mirror of
https://github.com/Steffo99/unimore-hpc-assignments.git
synced 2024-11-24 09:04:23 +00:00
482 lines
12 KiB
Text
482 lines
12 KiB
Text
#include <stdio.h>
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#include <unistd.h>
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#include <string.h>
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#include <math.h>
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#include <iostream>
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#include <string>
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/* Include polybench common header. */
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#include "polybench.hu"
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/* Include benchmark-specific header. */
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/* Default data type is double, default size is 4000. */
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#include "atax.hu"
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// Workaround for the editor not finding M_PI
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// It is exclusive to the GNU C compiler
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// https://www.gnu.org/software/libc/manual/html_node/Mathematical-Constants.html
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#ifndef M_PI
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#define M_PI 3.141
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#endif
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// Default if CUDA_NTHREADS is not set
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#ifndef CUDA_NTHREADS
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#define CUDA_NTHREADS 128
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#endif
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/**
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* Given a `x` and a `y`, compute the relative index of the element in the `A` matrix.
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*/
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__host__ __device__ inline static unsigned int a_index(unsigned int x, unsigned int y) {
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return x * NY + y;
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}
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/**
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* Log a debug message.
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*/
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__host__ inline static void print_debug(std::string txt) {
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#ifdef HPC_DEBUG
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std::cerr << txt << std::endl;
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#endif
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}
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/**
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* Log an error message.
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*/
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#ifdef HPC_USE_CUDA
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__host__ inline static void print_cudaError(cudaError_t err, std::string txt) {
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#ifdef HPC_DEBUG
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std::cerr << txt;
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fprintf( stderr, ": error in file '%s' in line %i: %s.\n", __FILE__, __LINE__, cudaGetErrorString(err) );
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#endif
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}
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#endif
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/**
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* Initialize the arrays to be used in the computation:
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*
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* - `X` is filled with multiples of `M_PI`;
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* - `Y` is zeroed;
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* - `A` is filled with sample data.
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*
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* To be called on the CPU (uses the `__host__` qualifier).
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*/
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#ifndef HPC_USE_CUDA
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__host__ static void init_array(DATA_TYPE* A, DATA_TYPE* X, DATA_TYPE* Y)
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{
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for (unsigned int y = 0; y < NY; y++)
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{
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X[y] = y * M_PI;
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}
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for (unsigned int x = 0; x < NX; x++)
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{
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Y[x] = 0;
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}
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for (unsigned int x = 0; x < NX; x++)
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{
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for (unsigned int y = 0; y < NY; y++)
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{
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A[a_index(x, y)] = (DATA_TYPE)(x * (y + 1)) / NX;
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}
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}
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}
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#endif
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/**
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* Initialize the `X` array.
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*
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* Runs on the device.
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*/
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#ifdef HPC_USE_CUDA
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__device__ static void init_array_cuda_x(DATA_TYPE* X, unsigned int threads)
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{
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// Find how many iterations should be performed by each thread
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unsigned int perThread = NY / threads + 1;
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// Find the index of the current thread, even if threads span multiple blocks
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int blockThreadIdx = blockIdx.x * blockDim.x + threadIdx.x;
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// Have each thread perform the previously determined number of iterations
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for(int stride = 0; stride < perThread; stride++)
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{
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// Find the index of the current iteration
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// This is equal to `y` of the init_array function
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unsigned int iterationIdx = threads * stride + blockThreadIdx;
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// Prevent the thread from accessing unallocated memory
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if(iterationIdx < NY)
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{
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// Set the array element
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X[iterationIdx] = iterationIdx * M_PI;
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}
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}
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}
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#endif
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/**
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* Initialize the `Y` array.
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*
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* Runs on the device.
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*/
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#ifdef HPC_USE_CUDA
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__device__ static void init_array_cuda_y(DATA_TYPE* Y, unsigned int threads)
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{
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// Find how many iterations should be performed by each thread
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unsigned int perThread = NX / threads + 1;
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// Find the index of the current thread, even if threads span multiple blocks
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int blockThreadIdx = blockIdx.x * blockDim.x + threadIdx.x;
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// Have each thread perform the previously determined number of iterations
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for(int stride = 0; stride < perThread; stride++)
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{
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// Find the index of the current iteration
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// This is equal to `y` of the init_array function
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unsigned int iterationIdx = threads * stride + blockThreadIdx;
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// Prevent the thread from accessing unallocated memory
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if(iterationIdx < NX)
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{
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// Set the array element
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Y[iterationIdx] = 0;
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}
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}
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}
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#endif
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/**
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* Initialize the `A` array.
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*
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* Runs on the device.
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*/
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#ifdef HPC_USE_CUDA
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__device__ static void init_array_cuda_a(DATA_TYPE* A, unsigned int threads)
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{
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// Find how many elements should be written in total
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unsigned int elements = NX * NY;
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// Find how many iterations should be performed by each thread
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unsigned int perThread = elements / threads + 1;
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// Find the index of the current thread, even if threads span multiple blocks
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int blockThreadIdx = blockIdx.x * blockDim.x + threadIdx.x;
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// Have each thread perform the previously determined number of iterations
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for(int stride = 0; stride < perThread; stride++)
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{
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// Find the index of the current iteration
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// This is equal to `y` of the init_array function
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unsigned int iterationIdx = threads * stride + blockThreadIdx;
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// Determine current x and y
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unsigned int y = iterationIdx % NY;
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unsigned int x = iterationIdx / NY;
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// Prevent the thread from accessing unallocated memory
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if(iterationIdx < elements)
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{
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// Set the array element
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A[iterationIdx] = (DATA_TYPE)(x * (y + 1)) / NX;
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}
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}
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}
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#endif
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/**
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* Initialize the arrays to be used in the computation:
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*
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* - `X` is filled with multiples of `M_PI`;
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* - `Y` is zeroed;
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* - `A` is filled with sample data.
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*
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* Beware that `A` here is a simple array, it is not a matrix, so elements are accessed via [y * NX + x] (I think?).
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*
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* It is called by the host, runs on the device, and calls the other init_arrays on the device.
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*/
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#ifdef HPC_USE_CUDA
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__global__ static void init_array_cuda(DATA_TYPE* A, DATA_TYPE* X, DATA_TYPE* Y)
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{
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unsigned int threads = gridDim.x * blockDim.x;
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init_array_cuda_x(X, threads);
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init_array_cuda_y(Y, threads);
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init_array_cuda_a(A, threads);
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}
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#endif
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/**
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* Print the given array.
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*
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* Cannot be parallelized, as the elements of the array should be
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*
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* To be called on the CPU (uses the `__host__` qualifier).
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*/
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#ifdef HPC_DEBUG
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__host__ static void print_array(DATA_TYPE* Z, unsigned int size)
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{
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for (unsigned int z = 0; z < size; z++)
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{
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fprintf(stderr, DATA_PRINTF_MODIFIER, Z[z]);
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}
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fprintf(stderr, "\n");
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}
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#endif
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/**
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* Compute ATAX :
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* - A is the input matrix
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* - X is an input vector
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* - Y is the result vector
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*
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* In particular:
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* ```
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* A * (A * X) = Y
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* ```
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* Wait, there's no transposition here?!?
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*
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* Parallelizing this is the goal of the assignment.
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*
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* To be called on the CPU uses the `__host__` qualifier otherwise
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* for the GPU uses the `__global__` qualifier.
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*/
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#ifndef HPC_USE_CUDA
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__host__ static void kernel_atax(DATA_TYPE* A, DATA_TYPE* X, DATA_TYPE* Y)
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{
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for (unsigned int x = 0; x < NY; x++)
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{
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DATA_TYPE tmp = 0;
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for (unsigned int y = 0; y < NX; y++)
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{
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tmp += A[a_index(x, y)] * X[y];
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}
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for (unsigned int y = 0; y < NX; y++)
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{
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Y[x] += A[a_index(x, y)] * tmp;
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}
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}
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}
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#else
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__global__ static void kernel_atax_cuda(DATA_TYPE* A, DATA_TYPE* X, DATA_TYPE* Y)
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{
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// Find out how many threads there are
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unsigned int threads = gridDim.x * blockDim.x;
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// Find how many iterations should be performed by each thread
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unsigned int perThread = NX / threads + 1;
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// Find the index of the current thread, even if threads span multiple blocks
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unsigned int blockThreadIdx = blockIdx.x * blockDim.x + threadIdx.x;
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// Have each thread perform the previously determined number of iterations
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for(int stride = 0; stride < perThread; stride++)
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{
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// Iterate over x; y is not parallelized
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unsigned int x = threads * stride + blockThreadIdx;
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// Prevent the thread from accessing unallocated memory
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if(x < NX)
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{
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// The same tmp as earlier
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DATA_TYPE tmp = 0;
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for (unsigned int y = 0; y < NX; y++)
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{
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tmp += A[a_index(x, y)] * X[y];
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}
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for (unsigned int y = 0; y < NX; y++)
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{
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// THIS DOES NOT WORK ON THE NANO, AS IT IS TOO OLD TO SUPPORT ATOMIC ADDITION WITH DOUBLES!
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// If you want to use the Nano, swap this for something else, or change atax.hu to use float instead of double
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atomicAdd(&Y[x], A[a_index(x, y)] * tmp);
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}
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}
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}
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}
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#endif
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/**
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* The main function of the benchmark, which sets up tooling to measure the time spent computing `kernel_atax`.
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*
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* We should probably avoid editing this.
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*/
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__host__ int main(int argc, char** argv)
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{
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print_debug("[Main] Starting...");
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std::cerr << "[Main] NX is: " << NX << std::endl;
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std::cerr << "[Main] NY is: " << NY << std::endl;
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#ifndef HPC_USE_CUDA
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print_debug("[Mode] Host-only");
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print_debug("[Pointers] Allocating...");
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DATA_TYPE* A = new DATA_TYPE[NX * NY];
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DATA_TYPE* X = new DATA_TYPE[NY];
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volatile DATA_TYPE* Y = new DATA_TYPE[NX];
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print_debug("[Pointers] Allocated!");
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#ifdef HPC_INCLUDE_INIT
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print_debug("[Benchmark] Starting...");
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polybench_start_instruments;
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#endif
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print_debug("[Init] Initializing...");
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init_array(A, X, (DATA_TYPE*) Y);
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print_debug("[Init] Initialized!");
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#ifndef HPC_INCLUDE_INIT
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print_debug("[Benchmark] Starting...");
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polybench_start_instruments;
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#endif
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print_debug("[Kernel] Running...");
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kernel_atax(A, X, (DATA_TYPE*) Y);
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print_debug("[Kernel] Completed!");
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print_debug("[Benchmark] Stopping...");
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polybench_stop_instruments;
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polybench_print_instruments;
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print_debug("[Benchmark] Complete!");
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#ifdef HPC_DEBUG
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print_debug("[Debug] Displaying A:");
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print_array(A, NX * NY);
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print_debug("[Debug] Displaying X:");
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print_array(X, NY);
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print_debug("[Debug] Displaying Y:");
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print_array(Y, NX);
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#endif
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#else
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print_debug("[Mode] Host-and-device, CUDA");
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print_debug("[Pointers] Allocating...");
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DATA_TYPE* A;
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DATA_TYPE* X;
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DATA_TYPE* Y;
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#ifdef HPC_DEBUG
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DATA_TYPE* host_A = new DATA_TYPE[NX * NY];
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DATA_TYPE* host_X = new DATA_TYPE[NY];
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#endif
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volatile DATA_TYPE* host_Y = new DATA_TYPE[NX];
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print_debug("[CUDA] Allocating A...");
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if(cudaError_t err = cudaMalloc((void**)&A, sizeof(DATA_TYPE) * NX * NY))
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{
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print_cudaError(err, "[CUDA] Could not allocate A!");
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return 1;
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}
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print_debug("[CUDA] Allocated A!");
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print_debug("[CUDA] Allocating X...");
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if(cudaError_t err = cudaMalloc((void**)&X, sizeof(DATA_TYPE) * NY))
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{
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print_cudaError(err, "[CUDA] Could not allocate X!");
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return 1;
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}
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print_debug("[CUDA] Allocated X!");
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print_debug("[CUDA] Allocating Y...");
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if(cudaError_t err = cudaMalloc((void**)&Y, sizeof(DATA_TYPE) * NX))
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{
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print_cudaError(err, "[CUDA] Could not allocate Y!");
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return 1;
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}
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print_debug("[CUDA] Allocated Y!");
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#ifdef HPC_INCLUDE_INIT
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print_debug("[Benchmark] Starting...");
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polybench_start_instruments;
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#endif
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print_debug("[Init] Initializing...");
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init_array_cuda<<<32, 32>>>((DATA_TYPE*) A, (DATA_TYPE*) X, (DATA_TYPE*) Y);
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if(cudaError_t err = cudaGetLastError())
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{
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print_cudaError(err, "[Init] Failed to execute kernel!");
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return 1;
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}
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print_debug("[Init] Complete!");
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#ifndef HPC_INCLUDE_INIT
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print_debug("[Benchmark] Starting...");
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polybench_start_instruments;
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#endif
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print_debug("[Kernel] Running...");
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kernel_atax_cuda<<<32, 32>>>((DATA_TYPE*) A, (DATA_TYPE*) X, (DATA_TYPE*) Y);
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print_debug("[Kernel] Complete!");
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#ifdef HPC_DEBUG
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print_debug("[CUDA] Copying A back...");
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if(cudaError_t err = cudaMemcpy(host_A, A, sizeof(DATA_TYPE) * NX * NY, cudaMemcpyDeviceToHost)) {
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print_cudaError(err, "[CUDA] Could copy A back!");
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return 1;
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};
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print_debug("[CUDA] Copied A back!");
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print_debug("[CUDA] Copying X back...");
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if(cudaError_t err = cudaMemcpy(host_X, X, sizeof(DATA_TYPE) * NY, cudaMemcpyDeviceToHost)) {
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print_cudaError(err, "[CUDA] Could copy X back!");
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return 1;
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};
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print_debug("[CUDA] Copied X back!");
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#endif
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print_debug("[CUDA] Copying Y back...");
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if(cudaError_t err = cudaMemcpy((void*) host_Y, Y, sizeof(DATA_TYPE) * NX, cudaMemcpyDeviceToHost)) {
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print_cudaError(err, "[CUDA] Could copy Y back!");
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return 1;
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};
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print_debug("[CUDA] Copied Y back!");
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print_debug("[Benchmark] Stopping...");
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polybench_stop_instruments;
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polybench_print_instruments;
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print_debug("[Benchmark] Complete!");
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print_debug("[CUDA] Freeing A...");
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if(cudaError_t err = cudaFree(A)) {
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print_cudaError(err, "[CUDA] Could not free A!");
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return 1;
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}
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print_debug("[CUDA] Freed A!");
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print_debug("[CUDA] Freeing X...");
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if(cudaError_t err = cudaFree(X)) {
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print_cudaError(err, "[CUDA] Could not free X!");
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return 1;
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}
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print_debug("[CUDA] Freed X!");
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print_debug("[CUDA] Freeing Y...");
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if(cudaError_t err = cudaFree(Y)) {
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print_cudaError(err, "[CUDA] Could not free Y!");
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return 1;
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}
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print_debug("[CUDA] Freed Y!");
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#ifdef HPC_DEBUG
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print_debug("[Debug] Displaying A:");
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print_array(host_A, NX * NY);
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print_debug("[Debug] Displaying X:");
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print_array(host_X, NY);
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print_debug("[Debug] Displaying Y:");
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print_array((double*) host_Y, NX);
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#endif
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#endif
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return 0;
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}
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