Abstract This paper describes a distributed implementation of PROuST, a method for protein structure comparison, that involves a major restructuring of the application for an efficient grid immersion. PROuST consists of several components that perform different tasks at different stages. Given a target protein, an index-based search retrieves from a database a list of proteins that are good candidates for similarity, then a dynamic programming algorithm aligns the target protein with each candidate protein. The same geometric properties of secondary structure elements of proteins are used by different components of PROuST. Thus, an important issue of the distributed implementation is data transfer vs. data recomputation tradeoffs. Our implementation avoids recomputation by re-using the hash table data as much as possible, once they are accessed. The algorithmic changes to the application allow to reduce the number of data accesses to storage elements and consequently the execution time. In addition this paper discusses data replication strategies on a grid environment to optimize the data transfer time

Application Re-Structuring and Data Management on a GRID Environment: a Case Study for Bioinformatics

COMIN, MATTEO;
2006

Abstract

Abstract This paper describes a distributed implementation of PROuST, a method for protein structure comparison, that involves a major restructuring of the application for an efficient grid immersion. PROuST consists of several components that perform different tasks at different stages. Given a target protein, an index-based search retrieves from a database a list of proteins that are good candidates for similarity, then a dynamic programming algorithm aligns the target protein with each candidate protein. The same geometric properties of secondary structure elements of proteins are used by different components of PROuST. Thus, an important issue of the distributed implementation is data transfer vs. data recomputation tradeoffs. Our implementation avoids recomputation by re-using the hash table data as much as possible, once they are accessed. The algorithmic changes to the application allow to reduce the number of data accesses to storage elements and consequently the execution time. In addition this paper discusses data replication strategies on a grid environment to optimize the data transfer time
2006
Proceedings of IEEE International Workshop on High Performance Computational Biology
Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International
9781424400546
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2472206
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