mirror of
https://github.com/Steffo99/unimore-hpc-assignments.git
synced 2024-11-23 00:24:23 +00:00
f919c9f9ce
it's not like anybody but me reads the `git log` anyways
or does someone? in that case, hello, whoever you are! 👋
313 lines
6.6 KiB
Text
313 lines
6.6 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 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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// Enable syntax highlighting for the CUDA mode
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// TODO: Remove this, as it will be set by .bench.sh
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#define HPC_USE_CUDA
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// Enable syntax highlighting for the stride mode
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// TODO: Remove this, as it will be set by .bench.sh
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#define HPC_USE_STRIDE
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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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/* X = [ 3.14, 6.28, 9.42, ... ] */
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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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/* Y = [ 0.00, 0.00, 0.00, ... ] */
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for (unsigned int x = 0; x < NY; x++)
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{
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Y[x] = 0;
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}
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/*
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* A = [
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* [ 0, 0, 0, 0, ... ],
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* [ 1 / NX, 2 / NX, 3 / NX, 4 / NX, ... ],
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* [ 2 / NX, 4 / NX, 6 / NX, 8 / NX, ... ],
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* [ 3 / NX, 6 / NX, 9 / NX, 12 / NX, ... ],
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* ...
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* ]
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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[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 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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* 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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* 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;
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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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// 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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int iterationIdx = blockThreadIdx * stride;
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// Prevent the thread from accessing unallocated memory
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if(iterationIdx < NY) {
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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;
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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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// 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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int iterationIdx = blockThreadIdx * stride;
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// Prevent the thread from accessing unallocated memory
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if(iterationIdx < NX) {
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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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}
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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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__host__ static void print_array(DATA_TYPE* Y)
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{
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for (unsigned int x = 0; x < NX; x++)
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{
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fprintf(stderr, DATA_PRINTF_MODIFIER, Y[x]);
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}
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fprintf(stderr, "\n");
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}
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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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* Currently to be called on the CPU (uses the `__host__` qualifier), but we may probably want to change that soon.
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*/
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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 < NX; x++)
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{
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DATA_TYPE tmp = 0;
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for (unsigned int y = 0; y < NY; y++)
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{
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tmp += A[x][y] * X[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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Y[y] += A[x][y] * tmp;
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}
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}
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}
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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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#ifndef HPC_USE_CUDA
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// A[NX][NY]
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DATA_TYPE** A = new DATA_TYPE*[NX] {};
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for(unsigned int x = 0; x < NX; x++)
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{
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A[x] = new DATA_TYPE[NY] {};
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}
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// X[NY]
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DATA_TYPE* X = new DATA_TYPE[NY] {};
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// Y[NX]
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DATA_TYPE* Y = new DATA_TYPE[NX] {};
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#ifdef HPC_INCLUDE_INIT
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polybench_start_instruments;
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#endif
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init_array(A, X, Y);
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#ifndef HPC_INCLUDE_INIT
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polybench_start_instruments;
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#endif
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kernel_atax(A, X, Y);
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polybench_stop_instruments;
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polybench_print_instruments;
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polybench_prevent_dce(
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print_array(Y)
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);
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#else
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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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if(cudaMalloc(&A, sizeof(DATA_TYPE) * NX * NY))
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{
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std::cerr << "Could not allocate A on the device\n";
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return 1;
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}
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if(cudaMalloc(&X, sizeof(DATA_TYPE) * NY))
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{
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std::cerr << "Could not allocate X on the device\n";
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return 1;
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}
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if(cudaMalloc(&Y, sizeof(DATA_TYPE) * NX))
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{
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std::cerr << "Could not allocate Y on the device\n";
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return 1;
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}
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#ifdef POLYBENCH_INCLUDE_INIT
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polybench_start_instruments;
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#endif
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init_array_cuda<<<1, 1>>>(A, X, Y);
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#ifndef POLYBENCH_INCLUDE_INIT
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polybench_start_instruments;
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#endif
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// kernel_atax_cuda<<<1, 1>>>();
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polybench_stop_instruments;
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polybench_print_instruments;
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// Y = cudaMemcpy();
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/*
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polybench_prevent_dce(
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print_array(Y)
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);
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*/
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#endif
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return 0;
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}
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