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2016 (English)In: Procedia Computer Science, Elsevier, 2016, p. 98-107Conference paper, Published paper (Refereed)
Abstract [en]
Streaming computing models allow for on-the-y processing of large data sets. With the increased demand for processing large amount of data in a reasonable period of time, streaming models are more and more used on supercomputers to solve data-intensive problems. Because supercomputers have been mainly used for compute-intensive workload, supercomputer performance metrics focus on the number of oating point operations in time and cannot fully characterize a streaming application performance on supercomputers. We introduce the injection and processing rates as the main metrics to characterize the performance of streaming computing on supercomputers. We analyze the dynamics of these quantities in a modi ed STREAM benchmark developed atop of an MPI streaming library in a series of di erent congurations. We show that after a brief transient the injection and processing rates converge to sustained rates. We also demonstrate that streaming computing performance strongly depends on the number of connections between data producers and consumers and on the processing task granularity.
Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Big data, Data-driven applications, High-performance computing, Streaming computing, Data handling, Supercomputers, Computing performance, High performance computing, Performance characterization, Performance metrics, Processing rates, Streaming applications, Task granularity
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-195477 (URN)10.1016/j.procs.2016.05.301 (DOI)000579452200009 ()2-s2.0-84978536252 (Scopus ID)
Conference
International Conference on Computational Science, ICCS 2016, 6 June 2016 through 8 June 2016
Note
Funding Details: 671500, EC, European Commission
QC 20161125
2016-11-252016-11-032024-01-15Bibliographically approved