INCREASING THE EFFICIENCY OF PDS DATA PROCESSING

Cornell University, Ithaca NY

Investigators

Linked publications & trials

Abstract

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. 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).