Abstract
Problem statement: The parallelization of multiple progressive alignment algorithms is a difficult task. All known methods have strong bottlenecks resulting from synchronization delays. This is even more constraining in distributed memory systems, where message passing also delays the interprocess communication. Despite these drawbacks, parallel computing is becoming increasingly necessary to perform multiple sequence alignment. Approach: In this study, it is introduced a solution for parallelizing multiple progressive alignments in distributed memory systems that overcomes such delays. Results: The proposed approach uses threads to separate actual alignment from synchronization and communication. It also uses a different approach to schedule independent tasks. Conclusion/Recommendations: The approach was intensively tested, producing a performance remarkably better than a largely used algorithm. It is suggested that it can be applied to improve the performance of some multiple alignment tools, as CLUSTALW and MUSCLE.
Highlights
An important field of bioinformatics is the analysis developed to support the genetic researches
Of amino acid sequences, which is the major form to the parallelism provided by them either are restricted to produce data about evolutionary processes
The solutions achieved by a progressive approach are not optimal, its computational cost is lower than the cost from exact methods
Summary
Development of faster alignment tools using high performance computing. Several such tools have been. Strategies acting in the node algorithm result at a reasonable execution time Another application of parallelism in communication but reducing the latency from data bioinformatics can be seen in (Bhandarkar et al, 1998), dependence, since a single tree node is dependent only where it is presented practical experience with the from its children. As a consequence of the data exchange composed by three components: the time to align pairs optimization, whenever a processor becomes idle, any of sequences (τalign), the time to transfer a pair of sequences to another host (τtransfer) and the time that host must wait for the sending hosts to become available to transmit (τsynch) This is represented in the following Eq 1: active task may be scheduled, independently from the locality of its input data.
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