3096

Architecture-Aware Optimization Targeting Multithreaded Stream Computing

Byunghyun Jang, Synho Do, Homer Pien, David Kaeli
Department of ECE, Northeastern University, Boston, U.S.A.
GPGPU-2 Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units

@conference{jang2009architecture,

   title={Architecture-aware optimization targeting multithreaded stream computing},

   author={Jang, B. and Do, S. and Pien, H. and Kaeli, D.},

   booktitle={Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units},

   pages={62–70},

   year={2009},

   organization={ACM}

}

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Optimizing program execution targeted for Graphics Processing Units (GPUs) can be very challenging. Our ability to efficiently map serial code to a GPU or stream processing platform is a time consuming task and is greatly hampered by a lack of detail about the underlying hardware. Programmers are left to attempt trial and error to produce optimized codes. Recent publication of the underlying instruction set architecture (ISA) of the AMD/ATI GPU has allowed researchers to begin to propose aggressive optimizations. In this work, we present an optimization methodology that utilizes this information to accelerate programs on AMD/ATI GPUs. We start by defining optimization spaces that guide our work. We begin with disassembled machine code and collect program statistics provided by the AMD Graphics Shader Analyzer (GSA) profiling toolset. We explore optimizations targeting three different computing resources: 1) ALUs, 2) fetch bandwidth, and 3) thread usage, and present optimization techniques that consider how to better utilize each resource. We demonstrate the effectiveness of our proposed optimization approach on an AMD Radeon HD3870 GPU using the Brook+ stream programming language. We describe our optimizations using two commonly-used GPGPU applications that present very different program characteristics and optimization spaces: matrix multiplication and back-projection for medical image reconstruction. Our results show that optimized code can improve performance by 1.45x–6.7x as compared to unoptimized code run on the same GPU platform. The speedup obtained with our optimized implementations are 882x (matrix multiply) and 19x (back-projection) faster as compared with serial implementations run on an Intel 2.66 GHz Core 2 Duo with a 2 GB main memory.
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