Smith-Waterman implementation on a FSB-FPGA module using the Intel Accelerator Abstraction Layer

Allred, Jeff; Coyne, Jack; Lynch, William L.; Natoli, V.; Grecco, Joseph; Morrissette, Joel

doi:10.1109/ipdps.2009.5161214

Cited by 20 publications

(10 citation statements)

References 5 publications

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“…This allows for a seamless integration of the FPGA accelerator for the alignment kernel with the PaPaRa algorithm running on a standard Linux PC. Related FPGA implementations of the Smith Waterman algorithm used FPGA boards that were connected to the PC via the PCI bus [22] or directly via the CPU bus [23].…”

Section: Related Workmentioning

confidence: 99%

Accelerating Phylogeny-Aware Short DNA Read Alignment with FPGAs

Alachiotis

Berger

Stamatakis

2011

2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines

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Abstract-Recent advances in molecular sequencing technology have given rise to novel algorithms for simultaneously aligning short sequence reads to reference sequence alignments and corresponding evolutionary reference trees. We present a complete hardware/software implementation for the acceleration of a program called PaPaRa, a newly introduced dynamic programming algorithm for this purpose.We verify the correctness of the proposed architecture on a real FPGA and introduce a straight-forward communication protocol (using gigabit ethernet) for seamless integration with the encapsulating steering software that is executed on a PC processor. The hardware description and the software implementation are freely available for download.When mapped to a Virtex 6 FPGA, our reconfigurable architecture can compute 133.4 billion cell updates per second for the novel, tree-based alignment kernel of PaPaRa. Compared to PaPaRa, running on a 3.2GHz Intel Core i5 CPU, we obtain speedups for the alignment kernel, that range between 170 and 471. For the entire application, that is, the alignment kernel and the trace-back step, we obtain speedups between 74 and 118.

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Section: Related Workmentioning

confidence: 99%

Accelerating Phylogeny-Aware Short DNA Read Alignment with FPGAs

Alachiotis

Berger

Stamatakis

2011

2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines

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“…Our target execution platform consists of a high-end FPGA accelerator from XtremeData (XD2000i-FSBFPGA) that has already been successfully used for implementing bioinformatics algorithms [33]. This platform contains two Stratix-III 260 FPGAs, high bandwidth local memory (8.5 Gbytes/s) and a tight coupling to the host front side bus though Intel Quick Assist technology, providing sustained 2 Gbytes/s bandwidth between the FPGA and the host main system memory.…”

Section: Resultsmentioning

confidence: 99%

Combining execution pipelines to improve parallel implementation of HMMER on FPGA

Abbas

Derrien

Rajopadhye

et al. 2015

Microprocessors and Microsystems

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International audienceHMMER is a widely used tool in bioinformatic, based on the Profile Hidden Markov Models. The computation kernels of HMMER, namely MSV and P7Viterbi are very compute intensive, and their data dependencies if interpreted naively, lead to a purely sequential execution. In this paper, we propose a original parallelization scheme for HMMER by rewriting the mathematical formulation, to expose hidden potential parallelization opportunities. Our parallelization scheme targets FPGA technology, and our architecture can achieve 10 times speedup compared with the latest HMMER3 SSE version, without compromising on the sensitivity of original algorithm

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“…Our target execution platform consists in a high-end FPGA accelerator from XtremeData (XD2000i-FSBFPGA) which has already been successfully used for implementing bioinformatics algorithms [2]. This platform contains two Stratix-III 260 FPGAs, high bandwidth local memory (8.5 GBytes/s) and a tight coupling to the host front side bus though Intel Quick Assist technology, providing sustained 2 GBytes/s bandwidth between the FPGA and the host main system memory.…”

Section: Rr N°7370 6 Experimental Resultsmentioning

confidence: 99%

Accelerating HMMER on FPGA using parallel prefixes and reductions

Abbas

Derrien

Rajopadhye

et al. 2010

2010 International Conference on Field-Programmable Technology

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HMMER est un outil basé sur la notion prols à base modèles de Markov cachés, qui est très largement utilisé en bio-informatique. Les parties critiques de lâalgorithme (fonctions MSV et P7Viterbi) utilisées dans HMMER sont très consommatrices en temps de calcul et réputées très diciles à paralléliser. Dans cet article, nous proposons un schéma de parallélisation original pour HMMER, basé sur une reformulation mathématique de lâalgorithme qui permet de découvrir de nouvelles possibilités de parallélisation bien adaptées à des implantations matérielles dédiées. Nous avons implanté cette approche sur un accélérateur FPGA et avons mesuré des gains en performance supérieurs à 10 par rapport à lâimplémentation logicielle de HMMER3, laquelle exploite pourtant déjà de manière extrêmement ecace les extensions SIMD des processeurs x86.

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Smith-Waterman implementation on a FSB-FPGA module using the Intel Accelerator Abstraction Layer

Cited by 20 publications

References 5 publications

Accelerating Phylogeny-Aware Short DNA Read Alignment with FPGAs

Accelerating Phylogeny-Aware Short DNA Read Alignment with FPGAs

Combining execution pipelines to improve parallel implementation of HMMER on FPGA

Accelerating HMMER on FPGA using parallel prefixes and reductions

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