Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 1. 4
OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 1. 4
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Metaprogramming GPUs with Sh
LU-GPU: Efficient Algorithms for Solving Dense Linear Systems on Graphics Hardware
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Accelerator: using data parallelism to program GPUs for general-purpose uses
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
A performance-oriented data parallel virtual machine for GPUs
ACM SIGGRAPH 2006 Sketches
Scout: a data-parallel programming language for graphics processors
Parallel Computing
Using The Gnu Compiler Collection: A Gnu Manual For Gcc Version 4.3.3
Using The Gnu Compiler Collection: A Gnu Manual For Gcc Version 4.3.3
GPU-ABiSort: optimal parallel sorting on stream architectures
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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In recent years, the GPU (graphics processing unit) has evolved into an extremely powerful and flexible processor, with it now representing an attractive platform for general-purpose computation. Moreover, changes to the design and programmability of GPUs provide the opportunity to perform general-purpose computation on a GPU (GPGPU). Even though many programming languages, software tools, and libraries have been proposed to facilitate GPGPU programming, the unusual and specific programming model of the GPU remains a significant barrier to writing GPGPU programs. In this paper, we introduce a novel compiler-based approach for GPGPU programming. Compiler directives are used to label code fragments that are to be executed on the GPU. Our GPGPU compiler, Guru, converts the labeled code fragments into ISO-compliant C code that contains appropriate OpenGL and Cg APIs. A native C compiler can then be used to compile it into the executable code for GPU. Our compiler is implemented based on the Open64 compiler infrastructure. Preliminary experimental results from selected benchmarks show that our compiler produces significant performance improvements for programs that exhibit a high degree of data parallelism.