Implementing parallel hmm-pfam on the EARTH multithreaded architecture

Zhu, Weirong; Niu, Yanwei; Lu, Jue; Gao, Guang R.

doi:10.1109/csb.2003.1227404

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Another multi-threading implementation of the hmmpfam program was done by Zhu et al [ 11 ]. They reimplemented hmmpfam on EARTH (Efficient Architecture for Running Threads)—an event-driven fine-grain multithreaded program execution model.…”

Section: Software Accelerated Hmmermentioning

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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Section: Software Accelerated Hmmermentioning

confidence: 99%

Modern Computational Techniques for the HMMER Sequence Analysis

Meng

2013

ISRN Bioinformatics

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“…A VLSI design for evaluating HMM was introduced in [4], and the proposed architecture has sufficient power to construct a recognition system that could respond in real time. An implementation of HMM on a cluster of 128 dual-CPU nodes was introduced in [5], the execution speed was highly improved. Generally, the second way is better than the first, but cost much more than the first.…”

Section: Introductionmentioning

confidence: 99%

The fast evaluation of hidden Markov models on GPU

Shuang-ping

2009

2009 IEEE International Conference on Intelligent Computing and Intelligent Systems

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It is compute-intensive to evaluate the probability of an observation sequence on a hidden Markov model. Some fast algorithms exit, the forward-backward procedure is the most popular one among them. The forward-backward procedure can save much computation, but its time complexity is N 2T, in other words, there is a high computational complexity in the algorithm. In this paper, we present a parallel evaluation algorithm using a commodity graphics processing unit. The algorithm exploits the single-instruction-multiple-thread architecture of GPU to get high-performance. First, the forward probabilities are calculated in parallel, and then they are summed up also in parallel to get the probability of an observation sequence. The optimal using of memory bandwidth is studied in the algorithm to obtain the best performance. The algorithm was implemented on a NVIDIA 9800 GTX+ GPU, experimental results showed the parallel algorithm can evaluate the probability of an observation sequence on a hidden Markov model 4--25 times fast than the classic one does. hidden markov model; GPGPU,. evaluation probability I.

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“…searching profile HMMs against the Swissprot/TrEMBL sequence databases [2]. Consequently several parallel solutions for HMMer database searching have been developed on coarsegrained architectures, such as clusters [4,17], as well as on fine-grained architectures, such as network processors [16] and graphics cards [10]. In this paper we show how reconfigurable field-programmable gate array (FPGA)-based hardware platforms can be used to accelerate HMMer database searching by one to two orders of magnitude.…”

Section: Introductionmentioning

confidence: 99%