Difference between revisions of "MUMmerGPU"
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|sw summary=MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by HTS. | |sw summary=MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by HTS. | ||
|bio domain=Genomics, Transcriptomics, | |bio domain=Genomics, Transcriptomics, | ||
| − | |bio method= | + | |bio method=Sequence alignment, |
| − | |bio tech= | + | |bio tech=Any |
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MUMmerGPU is an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies. MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. | MUMmerGPU is an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies. MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. | ||
Latest revision as of 22:33, 19 December 2015
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Application data |
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| Biological application domain(s) | Genomics, Transcriptomics |
|---|---|
| Principal bioinformatics method(s) | Sequence alignment |
| Technology | Any |
| Maintained? | Maybe |
Summary: MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by HTS.
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MUMmerGPU is an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies. MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies.
MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13x faster than the serial CPU version of the alignment code and nearly 4x faster in total computation time than MUMmerGPU 1.0. We exhaustively examined 128 GPU data layout configurations to improve register footprint and running time and conclude higher occupancy has greater impact than reduced latency for achieving high performance for data intensive GPGPU applications.
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