Evaluation of SMP shared memory machines for use with in-memory and OpenMP big data applications. Younge, A., J., Reidy, C., Henschel, R., & Fox, G., C. In Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016, pages 1597-1606, 2016. Institute of Electrical and Electronics Engineers Inc..
Evaluation of SMP shared memory machines for use with in-memory and OpenMP big data applications [link]Website  doi  abstract   bibtex   
While distributed memory systems have shaped the field of distributed systems for decades, the demand for many-core shared memory resources is increasing. Symmetric Multiprocessor Systems (SMPs) have become increasingly important recently among a wide array of disciplines, rangingfrom Bioinformatics to astrophysics, and beyond. With the increase in big data computing, the size and scope of traditional commodity server systems is often outpaced. While some big data applications can be mapped to distributed memory systems found through many cluster and cloud technologies today, this effort represents a large barrier of entry that some projects cannot cross. Shared memory SMP systems look to effectively and efficiently fill this niche within distributed systems by providing high throughput and performance with minimized development effort, as the computing environment often represents what many researchers are already familiar with. In this paper, we look at the use of two common shared memory systems, the ScaleMP vSMP virtualized SMP deployment at Indiana University, and the SGI UV architecture deployed at University of Arizona. While both systems are notably different in their design, their potential impact on computing is remarkably similar. As such, we look to compare each system first under a set of OpenMP threaded benchmarks via the SPEC group, and to follow up with our experience using each machine for Trinity de-novo assembly. We find both SMP systems are well suited to support various big data applications, with the newer vSMP deployment often slightly faster, however, certain caveats and performance considerations are necessary when considering such SMP systems. © 2016 IEEE.
@inproceedings{
 title = {Evaluation of SMP shared memory machines for use with in-memory and OpenMP big data applications},
 type = {inproceedings},
 year = {2016},
 keywords = {Application programming interfaces (API),Astrophy,Distributed computer systems,SGI UV,ScaleMP,Symmetric multi processing,Virt},
 pages = {1597-1606},
 websites = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991677893&doi=10.1109%2FIPDPSW.2016.133&partnerID=40&md5=f5680d15f3df0b6086275edcd9a8457e},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 id = {04133db5-1d44-3127-ad88-37582054a599},
 created = {2019-10-01T17:21:28.041Z},
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 citation_key = {Younge20161597},
 source_type = {conference},
 notes = {cited By 0; Conference of 30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 ; Conference Date: 23 May 2016 Through 27 May 2016; Conference Code:122812},
 folder_uuids = {22c3b665-9e84-4884-8172-710aa9082eaf},
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 abstract = {While distributed memory systems have shaped the field of distributed systems for decades, the demand for many-core shared memory resources is increasing. Symmetric Multiprocessor Systems (SMPs) have become increasingly important recently among a wide array of disciplines, rangingfrom Bioinformatics to astrophysics, and beyond. With the increase in big data computing, the size and scope of traditional commodity server systems is often outpaced. While some big data applications can be mapped to distributed memory systems found through many cluster and cloud technologies today, this effort represents a large barrier of entry that some projects cannot cross. Shared memory SMP systems look to effectively and efficiently fill this niche within distributed systems by providing high throughput and performance with minimized development effort, as the computing environment often represents what many researchers are already familiar with. In this paper, we look at the use of two common shared memory systems, the ScaleMP vSMP virtualized SMP deployment at Indiana University, and the SGI UV architecture deployed at University of Arizona. While both systems are notably different in their design, their potential impact on computing is remarkably similar. As such, we look to compare each system first under a set of OpenMP threaded benchmarks via the SPEC group, and to follow up with our experience using each machine for Trinity de-novo assembly. We find both SMP systems are well suited to support various big data applications, with the newer vSMP deployment often slightly faster, however, certain caveats and performance considerations are necessary when considering such SMP systems. © 2016 IEEE.},
 bibtype = {inproceedings},
 author = {Younge, A J and Reidy, C and Henschel, R and Fox, Geoffrey Charles},
 doi = {10.1109/IPDPSW.2016.133},
 booktitle = {Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016}
}
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