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.
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References
Plishker, W., Sane, N., Kiemb, M., Anand, K., Bhattacharyya, S.: Functional DIF for Rapid Prototyping. In: RSP, pp. 17–23 (2008)
Dally, J., Labonte, F., Das, A., Hanrahan, P., Ahn, J., Gummaraju, J., Erez, M., Jayasena, N., Buck, I., Knight, J., Kapasi, U.: Merrimac: Supercomputing with Streams. In: Proc. of the Int. Conf. on Supercomputing, USA (2003)
Gummaraju, J., Rosenblum, M.: Stream Programming on General-Purpose Processors. In: MICRO 2005, Washington, DC, USA, pp. 343–354. IEEE Computer Society, Los Alamitos (2005)
Lindholm, E., Nickolls, J., Oberman, S., Montrym, J.: NVIDIA Tesla: A Unified Graphics and Computing Architecture. In: MICRO 2008, March 2008, vol. 28(2), pp. 39–55 (2008)
Thies, W., Karczmarek, M., Amarasinghe, S.: StreamIt: A Language for Streaming Applications. In: Horspool, R.N. (ed.) CC 2002. LNCS, vol. 2304, pp. 179–196. Springer, Heidelberg (2002)
Ronquist, F., Huelsenbeck, J.: MrBayes 3: Bayesian Phylogenetic Inference Under Mixed Models. Bioinformatics 19(12), 1572–1574 (2003)
Stamatakis, A., Ludwig, T., Meier, H.: RAxML-III: A Fast Program for Maximum Likelihood-based Inference of Large Phylogenetic Trees. Bioinformatics 21(4), 456–463 (2005)
Felsenstein, J.: Evolutionary Trees from DNA Sequences: A Maximum Likelihood Approach. Journal of Molecular Evolution 17, 368–376 (1981)
Ott, M., Zola, J., Stamatakis, A., Aluru, S.: Large-scale Maximum Likelihood-based Phylogenetic Analysis on the IBM BlueGene/L. In: On-Line Proc. of IEEE/ACM Supercomputing Conf. (2007)
Yang, Z.: Maximum Likelihood Phylogenetic Estimation from DNA Sequences with Variable Rates Over Sites. Journal of Molecular Evolution 39, 306–314 (1994)
Parhi, K.: VLSI Digital Signal Processing Systems. Wiley, New York (1999)
Xilinx: Virtex-5 Family Overview. Xilinx Product Specification (February 2009)
Rambaut, A., Grass, N.: Seq-Gen: An Application for the Monte Carlo Simulation of DNA Sequence Evolution along Phylogenetic Trees. C. App. in BioSc. 13, 235–238 (1997)
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Pratas, F., Sousa, L. (2009). Applying the Stream-Based Computing Model to Design Hardware Accelerators: A Case Study. In: Bertels, K., Dimopoulos, N., Silvano, C., Wong, S. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2009. Lecture Notes in Computer Science, vol 5657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03138-0_26
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DOI: https://doi.org/10.1007/978-3-642-03138-0_26
Publisher Name: Springer, Berlin, Heidelberg
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