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UID:submissions.supercomputing.org_SC20_sess171_pap224@linklings.com
SUMMARY:MoHA: A Composable System for Efficient In-Situ Analytics on Heter
 ogeneous HPC Systems
DESCRIPTION:Paper\n\nMoHA: A Composable System for Efficient In-Situ Analy
 tics on Heterogeneous HPC Systems\n\nXing, Agrawal, Ramnath\n\nHeterogeneo
 us, dense computing architectures consisting of several accelerators, such
  as GPUs, attached to general-purpose CPUs are now integral to high-perfor
 mance computing (HPC) systems.  These architectures, however, pose severe 
 memory and I/O constraints to computations involving in-situ analytics.  T
 his paper introduces MoHA, a framework for in-situ analytics that is desig
 ned to efficiently use the limited resources available on heterogeneous pl
 atforms. MoHA achieves this efficiency through the extensive use of bitmap
 s as a compressed or summary representation of simulation outputs. Our spe
 cific contributions in this paper include the design of bitmap generation 
 and storage methods suitable for GPUs, the design and efficient implementa
 tion of a set of key operators for MoHA and demonstrations of how several 
 real queries on real datasets can be implemented using these operators.  W
 e demonstrate that MoHA reduces I/O transfer as well as overall processing
  time when compared to a baseline that does not use compressed representat
 ions.\n\nTag: Data Analytics, Compression, and Management, Performance/Pro
 ductivity Measurement and Evaluation, Resource Management and Scheduling\n
 \nRegistration Category: Tech Program Reg Pass
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