Programming/Cuda
From HPC
Contents
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.
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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 (to be retired)
- module add cuda/7.5.18 (to be retired)
- module add cuda/8.0.61
- module add cuda/9.0.176
Compilation
The program would be compiled using NVIDIA's own compiler:
[username@login01 ~]$ module add cuda/9.0.176 [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/9.0.176 /home/user/CUDA/testGPU
[username@login01 ~]$ sbatch demoCUDA.job Submitted batch job 290552
Alternatives to CUDA
- OpenCL (used for DSP, FPGAs too)
- openMP to GPU pragmas ( >version 4, CPU and GPU)
- MPI (CPU nodes only)