CUDA

CUDA Overview

CUDA is a feature set for programming nVidia GPUs. We have many dwarf nodes that are CUDA-enabled with 1-2 GPUs and most of the Wizard nodes have 4 GPUs each. Most of these are consumer grade nVidia 1080 Ti graphics cards that are good for accelerating 32-bit calculations. Dwarf36-38 have two nVidia RTX A4000 graphic cards and dwarf39 has two nVidia 1080 Ti graphics cards that are available for anybody to use but you'll need to email beocat@cs.ksu.edu to request being added to the GPU priority group then you'll need to submit jobs with --partition=ksu-gen-gpu.q. Wizard20 and wizard21 each have two nVidia P100 cards that are much more costly than the consumer grade 1080Ti cards but can accelerate 64-bit calculations much better.

Training videos

CUDA Programming Model Overview: http://www.youtube.com/watch?v=aveYOlBSe-Y

CUDA Programming Basics Part I (Host functions): http://www.youtube.com/watch?v=79VARRFwQgY

CUDA Programming Basics Part II (Device functions): http://www.youtube.com/watch?v=G5-iI1ogDW4

Compiling CUDA Applications

nvcc is the compiler for CUDA applications. When compiling your applications manually you will need to load a CUDA enabled compiler toolchain (e.g. fosscuda):

• module load fosscuda
• Do not run your cuda applications on the headnode. I cannot guarantee it will run, and it will give you terrible results if it does run.

With those two things in mind, you can compile CUDA applications as follows:

module load fosscuda
nvcc <source>.cu -o <output>

Example

Create your Application

Copy the following Application into Beocat as vecadd.cu

//  Kernel definition, see also section 4.2.3 of Nvidia Cuda Programming Guide
__global__  void vecAdd(float* A, float* B, float* C)
{
            // threadIdx.x is a built-in variable  provided by CUDA at runtime
            int i = threadIdx.x;
       A[i]=0;
       B[i]=i;
       C[i] = A[i] + B[i];
}

#include  <stdio.h>
#define  SIZE 10
int  main()
{
   int N=SIZE;
   float A[SIZE], B[SIZE], C[SIZE];
   float *devPtrA;
   float *devPtrB;
   float *devPtrC;
   int memsize= SIZE * sizeof(float);

   cudaMalloc((void**)&devPtrA, memsize);
   cudaMalloc((void**)&devPtrB, memsize);
   cudaMalloc((void**)&devPtrC, memsize);
   cudaMemcpy(devPtrA, A, memsize,  cudaMemcpyHostToDevice);
   cudaMemcpy(devPtrB, B, memsize,  cudaMemcpyHostToDevice);
   // __global__ functions are called:  Func<<< Dg, Db, Ns  >>>(parameter);
   vecAdd<<<1, N>>>(devPtrA,  devPtrB, devPtrC);
   cudaMemcpy(C, devPtrC, memsize,  cudaMemcpyDeviceToHost);

   for (int i=0; i<SIZE; i++)
        printf("C[%d]=%f\n",i,C[i]);

  cudaFree(devPtrA);
  cudaFree(devPtrA);
  cudaFree(devPtrA);

}

Gain Access to a CUDA-capable Node

See our advanced scheduler documentation

Compile Your Application

module load fosscuda
nvcc vecadd.cu -o vecadd

This will create a program with the name 'vecadd' (specified by the '-o' flag).

Run Your Application

Run the program as you usually would, namely

./vecadd

Assuming you don't want to run the program interactively because this is a large job, you can submit a job via sbatch, just be sure to add '--gres=gpu:1' to the sbatch directive.