Accelerating HMMer on FPGAs using systolic array based architecture

Yan-teng, Sun; Li, Peng; Gu, Guochang; Wen, Yonggang; Liu, Yuan; Liu, Dong

doi:10.1109/ipdps.2009.5160927

Cited by 14 publications

(8 citation statements)

References 13 publications

(14 reference statements)

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“…For taxonomical analysis, 16S/18S rRNA fragment extraction is realized by GPU based HMM [21], [24], [25] with parallel computing on GPU, while the gene prediction is realized by FragGeneScan [26].…”

Section: Methodsmentioning

confidence: 99%

Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization

Pan

Song

et al. 2014

PLoS ONE

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The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.

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Section: Methodsmentioning

confidence: 99%

Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization

Pan

Song

et al. 2014

PLoS ONE

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“…Another recent research [ 19 ] proposed a systolic-array-based implementation of plan 7 HMM on FPGAs with a parallel data providing unit and an autorecalculation unit. A speedup of 56.8 with 20 PEs on a Virtex-5 board was obtained compared to an Intel Core 2 Duo 2.33 GHz CPU.…”

Section: Hardware-accelerated Hmmermentioning

confidence: 99%

“…Since such hardware is specifically tailored for the sequence alignment problem, good performance can be easily achieved. FPGA has been used to build special-purpose hardware for accelerating HMMER [ 19 , 27 , 28 ]. An FPGA is an integrated circuits that contains a lot of programmable interconnected “logic blocks.” It can be configured to implement various algorithms using a hardware description language.…”

Section: Introductionmentioning

confidence: 99%

Modern Computational Techniques for the HMMER Sequence Analysis

Meng

2013

ISRN Bioinformatics

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This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies.

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“…[15], [16] developed a MPI HMMER 2 implementation on cluster, which will get near liner speedup when the number of nodes is less than 64. There are many parallel implementation on novel hardware such as FPGA [6]- [9], GPUs [4], [5], [10], [23], network processor [11] and Cell/B.E. Processor [12].…”

Section: Algorithm 1: Pseudo-code Of Hmmsearchmentioning

confidence: 99%

A Speculative HMMER Search Implementation on GPU

Han

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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Due to the exponentially growing bioinformatics databases and rapidly popular of GPU for general purpose computing, it is promising to employ GPU techniques to accelerate the sequence search process. Hmmsearch from HMMER bioinformatics software package is a wildly used software tool for sensitive profile HMM (Hidden Markov Model) searches of biological sequence databases. In this paper, we implement a speculative hmmsearch implementation on NVIDIA Fermi GPU and apply various optimizations to it. We test the enhancements in our GPU implementation in order to demonstrate the effectiveness of optimization strategies. Result shows that our speculative hmmsearch implementation achieves up to 6.5x speedup over previous fast single-threaded SSE implementation.

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Accelerating HMMer on FPGAs using systolic array based architecture

Abstract: HMMer is a widely-used bioinformatics software package that uses profile HMMs (Hidden Markov Models)

Cited by 14 publications

References 13 publications

Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization

Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization

Modern Computational Techniques for the HMMER Sequence Analysis

A Speculative HMMER Search Implementation on GPU

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