Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Bio-sequence database scanning on a GPU
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Parallel Position Weight Matrices algorithms
Parallel Computing
CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SIMD re-convergence at thread frontiers
Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Using the GPGPU for scaling up mining software repositories
Proceedings of the 34th International Conference on Software Engineering
Integrating GPU-accelerated sequence alignment and SNP detection for genome resequencing analysis
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Direct approaches to exploit many-core architecture in bioinformatics
Future Generation Computer Systems
Acceleration of the long read mapping on a PC-FPGA architecture (abstract only)
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
Reducing divergence in GPGPU programs with loop merging
Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units
Frequency-based re-sequencing tool for short reads on graphics processing units
International Journal of Computational Science and Engineering
International Journal of Computational Science and Engineering
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MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. 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. MUMmerGPU is available open-source at http://www.mummergpu.sourceforge.net.