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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. Dwarf38 has two nVidia 980 Ti graphic cards and dwarf39 has two nVidia 1080 Ti graphics cards that are available for anybody to use but you'll need to email 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: <HTML5video type="youtube" width="800" height="480" autoplay="false">aveYOlBSe-Y</HTML5video>

CUDA Programming Basics Part I (Host functions): <HTML5video type="youtube" width="800" height="480" autoplay="false">79VARRFwQgY</HTML5video>

CUDA Programming Basics Part II (Device functions): <HTML5video type="youtube" width="800" height="480" autoplay="false">G5-iI1ogDW4</HTML5video>

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>


Create your Application

Copy the following Application into Beocat as

//  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;
       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++)



Gain Access to a CUDA-capable Node

See our advanced scheduler documentation

Compile Your Application

module load fosscuda
nvcc -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


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.