Revision as of 11:01, 19 April 2018

Programming Details

CUDA is a parallel computing platform and application programming interface (API) model created by NVidia. It allows you to program a CUDA-enabled graphics processing unit (GPU) for general purpose processing.

The CUDA platform is designed to work with programming languages such as C, C++. Fortran CUDA is possible through the use of PGI-fortran, at the time of writing this was not available on Viper.

GPU hardware

Abstraction

GPU hardware consists of a number of key blocks:

• Memory (global, constant, shared)
• Streaming multiprocessors (SMs)
• Streaming processors (SPs)

Specification

Key features of the Tesla K40 GPU accelerator include:

• 12GB of ultra-fast GDDR5 memory allows users to process 2X larger datasets, enabling them to rapidly analyze massive volumes of data.
• 2,880 CUDA® parallel processing cores deliver application acceleration by up to 10X compared to using a CPU alone.
• Dynamic Parallelism enables GPU threads to dynamically spawn new threads, enabling users to quickly and easily crunch through adaptive and dynamic data structures.
• PCIe Gen-3 interconnect support accelerates data movement by 2X compared to PCIe Gen-2 technology.

Programming example

#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<<31;

  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: %fn", maxError);
}

Modules Available

The following modules are available:

• module add cuda/6.5.14 (or)
• module add cuda/7.5.18

Compilation

The program would be compiled using NVIDIA's own compiler:

[username@login01 ~]$ module add cuda/7.5.18
[username@login01 ~]$ nvcc -o testGPU testGPU.cu

Usage Examples

Batch example

#!/bin/bash

#SBATCH -J gpu-cuda
#SBATCH -N 1
#SBATCH --ntasks-per-node 1
#SBATCH -o %N.%j.%a.out
#SBATCH -e %N.%j.%a.err
#SBATCH -p gpu
#SBATCH --gres=gpu:tesla
#SBATCH --exclusive

module add cuda/7.5.18

/home/user/CUDA/testGPU

[username@login01 ~]$ sbatch demoCUDA.job
Submitted batch job 290552

Alternatives to CUDA

• OpenCL
• DirectCompute (Windows only)
• MPI (CPU nodes)

Further Information

• http://www.nvidia.com/object/cuda_home_new.html
• 'Youtube - CUDA training video'

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