In search of clusters: the coming battle in lowly parallel computing
In search of clusters: the coming battle in lowly parallel computing
Space and Time Optimal Parallel Sequence Alignments
IEEE Transactions on Parallel and Distributed Systems
Implementation of the Smith-Waterman algorithm on a reconfigurable supercomputing platform
HPRCTA '07 Proceedings of the 1st international workshop on High-performance reconfigurable computing technology and applications: held in conjunction with SC07
A parallel strategy for biological sequence alignment in restricted memory space
Journal of Parallel and Distributed Computing
Optimised fine and coarse parallelism for sequence homology search
International Journal of Bioinformatics Research and Applications
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
GPU accelerated smith-waterman
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Parallel models for sequence alignment on CPU and GPU
Proceedings of the 12th International Conference on Computer Systems and Technologies
GPU-based NFA implementation for memory efficient high speed regular expression matching
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
ACM Transactions on Programming Languages and Systems (TOPLAS)
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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Biological sequence comparison is a very important operation in Bioinformatics. Even though there do exist exact methods to compare biological sequences, these methods are often neglected due to their quadratic time and space complexity. In order to accelerate these methods, many GPU algorithms were proposed in the literature. Nevertheless, all of them restrict the size of the smallest sequence in such a way that Megabase genome comparison is prevented. In this paper, we propose and evaluate CUDAlign, a GPU algorithm that is able to compare Megabase biological sequences with an exact Smith-Waterman affine gap variant. CUDAlign was implemented in CUDA and tested in two GPU boards, separately. For real sequences whose size range from 1MBP (Megabase Pairs) to 47MBP, a close to uniform GCUPS (Giga Cells Updates per Second) was obtained, showing the potential scalability of our approach. Also, CUDAlign was able to compare the human chromosome 21 and the chimpanzee chromosome 22. This operation took 21 hours on GeForce GTX 280, resulting in a peak performance of 20.375 GCUPS. As far as we know, this is the first time such huge chromosomes are compared with an exact method.