Hardware Acceleration of Genetic Sequence Alignment

Arram, James; Tsoi, Kuen Hung; Luk, Wayne; Jiang, Peiyong

doi:10.1007/978-3-642-36812-7_2

Cited by 27 publications

(17 citation statements)

References 9 publications

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“…Methodologies for fast creation of system software did exist, but effective tools for large scale efforts of this sort did not; and (c) Applications of those days were not of the Big Data type, so the streaming capabilities of the DataFlow computing model could not generate performance superiority. Recent measurements show, that, currently, Maxeler can move internally over 1 TB of data per second [15].…”

Section: Discussionmentioning

confidence: 99%

Paradigm Shift in Big Data SuperComputing: DataFlow vs. ControlFlow

Trifunović

Milutinović

Salom

et al. 2015

Journal of Big Data

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The paper discusses the shift in the computing paradigm and the programming model for Big Data problems and applications. We compare DataFlow and ControlFlow programming models through their quantity and quality aspects. Big Data problems and applications that are suitable for implementation on DataFlow computers should not be measured using the same measures as ControlFlow computers. We propose a new methodology for benchmarking, which takes into account not only the execution time, but also the power and space, needed to complete the task. Recent research shows that if the TOP500 ranking was based on the new performance measures, DataFlow machines would outperform ControlFlow machines. To support the above claims, we present eight recent implementations of various algorithms using the DataFlow paradigm, which show considerable speed-ups, power reductions and space savings over their implementation using the ControlFlow paradigm.

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Section: Discussionmentioning

confidence: 99%

Paradigm Shift in Big Data SuperComputing: DataFlow vs. ControlFlow

Trifunović

Milutinović

Salom

et al. 2015

Journal of Big Data

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show abstract

“…This work expands on our previous efforts in accelerating sequence alignment using FPGAs [2], [3]. In these efforts we present: 1) a hardware acceleration of the FM-index for exact and approximate alignment, and 2) design space exploration for FPGA-based alignment architectures.…”

Section: Related Workmentioning

confidence: 91%

Leveraging FPGAs for Accelerating Short Read Alignment

Arram

Kaplan

Luk

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-One of the key challenges facing genomics today is how to efficiently analyse the massive amounts of data produced by next-generation sequencing platforms. With general-purpose computing systems struggling to address this challenge, specialised processors such as the Field-Programmable Gate Array (FPGA) are receiving growing interest. The means by which to leverage this technology for accelerating genomic data analysis is however largely unexplored. In this paper we present a runtime reconfigurable architecture for accelerating short read alignment using FPGAs. This architecture exploits the reconfigurability of FPGAs to allow the development of fast yet flexible alignment designs. We apply this architecture to develop an alignment design which supports exact and approximate alignment with up to 2 mismatches. Our design is based on the FM-index, with optimisations to improve the alignment performance. In particular, the n-step FM-index, index oversampling, a seedand-compare stage, and bi-directional backtracking are included. Our design is implemented and evaluated on a 1U Maxeler MPC-X2000 dataflow node with 8 Altera Stratix-V FPGAs. Measurements show that our design is 28 times faster than Bowtie2 running with 16 threads on dual Intel Xeon E5-2640 CPUs, and 9 times faster than Soap3-dp running on an NVIDIA Tesla C2070 GPU.

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“…In [89] and [72], Arram et al introduce a hardware design that incorporates specialized matchers for exact and approximate sequence alignment, while at the same time runtime reconfiguration is used to fully populate the FPGA with each type of matchers. Such decoupling enables the flexibility of optimizing each matcher according to the intended workload, hence resulting in higher parallelism and performance.…”

Section: Mappingmentioning

confidence: 99%

Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts

Liu

Luk

2017

2017 27th International Conference on Field Programmable Logic and Applications (FPL)

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Abstract-Genetic sequence alignment has always been a computational challenge in bioinformatics. Depending on the problem size, software-based aligners can take multiple CPUdays to process the sequence data, creating a bottleneck point in bioinformatic analysis flow. Reconfigurable accelerator can achieve high performance for such computation by providing massive parallelism, but at the expense of programming flexibility and thus has not been commensurately used by practitioners. Therefore, this paper aims to provide a thorough survey of the proposed accelerators by giving a qualitative categorization based on their algorithms and speedup. A comprehensive comparison between work is also presented so as to guide selection for biologist, and to provide insight on future research direction for FPGA scientists.

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Hardware Acceleration of Genetic Sequence Alignment

Cited by 27 publications

References 9 publications

Paradigm Shift in Big Data SuperComputing: DataFlow vs. ControlFlow

Paradigm Shift in Big Data SuperComputing: DataFlow vs. ControlFlow

Leveraging FPGAs for Accelerating Short Read Alignment

Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts

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