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CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU

Overview of attention for article published in BMC Bioinformatics, February 2016
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Title
CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0946-4
Pubmed ID
Authors

Hanyu Jiang, Narayan Ganesan

Abstract

HMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors. A Multi-Tiered Parallel Framework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism for MSV/SSV and Viterbi algorithms. We couple SIMT (Single Instruction Multiple Threads) mechanism with SIMD (Single Instructions Multiple Data) video instructions with warp-synchronism to achieve high-throughput processing and eliminate thread idling. We also propose a hardware-aware optimal allocation scheme of scarce resources like on-chip memory and caches in order to boost performance and scalability of CUDAMPF. In addition, runtime compilation via NVRTC available with CUDA 7.0 is incorporated into the presented framework that not only helps unroll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic loading and switching of kernels depending on the query model size, in order to achieve optimal performance. CUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications. It achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features. In addition to exceeding performance of other acceleration attempts, comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields upto 440 GCUPS for SSV, 277 GCUPS for MSV and 14.3 GCUPS for P7Viterbi all with 100 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7Viterbi respectively. The source code is available at https://github.com/Super-Hippo/CUDAMPF .

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 5%
Norway 1 5%
Unknown 20 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 36%
Student > Ph. D. Student 6 27%
Student > Master 4 18%
Other 2 9%
Lecturer 1 5%
Other 1 5%
Readers by discipline Count As %
Computer Science 8 36%
Biochemistry, Genetics and Molecular Biology 6 27%
Agricultural and Biological Sciences 2 9%
Engineering 2 9%
Medicine and Dentistry 2 9%
Other 1 5%
Unknown 1 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 March 2016.
All research outputs
#6,909,307
of 12,010,397 outputs
Outputs from BMC Bioinformatics
#2,669
of 4,370 outputs
Outputs of similar age
#136,967
of 291,834 outputs
Outputs of similar age from BMC Bioinformatics
#94
of 136 outputs
Altmetric has tracked 12,010,397 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,370 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
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We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.