GPU-S2S: A Compiler for Source-to-Source Translation on GPU

CUDA facilitates the development of General Purpose computing on Graphics Processing Units (GPGPU), however, its complex memory system, thread-level structure, and data transmission control between memories have brought great challenges for programming on GPU. In order to facilitate the development...

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Bibliographic Details
Published in:2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming pp. 144 - 148
Main Authors: Dan Li, Haijun Cao, Xiaoshe Dong, Bao Zhang
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2010
Subjects:
Arrays
compiler directive
Data mining
GPU
Graphics processing unit
Instruction sets
Kernel
Libraries
Optimization
source-to-source translation
ISBN:1424494826, 9781424494828
ISSN:2168-3034
Online Access:Get full text
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Summary:CUDA facilitates the development of General Purpose computing on Graphics Processing Units (GPGPU), however, its complex memory system, thread-level structure, and data transmission control between memories have brought great challenges for programming on GPU. In order to facilitate the development of parallel programs on GPU and reuse existing sequential codes, in this paper we propose a novel directive based compiler guided approach. Through combining automatic mapping and static compilation, we have implemented a prototype of automatic source-to-source translation tool named GPU-S2S, capable of translating the C sequential code with directives into CUDA code. Experimental results show that CUDA code generated by GPU-S2S can achieve comparable performance with that of CUDA benchmark provided by NVIDIA CUDA SDK, and has significant performance improvements compared with its original C sequential code.
ISBN:1424494826
9781424494828
ISSN:2168-3034
DOI:10.1109/PAAP.2010.43