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Title: Partnership For Edge Physics Simulation

Abstract

In this effort, we will extend our prior work as part of CPES (i.e., DART and DataSpaces) to support in-situ tight coupling between application codes that exploits data locality and core-level parallelism to maximize on-chip data exchange and reuse. This will be accomplished by mapping coupled simulations so that the data exchanges are more localized within the nodes. Coupled simulation workflows can more effectively utilize the resources available on emerging HEC platforms if they can be mapped and executed to exploit data locality as well as the communication patterns between application components. Scheduling and running such workflows requires an extended framework that should provide a unified hybrid abstraction to enable coordination and data sharing across computation tasks that run on the heterogeneous multi-core-based systems, and develop a data-locality based dynamic tasks scheduling approach to increase on-chip or intra-node data exchanges and in-situ execution. This effort will extend our prior work as part of CPES (i.e., DART and DataSpaces), which provided a simple virtual shared-space abstraction hosted at the staging nodes, to support application coordination, data sharing and active data processing services. Moreover, it will transparently manage the low-level operations associated with the inter-application data exchange, such as data redistributions, andmore » will enable running coupled simulation workflow on multi-cores computing platforms.« less

Authors:
 [1]
  1. Rutgers Univ., New Brunswick, NJ (United States)
Publication Date:
Research Org.:
Rutgers Univ., New Brunswick, NJ (United States)
Sponsoring Org.:
ASCR; USDOE
OSTI Identifier:
1430730
Report Number(s):
SC0008455
DOE Contract Number:
SC0008455
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats

Parashar, Manish. Partnership For Edge Physics Simulation. United States: N. p., 2018. Web. doi:10.2172/1430730.
Parashar, Manish. Partnership For Edge Physics Simulation. United States. doi:10.2172/1430730.
Parashar, Manish. Mon . "Partnership For Edge Physics Simulation". United States. doi:10.2172/1430730. https://www.osti.gov/servlets/purl/1430730.
@article{osti_1430730,
title = {Partnership For Edge Physics Simulation},
author = {Parashar, Manish},
abstractNote = {In this effort, we will extend our prior work as part of CPES (i.e., DART and DataSpaces) to support in-situ tight coupling between application codes that exploits data locality and core-level parallelism to maximize on-chip data exchange and reuse. This will be accomplished by mapping coupled simulations so that the data exchanges are more localized within the nodes. Coupled simulation workflows can more effectively utilize the resources available on emerging HEC platforms if they can be mapped and executed to exploit data locality as well as the communication patterns between application components. Scheduling and running such workflows requires an extended framework that should provide a unified hybrid abstraction to enable coordination and data sharing across computation tasks that run on the heterogeneous multi-core-based systems, and develop a data-locality based dynamic tasks scheduling approach to increase on-chip or intra-node data exchanges and in-situ execution. This effort will extend our prior work as part of CPES (i.e., DART and DataSpaces), which provided a simple virtual shared-space abstraction hosted at the staging nodes, to support application coordination, data sharing and active data processing services. Moreover, it will transparently manage the low-level operations associated with the inter-application data exchange, such as data redistributions, and will enable running coupled simulation workflow on multi-cores computing platforms.},
doi = {10.2172/1430730},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Apr 02 00:00:00 EDT 2018},
month = {Mon Apr 02 00:00:00 EDT 2018}
}

Technical Report:

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