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Exploiting parallel R in the cloud with SPRINT.

M Piotrowski¹, G A McGilvary, T M Sloan

¹EPCC, The University of Edinburgh, Edinburgh, United Kingdom.

Methods of Information in Medicine

|December 11, 2012

Summary

This summary is machine-generated.

High Performance Computing (HPC) in the cloud, using the SPRINT R package on Amazon EC2, offers scalable performance for genomics research. Resource underutilization can improve results, making cloud computing an accessible alternative for organizations with limited budgets.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

DNA sequencing and microarray technologies generate vast amounts of data.
High Performance Computing (HPC) in the cloud provides a cost-effective solution for analyzing large genomic datasets.
The R statistical language lacks native support for multi-processor architectures.

Purpose of the Study:

To evaluate setting up and running SPRINT-enabled genomic analyses on Amazon Elastic Compute Cloud (EC2).
To assess the benefits of submitting applications to EC2 from diverse geographical locations.
To determine if resource underutilization can enhance application performance.

Main Methods:

Benchmarking SPRINT parallel implementations (correlation, permutation testing, partitioning around medoids, papply) on Amazon EC2 with varying dataset sizes.
Submitting jobs from the UK and Thailand to analyze cost variations.
Utilizing Amazon EC2 for cloud-based high-throughput genomic data analysis.

Main Results:

Achieving scalable performance, with improvements varying by algorithm.
Demonstrating that resource underutilization can decrease analysis time.
Identifying that end-user location influences costs due to taxation.

Conclusions:

Amazon EC2 and cloud computing present a viable alternative for HPC needs, especially for smaller organizations.
Cloud computing offers new opportunities for genomics research with limited financial resources.
SPRINT facilitates access to HPC for genomics researchers using cloud platforms.