NDLI: Servet: A benchmark suite for autotuning on multicore clusters

Content Provider	IEEE Xplore Digital Library
Author	Gonzalez-Dominguez, J. Taboada, G.L. Fraguela, B.B. Martin, M.J. Tourio, J.
Copyright Year	2010
Description	Author affiliation: Computer Architecture Group, Department of Electronics and Systems, University of A Coruña, Spain (Gonzalez-Dominguez, J.; Taboada, G.L.; Fraguela, B.B.; Martin, M.J.; Tourio, J.)
Abstract	The growing complexity in computer system hierarchies due to the increase in the number of cores per processor, levels of cache (some of them shared) and the number of processors per node, as well as the high-speed interconnects, demands the use of new optimization techniques and libraries that take advantage of their features. In this paper Servet, a suite of benchmarks focused on detecting a set of parameters with high influence in the overall performance of multicore systems, is presented. These benchmarks are able to detect the cache hierarchy, including their size and which caches are shared by each core, bandwidths and bottlenecks in memory accesses, as well as communication latencies among cores. These parameters can be used by auto-tuned codes to increase their performance in multicore clusters. Experimental results using different representative systems show that Servet provides very accurate estimates of the parameters of the machine architecture.
Starting Page	1
Ending Page	9
File Size	1026566
Page Count	9
File Format	PDF
ISBN	9781424464425
ISSN	15302075
e-ISBN	9781424464432
DOI	10.1109/IPDPS.2010.5470358
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multicore processing Hardware Computer architecture Delay Parameter estimation Libraries Bandwidth Clustering algorithms Parallel processing Parallel architectures Optimization Benchmarking Autotuning Multicore Clusters
Content Type	Text
Resource Type	Article

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