Guided self-scheduling: A practical scheduling scheme for parallel supercomputers
IEEE Transactions on Computers
Load-sharing in heterogeneous systems via weighted factoring
Proceedings of the eighth annual ACM symposium on Parallel algorithms and architectures
Trapezoid Self-Scheduling: A Practical Scheduling Scheme for Parallel Compilers
IEEE Transactions on Parallel and Distributed Systems
Parallel Multiple Sequences Alignment in SMP Cluster
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
IEEE Transactions on Computers
MT-clustalW: multithreading multiple sequence alignment
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
Hi-index | 0.00 |
Multiple Sequence Alignment (MSA) is an important problem in Bioinformatics that aims to align more than two sequences in order to emphasize similarity regions. This problem is known to be NP-Hard, so heuristic methods are used to solve it. DIALIGN-TX is an iterative heuristic method for MSA that generates alignments by concatenating ungapped regions with high similarity. Usually, the first phase of MSA algorithms is parallelized by distributing several independent tasks among the nodes. Even though heterogeneous multicore clusters are becoming very common nowadays, very few task allocation policies were proposed for this type of architecture. This paper proposes an MPI/OpenMP master/slave parallel strategy to run DIALIGN-TX in heterogeneous multicore clusters, with several allocation policies. We show that an appropriate choice of the master node has great impact on the overall system performance. Also, the results obtained in a heterogeneous multicore cluster composed of 4 nodes (30 cores), with real sequence sets show that the execution time can be drastically reduced when the appropriate allocation policy is used.