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Parallel Branch Prediction on GPU Platform

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Book cover High Performance Computing and Applications

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5938))

Abstract

Branch Prediction is a common function in nowadays microprocessor. Branch predictor is duplicated into multiple copies in each core of a multicore and many-core processor and makes prediction for multiple concurrent running programs respectively. To evaluate the parallel branch prediction in many-core processor, existed schemes generally use a parallel simulator running in CPU which does not have a real passive parallel running environment to support a many-core simulation and thus has bad simulating performance. In this paper, we firstly try to use a real many-core platform, GPU, to do a parallel branch prediction for future general purpose many-core processor. We verify the new GPU based parallel branch predictor against the traditional CPU based branch predictor. Experiment result shows that GPU based parallel simulation scheme is a promising way to faster simulating speed for future many-core processor research.

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Author information

Authors and Affiliations

  1. College of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia, 010021, P.R. China

    Liqiang He & Guangyong Zhang

Authors
  1. Liqiang He
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  2. Guangyong Zhang
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Editor information

Editors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, 200072, Shanghai, P.R. China

    Wu Zhang  & Weiqin Tong  & 

  2. Schulich School of Engineering, University of Calgary, T2N 1N4, Calgary, AB, Canada

    Zhangxin Chen

  3. Department of Mathematics, University of Wyoming, 1000 University Avenue, 82071-3036, Laramie, WY, USA

    Craig C. Douglas

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© 2010 Springer-Verlag Berlin Heidelberg

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He, L., Zhang, G. (2010). Parallel Branch Prediction on GPU Platform. In: Zhang, W., Chen, Z., Douglas, C.C., Tong, W. (eds) High Performance Computing and Applications. Lecture Notes in Computer Science, vol 5938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11842-5_20

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  • DOI: https://doi.org/10.1007/978-3-642-11842-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11841-8

  • Online ISBN: 978-3-642-11842-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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