DC Field | Value | Language |
---|---|---|
dc.contributor.author | BAE, HANYEOREUM | ko |
dc.contributor.author | Kwon, Miryeong | ko |
dc.contributor.author | Gouk, Donghyun | ko |
dc.contributor.author | Han, Sanghyun | ko |
dc.contributor.author | LEE, CHANGRIM | ko |
dc.contributor.author | Park, Dongchul | ko |
dc.contributor.author | Jung, Myoungsoo | ko |
dc.date.accessioned | 2021-11-10T06:47:23Z | - |
dc.date.available | 2021-11-10T06:47:23Z | - |
dc.date.created | 2021-09-05 | - |
dc.date.created | 2021-09-05 | - |
dc.date.created | 2021-09-05 | - |
dc.date.created | 2021-09-05 | - |
dc.date.issued | 2021-10-24 | - |
dc.identifier.citation | 39th IEEE International Conference on Computer Design ICCD), pp.316 - 320 | - |
dc.identifier.issn | 1063-6404 | - |
dc.identifier.uri | http://hdl.handle.net/10203/289092 | - |
dc.description.abstract | We investigate runtime environment characteristics and explore the challenges of conventional in-memory graph processing. This system-level analysis includes empirical results and observations, which are opposite to the existing expectations of graph application users. Specifically, since raw graph data are not the same as the in-memory graph data, processing a billion-scale graph exhausts all system resources and makes the target system unavailable due to out-of-memory at runtime. To address a lack of memory space problem for big-scale graph analysis, we configure real persistent memory devices (PMEMs) with different operation modes and system software frameworks. In this work, we introduce PMEM to a representative in-memory graph system, Ligra, and perform an in-depth analysis uncovering the performance behaviors of different PMEM-applied inmemory graph systems. Based on our observations, we modify Ligra to improve the graph processing performance with a solid level of data persistence. Our evaluation results reveal that Ligra, with our simple modification, exhibits 4.41 x and 3.01 x better performance than the original Ligra running on a virtual memory expansion and conventional persistent memory, respectively. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Empirical Guide to Use of Persistent Memory for Large-Scale In-Memory Graph Analysis | - |
dc.type | Conference | - |
dc.identifier.wosid | 000763821700046 | - |
dc.identifier.scopusid | 2-s2.0-85123941556 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 316 | - |
dc.citation.endingpage | 320 | - |
dc.citation.publicationname | 39th IEEE International Conference on Computer Design ICCD) | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/ICCD53106.2021.00057 | - |
dc.contributor.localauthor | Jung, Myoungsoo | - |
dc.contributor.nonIdAuthor | Han, Sanghyun | - |
dc.contributor.nonIdAuthor | Park, Dongchul | - |
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