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dc.contributor.authorJaleel, Aamer
dc.contributor.authorMattina, Matthew
dc.contributor.authorJacob, Bruce
dc.date.accessioned2007-11-08T18:43:32Z
dc.date.available2007-11-08T18:43:32Z
dc.date.issued2006-02
dc.identifier.citation"Last-level cache (LLC) performance of data-mining workloads on a CMP--A case study of parallel bioinformatics workloads." Aamer Jaleel, Matthew Mattina, and Bruce Jacob. Proc. 12th International Symposium on High Performance Computer Architecture (HPCA 2006), Austin TX, February 2006.en
dc.identifier.urihttp://hdl.handle.net/1903/7453
dc.description.abstractWith the continuing growth in the amount of genetic data, members of the bioinformatics community are developing a variety of data-mining applications to understand the data and discover meaningful information. These applications are important in defining the design and performance decisions of future high performance microprocessors. This paper presents a detailed data-sharing analysis and chip-multiprocessor (CMP) cache study of several multithreaded data-mining bioinformatics workloads. For a CMP with a three-level cache hierarchy, we model the last-level of the cache hierarchy as either multiple private caches or a single cache shared amongst different cores of the CMP. Our experiments show that the bioinformatics workloads exhibit significant data-sharing—50–95% of the data cache is shared by the different threads of the workload. Furthermore, regardless of the amount of data cache shared, for some workloads, as many as 98% of the accesses to the last-level cache are to shared data cache lines. Additionally, the amount of data-sharing exhibited by the workloads is a function of the total cache size available—the larger the data cache the better the sharing behavior. Thus, partitioning the available last-level cache silicon area into multiple private caches can cause applications to lose their inherent data-sharing behavior. For the workloads in this study, a shared 32MB last-level cache is able to capture a tremendous amount of data-sharing and outperform a 32MB private cache configuration by several orders of magnitude. Specifically, with shared last-level caches, the bandwidth demands beyond the last-level cache can be reduced by factors of 3–625 when compared to private last-level caches.en
dc.format.extent377065 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.subjectbioinformaticsen
dc.subjectchip-multiprocessor (CMP)en
dc.subjectcache hierarchyen
dc.titleLast Level Cache (LLC) Performance of Data Mining Workloads On a CMP — Case Study of Parallel Bioinformatics Workloadsen
dc.typePresentationen
dc.relation.isAvailableAtA. James Clark School of Engineeringen_us
dc.relation.isAvailableAtElectrical & Computer Engineeringen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us


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