Exploiting Data Similarity to Reduce Memory Footprints

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Memory size has long limited large-scale applications on high-performance computing (HPC) systems. Since compute nodes frequently do not have swap space, physical memory often limits problem sizes. Increasing core counts per chip and power density constraints, which limit the number of DIMMs per node, have exacerbated this problem. Further, DRAM constitutes a significant portion of overall HPC system cost. Therefore, instead of adding more DRAM to the nodes, mechanisms to manage memory usage more efficiently - preferably transparently - could increase effective DRAM capacity and thus the benefit of multicore nodes for HPC systems. MPI application processes often exhibit significant ... continued below

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Biswas, S; de Supinski, B R; Schulz, M; Franklin, D; Sherwood, T & Chong, F T January 28, 2011.

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Memory size has long limited large-scale applications on high-performance computing (HPC) systems. Since compute nodes frequently do not have swap space, physical memory often limits problem sizes. Increasing core counts per chip and power density constraints, which limit the number of DIMMs per node, have exacerbated this problem. Further, DRAM constitutes a significant portion of overall HPC system cost. Therefore, instead of adding more DRAM to the nodes, mechanisms to manage memory usage more efficiently - preferably transparently - could increase effective DRAM capacity and thus the benefit of multicore nodes for HPC systems. MPI application processes often exhibit significant data similarity. These data regions occupy multiple physical locations across the individual rank processes within a multicore node and thus offer a potential savings in memory capacity. These regions, primarily residing in heap, are dynamic, which makes them difficult to manage statically. Our novel memory allocation library, SBLLmalloc, automatically identifies identical memory blocks and merges them into a single copy. SBLLmalloc does not require application or OS changes since we implement it as a user-level library. Overall, we demonstrate that SBLLmalloc reduces the memory footprint of a range of MPI applications by 32.03% on average and up to 60.87%. Further, SBLLmalloc supports problem sizes for IRS over 21.36% larger than using standard memory management techniques, thus significantly increasing effective system size. Similarly, SBLLmalloc requires 43.75% fewer nodes than standard memory management techniques to solve an AMG problem.

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PDF-file: 14 pages; size: 3.3 Mbytes

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  • Presented at: 25th IEEE International Parallel & Distributed Processing Symposium, Anchorage, AK, United States, May 16 - May 20, 2011

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  • Report No.: LLNL-CONF-468331
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 1018746
  • Archival Resource Key: ark:/67531/metadc845168

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • January 28, 2011

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  • May 19, 2016, 3:16 p.m.

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  • Nov. 28, 2016, 2:21 p.m.

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Biswas, S; de Supinski, B R; Schulz, M; Franklin, D; Sherwood, T & Chong, F T. Exploiting Data Similarity to Reduce Memory Footprints, article, January 28, 2011; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc845168/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.