CUDA

Syed Asad Zaman

BS Computer Science

FAST National University of Computing & Emerging Sciences

Parallel & Distrubted Computing

Abstract

CPU

  • small no. of cores
  • thread creation expensive
  • no more than 12 Threads

GPU

  • large no. of cores
  • 768 CUDA Cores
  • used for graphics rendering mostly
  • Divide & Conquer approach

Question Arises?

How to do general-purpose tasks on GPU?

Answer

CUDA Programming

Architecture

Requirements

  • Nivida GPU
  • CUDA
  • pyCUDA

Installation

sudo apt install nvidia-cuda-toolkit
 

Sample Code

#include <stdio.h>

__global__
void saxpy(int n, float a, float *x, float *y)
{
  int i = blockIdx.x*blockDim.x + threadIdx.x;
  if (i < n) y[i] = a*x[i] + y[i];
}

int main(void)
{
  int N = 1<<20;
  float *x, *y, *d_x, *d_y;
  x = (float*)malloc(N*sizeof(float));
  y = (float*)malloc(N*sizeof(float));

  cudaMalloc(&d_x, N*sizeof(float)); 
  cudaMalloc(&d_y, N*sizeof(float));

  for (int i = 0; i < N; i++) {
    x[i] = 1.0f;
    y[i] = 2.0f;
  }

  cudaMemcpy(d_x, x, N*sizeof(float), cudaMemcpyHostToDevice);
  cudaMemcpy(d_y, y, N*sizeof(float), cudaMemcpyHostToDevice);

  // Perform SAXPY on 1M elements
  saxpy<<<(N+255)/256, 256>>>(N, 2.0f, d_x, d_y);

  cudaMemcpy(y, d_y, N*sizeof(float), cudaMemcpyDeviceToHost);

  float maxError = 0.0f;
  for (int i = 0; i < N; i++)
    maxError = max(maxError, abs(y[i]-4.0f));
  printf("Max error: %f\n", maxError);

  cudaFree(d_x);
  cudaFree(d_y);
  free(x);
  free(y);
}
nvcc -o hello hello.cu && ./hello 
 

Demo

Title Text

Subtitle

CUDA

By Syed Asad Zaman

CUDA

  • 101