INCREASING THE EFFICIENCY OF PDS DATA PROCESSING

$28,708P41FY2010RRNIH

Cornell University, Ithaca NY

Investigators

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Abstract

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. A large increase in the amount of experimental data that is to follow the increased throughput will require reliable backup and data storage, as well as an adequate data processing capacity. Our data processing software involves the Maximum Entropy Method, applied to data from several samples, which requires substantial computational resources. Just a simulation of a single such data set takes several hours of eight quad-processor nodes. With the future development of more efficient simulation algorithms, we hope be able to analyze the respective experimental data using a computer cluster of comparable size or else to employ one or two powerful systems supporting multiple GPUs (graphic processor based computational units).

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