Cluster Computing using Intel Concurrent Collections
| dc.contributor.author | Mckissack, Randal | |
| dc.date.accessioned | 2018-10-23T15:45:42Z | |
| dc.date.available | 2018-10-23T15:45:42Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | The Intel Corporation is developing a new parallel software and compiler called Concurrent Collections (CnC) to make programming in parallel easier for the user. CnC provides a system of collections comprised of steps, items, and tags. A CnC user specifies their algorithm in a graph representation using these constructs. Using this graph of dependencies, CnC automatically identifies parallelizable code segments and executes code in parallel. The present work focuses on the distributed version of CnC, where parallel code is run across multiple compute nodes. Specific accomplishments included getting distributed CnC working on the cluster tara in the UMBC High Performance Computing Facility, running timing tests, analyzing the data, and creating a generalized portable version of the distributed CnC code. This work allows a user in the distributed mode to have independent control over the number of threads, cores, and nodes to be used by a program. Several performance studies were ran in order to analyze the efficiency of the parallelism. Results for a parameter study show that Distributed CnC achieves a near-ideal speed-up for an increasing number of nodes. | en_US |
| dc.description.sponsorship | Interdisciplinary Program in High Performance Computing (www.umbc.edu/hpcreu) in the UMBC Department of Mathematics and Statistics with fellow undergraduates Richard Adjogah and Ekene Sibeudu. We were supported by a grant to UMBC from the National Security Agency (NSA) through the Meyerhoff Scholarship Program. UMBC, the Department of Mathematics and Statistics, the Center for Interdisciplinary Research and Consulting (CIRC), and HPCF also supported this program. The computational hardware in HPCF is partially funded by the National Science Foundation through the MRI program (grant no. CNS–0821258) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from UMBC. | en_US |
| dc.description.uri | https://userpages.umbc.edu/~gobbert/papers/MckissackThesis2012.pdf | en_US |
| dc.format.extent | 10 pages | en_US |
| dc.genre | senior thesis | en_US |
| dc.identifier | doi:10.13016/M2TX3598H | |
| dc.identifier.uri | http://hdl.handle.net/11603/11657 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Mathematics Department Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | Intel | en_US |
| dc.subject | Cluster Computing | en_US |
| dc.subject | Concurrent Collections (CnC) | en_US |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | en_US |
| dc.title | Cluster Computing using Intel Concurrent Collections | en_US |
| dc.type | Text | en_US |