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Applying the Stream-Based Computing Model to Design Hardware Accelerators: A Case Study

  • Frederico Pratas
  • Leonel Sousa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5657)

Abstract

To facilitate the design of hardware accelerators we propose in this paper the adoption of the stream-based computing model and the usage of Graphics Processing Units (GPUs) as prototyping platforms. This model exposes the maximum data parallelism available in the applications and decouples computation from memory accesses. The design and implementation procedures, including the programming of GPUs, are illustrated with the widely used MrBayes bioinformatics application. Experimental results show that a straightforward mapping of the stream-based program for the GPU into hardware structures leads to improvements in performance, scalability and cost. Moreover, it is shown that a set of simple optimization techniques can be applied in order to reduce the cost, and the power consumption of hardware solutions.

Keywords

Graphic Processing Unit Substitution Matrix Conditional Likelihood Hardware Accelerator Discrete Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Frederico Pratas
    • 1
  • Leonel Sousa
    • 1
  1. 1.INESC-ID/IST TULisbon, PortugalLisboaPortugal

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