CUDA Programming on Clairaut

• The clairaut.cs.rit.edu machine has an NVIDIA Tesla C2075 GPU supercomputer card
  • 448 cores
  • 6 GB RAM
  • 1.15 GHz clock speed

• NVIDIA CUDA Version 3.2 is installed on the clairaut.cs.rit.edu machine
  • Note: This is not the latest CUDA release

• Introductory CUDA Programming Seminar

• NVIDIA CUDA Zone: http://developer.nvidia.com/category/zone/cuda-zone

• NVIDIA CUDA Toolkit 3.2 Downloads: http://developer.nvidia.com/cuda-toolkit-32-downloads

• NVIDIA CUDA 3.2 Documentation

• NVIDIA CUDA 3.2 C Programming Guide

• NVIDIA CUDA 3.2 Library documentation

• Log into clairaut.cs.rit.edu.

  $ ssh <username>@clairaut.cs.rit.edu
  

• Set PATH variable.

  $ export PATH=/opt/cuda/bin:$PATH
  

• Set LD_LIBRARY_PATH variable.

  $ export LD_LIBRARY_PATH=/opt/cuda/lib:$LD_LIBRARY_PATH
  

• Use nvcc to compile CUDA programs (see example below)

• Compute an N×N-element matrix C that is the outer product of two N-element vectors A and B
  C_ij = A_i B_j, 0 ≤ i ≤ N−1, 0 ≤ j ≤ N−1

• Sequential main program: OuterProductCPU.cu (download)

• GPU parallel main program: OuterProductGPU.cu (download)

  Things to notice:

  • Storage allocation in CPU main memory and GPU global memory
  • Data copying between CPU main memory and GPU global memory
  • How the grid and block dimensions are determined
  • How each thread determines its position in the overall computation
  • Explicit copying of vector elements from (slow) global memory to (fast) shared memory
  • Subsequent vector element accessing in shared memory

• Pseudorandom number generator: Random.cu (download)

• Utility functions: Util.cu (download)

• Makefile (download)

  all : OuterProductCPU \
  	OuterProductGPU
  
  OuterProductCPU : OuterProductCPU.cu Util.cu Random.cu
  	nvcc -o OuterProductCPU OuterProductCPU.cu
  
  OuterProductGPU : OuterProductGPU.cu Util.cu Random.cu
  	nvcc -arch compute_20 -code compute_20,sm_20 -o OuterProductGPU OuterProductGPU.cu
  
  clean :
  	rm -f OuterProductCPU
  	rm -f OuterProductGPU
  

• Example run on the GPU on clairaut.cs.rit.edu

  $ ./OuterProductGPU 142857 16384
  CUDA device 0: Tesla C2075, compute capability 2.0
  A[0] = 0.856121
  A[16383] = 0.074512
  B[0] = 0.927307
  B[16383] = 0.063003
  C[0][0] = 0.793887
  C[0][16383] = 0.053938
  C[16383][0] = 0.069096
  C[16383][16383] = 0.004694
  23 msec computation
  

• CPU vs. GPU computation time and total time comparison (msec) -- three runs

         ----CPU----  ----GPU----
      N  Comp  Total  Comp  Total
   1024    26     32     0    156
   1024    25     32     1    192
   1024    25     32     0    244
   2048   101    112     0    284
   2048   102    112     0    284
   2048    74     88     1    284
   4096   250    268     1    352
   4096   229    244     1    284
   4096   209    224     1    224
   8192   711    748     5    664
   8192   734    768     5    668
   8192   737    772     5    564
  16384  2625   2728    23   1752
  16384  2549   2656    22   1784
  16384  2600   2704    22   1776
  

• CPU vs. GPU computation time and total time comparison (msec) -- minimum of three runs

         ----CPU----  ----GPU----
      N  Comp  Total  Comp  Total
   1024    25     32     0    156
   2048    74     88     0    284
   4096   209    224     1    224
   8192   711    748     5    564
  16384  2549   2656    22   1752
  

Alan Kaminsky • Department of Computer Science • Rochester Institute of Technology • 4486 + 2220 = 6706

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Copyright © 2012 Alan Kaminsky. All rights reserved. Last updated 12-Apr-2012. Please send comments to ark­@­cs.rit.edu.