ACM SIGOPS Operating Systems Review
Designing memory consistency models for shared-memory multiprocessors
Designing memory consistency models for shared-memory multiprocessors
In search of clusters: the coming battle in lowly parallel computing
In search of clusters: the coming battle in lowly parallel computing
Scope consistency: a bridge between release consistency and entry consistency
Proceedings of the eighth annual ACM symposium on Parallel algorithms and architectures
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
Memory consistency and event ordering in scalable shared-memory multiprocessors
ISCA '90 Proceedings of the 17th annual international symposium on Computer Architecture
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
JIAJIA: A Software DSM System Based on a New Cache Coherence Protocol
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Shared virtual memory on loosely coupled multiprocessors
Shared virtual memory on loosely coupled multiprocessors
Using a DSM application to locally align DNA sequences
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
An FPGA-based accelerator for multiple biological sequence alignment with DIALIGN
HiPC'07 Proceedings of the 14th international conference on High performance computing
A data parallel strategy for aligning multiple biological sequences on multi-core computers
Computers in Biology and Medicine
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Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison is a basic operation in DNA sequencing projects, and most sequence comparison methods currently in use are based on heuristics, which are faster but offer no guarantees of producing the best alignments possible. In order to alleviate this problem, Smith-Waterman proposed an algorithm. This algorithm obtains the best local alignments but at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments involving three strategies to run the Smith-Waterman algorithm in a cluster of workstations using a Distributed Shared Memory System. Our results on an eight-machine cluster presented very good speed-up and indicate that impressive improvements can be achieved depending on the strategy used. In addition, we present a number of theoretical remarks concerning how to reduce the amount of memory used.