Programming/Cuda

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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.

Icon tick.png The CUDA platform is designed to work with programming languages such as C, C++. Fortran CUDA is possible through the use of PGI-fortran, which is now available.

GPU hardware

Abstraction

GPU hardware consists of a number of key blocks:

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


Specification

Viper has 4 K40m (GPU01-GPU04) and 1 P100 (GPU05)

NVidia K40m

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.

NVidia P100

Key features of the P100 GPU accelerator include:

  • 16GB HBM2 Memory with a Type PCI Express 3.0 x16 interface (bandwidth 720 GBps)
  • 3584 CUDA cores graphics Engine NVIDIA Tesla P100
  • Bus Type PCI Express 3.0 x16, API Supported OpenCL, OpenACC

Compute capability

For applications that require this information:

  • K40m is 3.5
  • P100 is 6.0

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);
}

Profiling

NVidia provide a good profiling tool call nvprof; its invocation is shown below:

$ module load cuda/9.0.176
$ nvprof ./mycudaprogram

This can also be done with Python too

$ nvprof --print-gpu-trace python train_mnist.py

An example of nvprof output is shown below:

$ nvprof python examples/stream/cusolver.py                                                                                                                                              [10/1910]
==27986== NVPROF is profiling process 27986, command: python examples/stream/cusolver.py
==27986== Profiling application: python examples/stream/cusolver.py
==27986== Profiling result:
Time(%)      Time     Calls       Avg       Min       Max  Name
 41.70%  125.73us         4  31.431us  30.336us  33.312us  void nrm2_kernel<double, double, double, int=0, int=0, int=128, int=0>(cublasNrm2Params<double, double>)
 21.94%  66.144us        36  1.8370us  1.7600us  2.1760us  [CUDA memcpy DtoH]
 13.77%  41.536us        48     865ns     800ns  1.4400us  [CUDA memcpy HtoD]
  3.02%  9.1200us         2  4.5600us  3.8720us  5.2480us  void syhemv_kernel<double, int=64, int=128, int=4, int=5, bool=1, bool=0>(cublasSyhemvParams<double>)
  2.65%  8.0000us         2  4.0000us  3.8720us  4.1280us  void gemv2T_kernel_val<double, double, double, int=128, int=16, int=2, int=2, bool=0>(int, int, double, double const *, int, double const *, i
nt, double, double*, int)
  2.63%  7.9360us         2  3.9680us  3.8720us  4.0640us  cupy_copy
  2.44%  7.3600us         2  3.6800us  3.1680us  4.1920us  void syr2_kernel<double, int=128, int=5, bool=1>(cublasSyher2Params<double>, int, double const *, double)
  2.23%  6.7200us         2  3.3600us  3.2960us  3.4240us  void dot_kernel<double, double, double, int=128, int=0, int=0>(cublasDotParams<double, double>)
  1.88%  5.6640us         2  2.8320us  2.7840us  2.8800us  void reduce_1Block_kernel<double, double, double, int=128, int=7>(double*, int, double*)
  1.74%  5.2480us         2  2.6240us  2.5600us  2.6880us  void ger_kernel<double, double, int=256, int=5, bool=0>(cublasGerParams<double, double>)
  1.57%  4.7360us         2  2.3680us  2.1760us  2.5600us  void axpy_kernel_val<double, double, int=0>(cublasAxpyParamsVal<double, double, double>)
  1.28%  3.8720us         2  1.9360us  1.7920us  2.0800us  void lacpy_kernel<double, int=5, int=3>(int, int, double const *, int, double*, int, int, int)
  1.19%  3.5840us         2  1.7920us  1.6960us  1.8880us  void scal_kernel_val<double, double, int=0>(cublasScalParamsVal<double, double>)
  0.98%  2.9440us         2  1.4720us  1.2160us  1.7280us  void reset_diagonal_real<double, int=8>(int, double*, int)
  0.98%  2.9440us         4     736ns     736ns     736ns  [CUDA memset]

==27986== API calls:
Time(%)      Time     Calls       Avg       Min       Max  Name
 60.34%  408.55ms         9  45.395ms  4.8480us  407.94ms  cudaMalloc
 37.60%  254.60ms         2  127.30ms     556ns  254.60ms  cudaFree
  0.94%  6.3542ms       712  8.9240us     119ns  428.32us  cuDeviceGetAttribute
  0.72%  4.8747ms         8  609.33us  320.37us  885.26us  cuDeviceTotalMem
  0.10%  693.60us        82  8.4580us  2.8370us  72.004us  cudaMemcpyAsync
  0.08%  511.79us         1  511.79us  511.79us  511.79us  cudaHostAlloc
  0.08%  511.75us         8  63.969us  41.317us  99.232us  cuDeviceGetName
  0.05%  310.04us         1  310.04us  310.04us  310.04us  cuModuleLoadData
  0.03%  234.87us        24  9.7860us  5.7190us  50.465us  cudaLaunch
  0.01%  50.874us         2  25.437us  16.898us  33.976us  cuLaunchKernel
  0.01%  49.923us         2  24.961us  15.602us  34.321us  cudaMemcpy
  0.01%  47.622us         4  11.905us  8.6190us  19.889us  cudaMemsetAsync
  0.01%  44.811us         2  22.405us  9.5590us  35.252us  cudaStreamDestroy
  0.01%  35.136us        27  1.3010us     289ns  5.8480us  cudaGetDevice
  0.00%  31.113us        24  1.2960us     972ns  3.2380us  cudaStreamSynchronize
  0.00%  30.736us         2  15.368us  4.4580us  26.278us  cudaStreamCreate
  0.00%  13.932us        17     819ns     414ns  3.7090us  cudaEventCreateWithFlags
  0.00%  13.678us        70     195ns     130ns     801ns  cudaSetupArgument
  0.00%  12.050us         4  3.0120us  2.1290us  4.5130us  cudaFuncGetAttributes
  0.00%  10.407us        22     473ns     268ns  1.9540us  cudaDeviceGetAttribute
  0.00%  10.370us        40     259ns     126ns  1.4100us  cudaGetLastError
  0.00%  9.9680us        16     623ns     185ns  2.9600us  cuDeviceGet



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/10.1.168

/home/user/CUDA/testGPU


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

Alternatives to CUDA

  • OpenACC (part of later gcc compilers)
  • OpenCL (used for DSP, FPGAs too)
  • openMP to GPU pragmas ( >version 4, CPU and GPU)
  • MPI (CPU nodes only)

Further Information

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