41
edits
Changes
m
CODE FOR 1D BLOCK OF 1D THREADS WITH CONSTANT MEMORYA NOTE ON SCALABILITY:
#include FINAL TIMINGS <pre style="cuda_runtime.h"#include "device_launch_parameters.hcolor: red"> THE GRAPH IMMEDIATELY BELOW IS INCORRECT: there was an error recording the 1D runtimes for assignment 2</pre>
#include <stdio.hnowiki>****************</nowiki>
// Load standard libraries#include <cstdio>#include <cstdlib>#include <iostream>#include <fstream>#include <cmath>#include <chrono>using namespace std;<pre style="color[[File: blue">__device__ __constant__ int d_m;__device__ __constant__ int d_n;__device__ __constant__ float d_xmin;__device__ __constant__ float d_xmax;__device__ __constant__ float d_ymin;__device__ __constant__ float d_ymax;__device__ __constant__ float d_pi;__device__ __constant__ float d_omega;__device__ __constant__ float d_dx;__device__ __constant__ float d_dy;__device__ __constant__ float d_dxxinv;__device__ __constant__ float d_dyyinv;__device__ __constant__ float d_dcent;</pre>__global__ void matrixKernel(float* a, float* b){//, float dcent, int n, int m, int xmin, int xmax, //int ymin, int ymax, float dx, float dy, float dxxinv, float dyyinv) {//MODIFY to suit algorithmdps915_gpusquad_a3chart.png]]
int j = threadIdx.x + 1; //above: we are using block and thread indexes to replace some of the iteration logic if (j < d_n - 1) { for (int i = 1; i < d_m - 1; i++) { int ij = i + d_m*j; float x = d_xmin + i*d_dx, y = d_ymin + j*d_dy; float input = abs(x) > 0.5 || abs(y) nowiki> 0.5 ? 0 : 1; a[ij] = (input + d_dxxinv*(b[ij - 1] + b[ij + 1]) + d_dyyinv*(b[ij - d_m] + b[ij + d_m]))*d_dcent; } } } // Set grid size and number of iterationsconst int save_iters = 20;const int total_iters = 5000;const int error_every = 2;const int m = 32, n = 1024;const float xmin = -1, xmax = 1;const float ymin = -1, ymax = 1; // Compute useful constantsconst float pi = 3.1415926535897932384626433832795;const float omega = 2 / (1 + sin(2 * pi / n));const float dx = (xmax - xmin) / (m - 1);const float dy = (ymax - ymin) / (n - 1);const float dxxinv = 1 / (dx*dx);const float dyyinv = 1 / (dy*dy);const float dcent = 1 / (2 * (dxxinv + dyyinv)); // Input functioninline float f(int i, int j) { float x = xmin + i*dx, y = ymin + j*dy; return abs(x) > 0.5 || abs(y) > 0.5 ? 0 : 1;} // Common output and error routinevoid outputImage(char* filename, float *a) { // Computes the error if sn%error every==0 // Saves the matrix if sn<=save iters int i, j, ij = 0, ds = sizeof(float); float x, y, data_float; const char *pfloat; pfloat = (const char*)&data_float; ofstream outfile; static char fname[256]; sprintf(fname, "%s.%d", filename, 101); outfile.open(fname, fstream::out | fstream::trunc | fstream::binary); data_float = m; outfile.write(pfloat, ds); for (i = 0; i < m; i++) { x = xmin + i*dx; data_float = x; outfile.write(pfloat, ds); } for (j = 0; j < n; j++) { y = ymin + j*dy; data_float = y; outfile.write(pfloat, ds); for (i = 0; i < m; i++) { data_float = a[ij++]; outfile.write(pfloat, ds); } } outfile.close();} void dojacobi() { int i, j, ij, k; float error, z; float *a, *b; float *u; float *v; u = new float[m*n]; v = new float[m*n]; // Set initial guess to be identically zero for (ij = 0; ij < m*n; ij++) u[ij] = v[ij] = 0; a = v; b = u; float* d_a; float* d_b; //malloc cudaMalloc((void**)&d_a, m*n * sizeof(float)); cudaMalloc((void**)&d_b, m*n * sizeof(float)); cudaMemcpy(d_a, a, n* m * sizeof(float), cudaMemcpyHostToDevice); cudaMemcpy(d_b, b, n* m * sizeof(float), cudaMemcpyHostToDevice);nowiki>
int nblocks = n / 1024; dim3 dGrid(nblocks); dim3 dBlock(1024);PROPER TIMINGS:
// Carry out Jacobi iterations for (k = 1; k <= total_iters; k++) { if (k % 2 == 0) { cudaError_t error = cudaGetLastError();[[File:Code_timings2.png]]
matrixKernel << <dGrid, dBlock >> > Blue = Serial (d_a, d_bA1);// , dcent, n, m, xmin, xmax, ymin, ymax, dx, dy, dxxinv, dyyinvOrange = Parallel (A2)Grey = Optimized Global (A3)Yellow = Optimized Shared (A3);
cudaDeviceSynchronize(); if (cudaGetLastError()) { std::cout << "error"; } }The above graph includes the total run times for the serial code, the 1D kernel from assignment 2, a kernel with global and constant memory with a 2D thread arrangement, and the same 2D arrangement but with shared memory utilizing ghost cells.
else { cudaError_t error = cudaGetLastError();We found that the most efficient version of the code was the 2d version that used constant memory and did not use shared memory. Because the shared memory version of the kernel required synchronization of threads to allocate shared memory every time a kernel was run, and a kernel was run 5000 times for each version of our code, this increased overhead for memory setup actually made the execution slower than the version with global memory.
matrixKernel << <dGridWe found that the most efficient version of the code was the 2d version that used constant memory and did not use shared memory. Because the shared memory version of the kernel required synchronization of threads to allocate shared memory every time a kernel was run, dBlock >> > (d_band a kernel was run 5000 times for each version of our code, d_a);// the if statements required to set up the ghost cells for shared memory may have created a certain amount of warp divergence, dcent, n, m, xmin, xmax, ymin, ymax, dx, dy, dxxinv, dyyinv);thus slowing down the runtimes of each individual kernel.
cudaDeviceSynchronize(); if (cudaGetLastError()) { std::cout << "error"; } } }Below, are two images that show 4 consecutive kernel runs for both global and shared versions of the code. It is apparent that shared kernel runs actually take more time than the global memory versions.
cudaMemcpy(a, d_a, n* m * sizeof(float), cudaMemcpyDeviceToHost);
outputImage("jacobi out", a);
cudaFree(d_a);
cudaFree(d_b);
delete[] u;
delete[] v;
}
int main() {TIMES FOR THE GLOBAL KERNEL[[File:kernelGlobalTimes.png]]
cudaMemcpyToSymbol(&d_n, &n, sizeof(int));
cudaMemcpyToSymbol(&d_xmin, &xmin, sizeof(float));
cudaMemcpyToSymbol(&d_xmax, &xmax, sizeof(float));
cudaMemcpyToSymbol(&d_ymin, &ymin, sizeof(float));
cudaMemcpyToSymbol(&d_ymax, &ymax, sizeof(float));
cudaMemcpyToSymbol(&d_pi, &pi, sizeof(float));
cudaMemcpyToSymbol(&d_omega, &pi, sizeof(float));
cudaMemcpyToSymbol(&d_dx, &dx, sizeof(float));
cudaMemcpyToSymbol(&d_dy, &dy, sizeof(float));
std[[File::chrono::steady_clock::time_point ts, te; ts = std::chrono::steady_clock::now(); dojacobi(); te = std::chrono::steady_clock::now(); std::chrono::steady_clock::duration duration = te - ts; auto ms = std::chrono::duration_cast<std::chrono::milliseconds>(duration); std::cout << "Parallel Code Time: " << mssharedKernelTimes.count() << " ms" << std::endl; cudaDeviceReset();png]]
return 0;}Note how the run times for each kernel with shared memory are significantly longer than those with global.
<nowiki>****************</nowiki>To try to determine if this issue was one of warp divergence, we tried to time a kernel with global memory that also initialized shared memory, although referenced global memory when carrying out the actual calculations:
FINAL TIMINGS <pre style="color[[File: red"> THE GRAPH IMMEDIATELY BELOW IS INCORRECT: there was an error recording the 1D runtimes for assignment 2</pre>GlobalInitSharedKernelTimes.png]]
<nowiki>****************</nowiki>The run of a kernel that allocated shared memory using a series of if statements, but executed instructions using global memory is shown in the figure above. While slightly longer than the run with global memory where shared memory is not initialized for ghost cells, it still takes less time to run than the version with Global memory. It is likely that Our group's attempts to employ shared memory failed because we did not adequately schedule or partition the shared memory, and the kernel was slowed as a result. The supposed occupancy of a block of shared memory was 34x32 (the dimensions of the shared memory matrix) x 4 (the size of a float) which equals 4,352 bytes per block, which is supposedly less than the maximum of about 49KB stated for a device with a 5.0 compute capability (which this series of tests on individual kernel run times was performed on). With this is mind it is still unclear as to why the shared memory performed more poorly that the global memory implementation.
[[File:dps915_gpusquad_a3chartUnfortunately our group's inability to effectively use profiling tools has left this discrepancy as a mystery.png]] <nowiki>****************</nowiki>
[[File:Code_timingsIn conclusion, while it may be possible to parallelize the algorithm we chose well, the effort to do so would involve ensuring that shared memory is properly synchronized in two block dimensions (2 dimensions of ghost cells rather than the 1 we implemented), and to ensure that shared memory is allocated appropriately such that maximum occupancy is established within the GPU. Unfortunately, our attempts fell short, and while implementing constant memory seemed to speed up the kernel a bit, our solution was not fully scalable in both dimensions, and shared memory was not implemented in a way that improved kernel efficiency.png]]
→Assignment 3
int ymin, int ymax, float dx, float dy, float dxxinv, float dyyinv) {//MODIFY to suit algorithm
int j = blockIdxblockDim.x * blockDimblockIdx.x + threadIdx.x + 1;
//above: we are using block and thread indexes to replace some of the iteration logic
<nowiki>****************</nowiki>
In our attempts to make the kernel scalable with ghost cells, we tried just replacing scaled along one dimension. However, we were inconsistent in our scaling. The 1D kernel scaled along the n (y) dimension while the 2d kernels scaled along the m (x) dimension. Scaling along the global memory values that were used as calculation constants with constant memoryx dimension, since these values would not have while allowing results to be modified by kernel logictestable between serial and 2D parallelized versions of the code, produced distributions that were strangely banded and skewed.In other words, we made the code render weird things faster:
[[File:MDimensionScale.png]]
TIMES FOR THE SHARED KERNEL
