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Short description:
Please summarise the application in a few sentences. Avoid links here. MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by HTS.
Software version:
Biological application domain(s) (Phylogenetics, Genomics, ...):
Genomics, Transcriptomics,
Principal bioinformatics method(s) (Assembly, Mapping, ...):
Sequence alignment,
Technology (Sanger, Illumina, 454, SOLiD, Ion Torrent, ...):
Any
Interface (Command line, Web UI, Desktop GUI, SOAP WS, HTTP WS, API, QL):
Resource type (Command-line tool, Web application, Desktop application, Script, Suite, Workbench, Database portal, Workflow, Plug-in, Library, Web API, Web service, SPARQL endpoint):
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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