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Theoretical analysis of classic algorithms on highly-threaded many-core GPUs

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Published:06 February 2014Publication History
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Abstract

The Threaded many-core memory (TMM) model provides a framework to analyze the performance of algorithms on GPUs. Here, we investigate the effectiveness of the TMM model by analyzing algorithms for 3 classic problems -- suffix tree/array for string matching, fast Fourier transform, and merge sort -- under this model. Our findings indicate that the TMM model can explain and predict previously unexplained trends and artifacts in experimental data.

References

  1. G. Encarnaijao, N. Sebastiao, and N. Roma. Advantages and GPU implementation of high-performance indexed DNA search based on suffix arrays. In Proc. of HPCS, 2011.Google ScholarGoogle Scholar
  2. N. K. Govindaraju et al. High performance discrete Fourier transforms on graphics processors. In Proc. of SC, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Ma, K. Agrawal, and R. D. Chamberlain. A memory access model for highly-threaded many-core architectures. Future Generation Computer Systems, 30: 202--215, January 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. N. Satish et al. Designing efficient sorting algorithms for manycore GPUs. In Proc. of IPDPS, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM SIGPLAN Notices
      ACM SIGPLAN Notices  Volume 49, Issue 8
      PPoPP '14
      August 2014
      390 pages
      ISSN:0362-1340
      EISSN:1558-1160
      DOI:10.1145/2692916
      Issue’s Table of Contents
      • cover image ACM Conferences
        PPoPP '14: Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming
        February 2014
        412 pages
        ISBN:9781450326568
        DOI:10.1145/2555243

      Copyright © 2014 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 6 February 2014

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