CUDA Papers

http://impact.crhc.illinois.edu/ftp/workshop/lcpc08_szu.pdf

Abstract

The computer industry has transitioned into multi-core andmany-core parallel systems. The CUDA programming environment fromNVIDIA is an attempt to make programming many-core GPUs moreaccessible to programmers. However, there are still many burdens placedupon the programmer to maximize performance when using CUDA. Onesuch burden is dealing with the complex memory hierarchy. Efficient andcorrect usage of the various memories is essential, making a difference of2-17x in performance. Currently, the task of determining the appropriatememory to use and the coding of data transfer between memories is stillleft to the programmer.We believe that this task can be better performedby automated tools. We present CUDA-lite, an enhancement to CUDA,as one such tool. We leverage programmer knowledge via annotationsto perform transformations and show preliminary results that indicateauto-generated code can have performance comparable to hand coding.

Authors

Sain-Zee Ueng, Melvin Lathara, Sara S. Baghsorkhi, Wen-mei W. Hwu, Center for Reliable and High-Performance Computing, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign