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HIPC
2009
Springer

Comparing the performance of clusters, Hadoop, and Active Disks on microarray correlation computations

8 years 3 months ago
Comparing the performance of clusters, Hadoop, and Active Disks on microarray correlation computations
Abstract--Microarray-based comparative genomic hybridization (aCGH) offers an increasingly fine-grained method for detecting copy number variations in DNA. These copy number variations can directly influence the expression of the proteins that are encoded in the genes in question. A useful analysis of the data produced from these microarray experiments is pairwise correlation. However, the high resolution of today's microarray technology requires that supercomputing computation and storage resources be leveraged in order to perform this analysis. This application is an exemplar of the class of data intensive problems which require high-throughput I/O in order to be tractable. Although the performance of these types of applications on a cluster can be improved by parallelization, storage hardware and network limitations restrict the scalability of an I/O-bound application such as this. The Hadoop software framework is designed to enable data-intensive applications on cluster archit...
Jeffrey A. Delmerico, Nathanial A. Byrnes, Andrew
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where HIPC
Authors Jeffrey A. Delmerico, Nathanial A. Byrnes, Andrew E. Brunoh, Matthew D. Jones, Steven M. Gallo, Vipin Chaudhary
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