Building a Bioinformatics Supercomputing Cluster

 in
Bioinformatics tools running in the OSCAR cluster environment turned 17 recycled PCs into a system that improves performance for user queries.
Conclusions and Results

Our local cluster is able to search an up-to-date database with fewer concurrent users and better overall throughput times than is the NCBI Web site. Simple wall-clock time trials were performed using our cluster and the NCBI Web site. We used eight simple queries consisting of protein and DNA sequences. A timer was started after submitting a query from the Web site and stopped once the results were displayed in the browser window. Trials on the NCBI Web site were performed at various times throughout the span of two weeks. All eight trials were averaged and compared to the cluster's times. The purpose of timing the query from the point of submission until the results are displayed was to observe times that an actual user would incur. On average, the cluster took less time to complete a query.

Figure 5. Our cluster, consisting of 17 recycled PCs, improves response times for users' queries.

Resources for this article: /article/8140.

Josh Stroschein (jstrosch@usd.edu) currently is pursuing his undergraduate degree in Computer Science and Criminal Justice. Josh is working on the cluster project through a grant at USD. He also works for Walton Internet Solutions, based in Vermillion, SD.

Doug Jennewein (djennewe@usd.edu) is a research analyst in Computer Science, and he has been with the USD since 1998. He received his Masters degree in Computer Science from USD in 2004. Doug's main research interest is high performance computing.

Joe Reynoldson (jreynold@usd.edu) is the research computing manager/instructor for the Computer Science Department, and he has been with USD since 1994. He received his Masters degree in Computer Science from USD in 1997. Joe teaches topics in Perl, systems management and Web development.

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