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Full-Text Articles in Computer Engineering
Managing Large Data Sets Using Support Vector Machines, Ranjini Srinivas
Managing Large Data Sets Using Support Vector Machines, Ranjini Srinivas
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for …
Improving Performance And Programmer Productivity For I/O-Intensive High Performance Computing Applications, Saba Sehrish
Improving Performance And Programmer Productivity For I/O-Intensive High Performance Computing Applications, Saba Sehrish
Electronic Theses and Dissertations
Due to the explosive growth in the size of scientific data sets, data-intensive computing is an emerging trend in computational science. HPC applications are generating and processing large amount of data ranging from terabytes (TB) to petabytes (PB). This new trend of growth in data for HPC applications has imposed challenges as to what is an appropriate parallel programming framework to efficiently process large data sets. In this work, we study the applicability of two programming models (MPI/MPI-IO and MapReduce) to a variety of I/O-intensive HPC applications ranging from simulations to analytics. We identify several performance and programmer productivity related …