLOCKS_PER_SEC;
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// do the same thing, except copy the arrays over every iteration
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double GPUtime2;
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start_time = std::clock();
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cl::Buffer buffer_A2(context, CL_MEM_READ_WRITE, sizeof(int)*n);
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cl::Buffer buffer_B2(context, CL_MEM_READ_WRITE, sizeof(int)*n);
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cl::Buffer buffer_C2(context, CL_MEM_READ_WRITE, sizeof(int)*n);
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cl::Buffer buffer_constants2(context, CL_MEM_READ_ONLY, sizeof(int)*2);
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for (int i=0; i<k; i++) {
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queue.enqueueWriteBuffer(buffer_A2, CL_TRUE, 0, sizeof(int)*n, A);
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queue.enqueueWriteBuffer(buffer_B2, CL_TRUE, 0, sizeof(int)*n, B);
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queue.enqueueWriteBuffer(buffer_constants2, CL_TRUE, 0, sizeof(int)*2, constants);
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add(buffer_A2, buffer_B2, buffer_C2, buffer_constants2);
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}
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queue.enqueueReadBuffer(buffer_C2, CL_TRUE, 0, sizeof(int)*n, C);
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GPUtime2 = (std::clock() - start_time) / (double) CLOCKS_PER_SEC;
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// similar to the first trial, except that each element is done
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// by one thread, instead of multiple elements per thread
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// start timer
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double GPUtime3;
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start_time = std::clock();
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cl::Buffer buffer_A3(context, CL_MEM_READ_WRITE, sizeof(int)*n);
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cl::Buffer buffer_B3(context, CL_MEM_READ_WRITE, sizeof(int)*n);
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cl::Buffer buffer_C3(context, CL_MEM_READ_WRITE, sizeof(int)*n);
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cl::Buffer buffer_constants3(context, CL_MEM_READ_ONLY, sizeof(int)*2);
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queue.enqueueWriteBuffer(buffer_A3, CL_TRUE, 0, sizeof(int)*n, A);
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queue.enqueueWriteBuffer(buffer_B3, CL_TRUE, 0, sizeof(int)*n, B);
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queue.enqueueWriteBuffer(buffer_constants3, CL_TRUE, 0, sizeof(int)*2, constants);
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// run it
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add_single.setArg(0, buffer_A3);
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add_single.setArg(1, buffer_B3);
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add_single.setArg(2, buffer_C3);
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add_single.setArg(3, buffer_constants3);
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queue.enqueueNDRangeKernel(add_single, cl::NDRange(n), cl::NDRange(32), cl::NullRange);
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// end timer
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queue.enqueueReadBuffer(buffer_C3, CL_TRUE, 0, sizeof(int)*n, C);
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GPUtime3 = (std::clock() - start_time) / (double) CLOCKS_PER_SEC;
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// let's compare!
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compareResults(CPUtime, GPUtime1, 1);
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compareResults(CPUtime, GPUtime2, 2);
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compareResults(CPUtime, GPUtime3, 3);
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return 0;
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}
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