Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets
| Autor: | Deepti D. Shrimankar, S. R. Sathe |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Speedup Computer science biosequences Parallel algorithm Parallel computing Biochemistry 03 medical and health sciences parallel programming model parallel algorithm cluster Molecular Biology lcsh:QH301-705.5 Multi-core processor Analysis of parallel algorithms Applied Mathematics Message passing Methodology SMP Supercomputer Computer Science Applications Computational Mathematics 030104 developmental biology lcsh:Biology (General) Parallel programming model Programming paradigm |
| Zdroj: | Bioinformatics and Biology Insights Bioinformatics and Biology Insights, Vol 2016, Iss 10, Pp 255-265 (2016) Bioinformatics and Biology Insights, Vol 10 (2016) |
| ISSN: | 1177-9322 |
| Popis: | Sequence alignment is an important tool for describing the relationships between DNA sequences. Many sequence alignment algorithms exist, differing in efficiency, in their models of the sequences, and in the relationship between sequences. The focus of this study is to obtain an optimal alignment between two sequences of biological data, particularly DNA sequences. The algorithm is discussed with particular emphasis on time, speedup, and efficiency optimizations. Parallel programming presents a number of critical challenges to application developers. Today's supercomputer often consists of clusters of SMP nodes. Programming paradigms such as OpenMP and MPI are used to write parallel codes for such architectures. However, the OpenMP programs cannot be scaled for more than a single SMP node. However, programs written in MPI can have more than single SMP nodes. But such a programming paradigm has an overhead of internode communication. In this work, we explore the tradeoffs between using OpenMP and MPI. We demonstrate that the communication overhead incurs significantly even in OpenMP loop execution and increases with the number of cores participating. We also demonstrate a communication model to approximate the overhead from communication in OpenMP loops. Our results are astonishing and interesting to a large variety of input data files. We have developed our own load balancing and cache optimization technique for message passing model. Our experimental results show that our own developed techniques give optimum performance of our parallel algorithm for various sizes of input parameter, such as sequence size and tile size, on a wide variety of multicore architectures. |
| Databáze: | OpenAIRE |
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