{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81953"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81953","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient Resource Utilization for Parallel I /O in Cluster Environments","abstract":"In this thesis work, performance factors for parallel I/O on clusters are examined and several algorithms are designed to support parallel I/O efficiently for scientific applications running on commodity clusters, making better utilization of system resources. Specifically, our algorithms are designed to (i) minimize data transfer over the network during I/O if network bandwidth is limited, (ii) reduce message passing latency during I/O of finely-distributed data, (iii) place I/O servers on the appropriate processors in heterogeneous environments, and (iv) balance I/O workload dynamically when necessary. These algorithms have been implemented in the Panda parallel I/O library and tested on several actual and simulated cluster environments. Performance results show that our algorithms improve overall parallel I/O performance significantly with only an insignificant amount of overhead. 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