CUDA-Lite: Reducing GPU Programming Complexity

  • Authors:

  • Sain-Zee Ueng;Melvin Lathara;Sara S. Baghsorkhi;Wen-Mei W. Hwu

  • Affiliations:

  • Center for Reliable and High-Performance Computing Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign,;Center for Reliable and High-Performance Computing Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign,;Center for Reliable and High-Performance Computing Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign,;Center for Reliable and High-Performance Computing Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign,

  • Venue:
  • Languages and Compilers for Parallel Computing
  • Year:
  • 2008

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Abstract

The computer industry has transitioned into multi-core and many-core parallel systems. The CUDA programming environment from NVIDIA is an attempt to make programming many-core GPUs more accessible to programmers. However, there are still many burdens placed upon the programmer to maximize performance when using CUDA. One such burden is dealing with the complex memory hierarchy. Efficient and correct usage of the various memories is essential, making a difference of 2-17x in performance. Currently, the task of determining the appropriate memory to use and the coding of data transfer between memories is still left to the programmer. We believe that this task can be better performed by automated tools. We present CUDA-lite, an enhancement to CUDA, as one such tool. We leverage programmer knowledge via annotations to perform transformations and show preliminary results that indicate auto-generated code can have performance comparable to hand coding.