A Speculative HMMER Search Implementation on GPU

Li, Xiaoqiang; Han, Wenting; Gu, Lin; An, Hong; Xu, Mu; Zhou, Wei; Li, Qi

doi:10.1109/ipdpsw.2012.91

Cited by 14 publications

(6 citation statements)

References 14 publications

(12 reference statements)

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“…Moreover, in recent years in order to take advantages of new high performance hardwares, several works on accelerating HMMER on GPUs have been reported such as [61] in 2010 and [62] in 2012. And Oliver et al [63] reported their work on accelerating HMMER searching using FPGAs in 2008.…”

Section: Taxonomymentioning

confidence: 99%

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

Yin

Lan

Tan

et al. 2017

Computational and Structural Biotechnology Journal

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The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.

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Section: Taxonomymentioning

confidence: 99%

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

Yin

Lan

Tan

et al. 2017

Computational and Structural Biotechnology Journal

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“…Previous research on GPU-based acceleration of algorithms related to HMMs (Hidden Markov models) in computational biology mainly focuses on HMMer, which is a software suite for protein sequence similarity calculation using probabilistic models called profile hidden Markov models (profile-HMMs) [8] [9]. However, the input of the profile-HMMs forward algorithm in HMMer is composed of a sequence and a reference database, which is much different from the input of PFA in the GATK HaplotypeCaller.…”

Section: B Related Workmentioning

confidence: 99%

Exploration of alternative GPU implementations of the pair-HMMs forward algorithm

Ren

Koen

Al-Ars

2016

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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In order to handle the massive raw data generated by next generation sequencing (NGS) platforms, GPUs are widely used by many genetic analysis tools to speed up the used algorithms. In this paper, we use GPUs to accelerate the pair-HMMs forward algorithm, which is used to calculate the overall alignment probability in many genomics analysis tools. We firstly evaluate two different implementation methods to accelerate the pair-HMMs forward algorithm according to their effectiveness on GPU platforms. Based on these two methods, we present several implementations of the pair-HMMs forward algorithm. We execute these implementations on the NVIDIA Tesla K40 card using different datasets to compare the performance. Experimental results show that the intra-task implementation has the highest throughput in most cases, achieving pure computational throughput as high as 23.56 GCUPS for synthetic datasets. On a real dataset, the inter-task implementation achieves 4.82x speedup compared with a vectorized implementation executed on a 20-core POWER8 system.

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“…The number of gene sequence alignment tasks reaches more than one million. Therefore, many researchers use parallel platforms [13] such as multi-core CPUs [9,14], GPUs [15,16,17,18,19] and Field Programmable Gate Arrays (FPGAs) [20,21,22,23,24,25] to develop parallelism in gene sequence alignment applications. Compared with CPUs and GPUs, FPGA chips serve as custom hardware for the computing-intensive applications with numerous computing and storage resources [26,27].…”

Section: Introductionmentioning

confidence: 99%

Pair-HMM accelerator based on non-cooperative structure

Wang

Lei

Dou

2019

IEICE Electron. Express

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Pair Hidden Markov Model (Pair-HMM) forward algorithm is gaining increasing popularity in biological research tools. We propose a novel non-cooperative structure of Pair-HMM forward algorithm accelerator on Field Programmable Gate Array (FPGA). We employ a tasklevel parallel scheme in the structure. We design the non-cooperative Processing Element (PE) to complete Pair-HMM forward algorithm independently. Our three-layer tree topology improves the scalability for different FPGAs. Compared to previous works, our structure reduces the idle cycles which occurs in the systolic array structure and the PE ring structure. Compared with the PE ring, our implementation on Arria 10 can achieve 1.19× speedups.

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A Speculative HMMER Search Implementation on GPU

Cited by 14 publications

References 14 publications

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

Exploration of alternative GPU implementations of the pair-HMMs forward algorithm

Pair-HMM accelerator based on non-cooperative structure

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