Understanding and improving computational science storage access through continuous characterization

Authors:
Philip Carns;Kevin Harms;William Allcock;Charles Bacon;Samuel Lang;Robert Latham;Robert Ross
Affiliations:
Mathematics and Computer Science Division, Argonne National Laboratory, IL 60439, USA;Argonne Leadership Computing Facility, Argonne National Laboratory, IL 60439, USA;Argonne Leadership Computing Facility, Argonne National Laboratory, IL 60439, USA;Argonne Leadership Computing Facility, Argonne National Laboratory, IL 60439, USA;Mathematics and Computer Science Division, Argonne National Laboratory, IL 60439, USA;Mathematics and Computer Science Division, Argonne National Laboratory, IL 60439, USA;Mathematics and Computer Science Division, Argonne National Laboratory, IL 60439, USA
Venue:
MSST '11 Proceedings of the 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies
Year:
2011

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Abstract

Computational science applications are driving a demand for increasingly powerful storage systems. While many techniques are available for capturing the I/O behavior of individual application trial runs and specific components of the storage system, continuous characterization of a production system remains a daunting challenge for systems with hundreds of thousands of compute cores and multiple petabytes of storage. As a result, these storage systems are often designed without a clear understanding of the diverse computational science workloads they will support.