SharP
Abstract
The pre-exascale systems are expected to have a significant amount of hierarchical and heterogeneous on-node memory, and this trend of system architecture in extreme-scale systems is expected to continue into the exascale era. along with hierarchical-heterogeneous memory, the system typically has a high-performing network ad a compute accelerator. This system architecture is not only effective for running traditional High Performance Computing (HPC) applications (Big-Compute), but also for running data-intensive HPC applications and Big-Data applications. As a consequence, there is a growing desire to have a single system serve the needs of both Big-Compute and Big-Data applications. Though the system architecture supports the convergence of the Big-Compute and Big-Data, the programming models and software layer have yet to evolve to support either hierarchical-heterogeneous memory systems or the convergence. A programming abstraction to address this problem. The programming abstraction is implemented as a software library and runs on pre-exascale and exascale systems supporting current and emerging system architecture. Using distributed data-structures as a central concept, it provides (1) a simple, usable, and portable abstraction for hierarchical-heterogeneous memory and (2) a unified programming abstraction for Big-Compute and Big-Data applications.
- Authors:
-
- Oak Ridge National Laboratory
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1439574
- Report Number(s):
- SharP; 005721WKSTN00
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Software
- Software Revision:
- 00
- Software Package Number:
- 005721
- Software CPU:
- WKSTN
- Open Source:
- Yes
- Source Code Available:
- Yes
- Country of Publication:
- United States
Citation Formats
Venkata, Manjunath Gorentla, and Aderholdt, William F. SharP.
Computer software. https://www.osti.gov//servlets/purl/1439574. Vers. 00. USDOE. 18 Aug. 2017.
Web.
Venkata, Manjunath Gorentla, & Aderholdt, William F. (2017, August 18). SharP (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1439574.
Venkata, Manjunath Gorentla, and Aderholdt, William F. SharP.
Computer software. Version 00. August 18, 2017. https://www.osti.gov//servlets/purl/1439574.
@misc{osti_1439574,
title = {SharP, Version 00},
author = {Venkata, Manjunath Gorentla and Aderholdt, William F},
abstractNote = {The pre-exascale systems are expected to have a significant amount of hierarchical and heterogeneous on-node memory, and this trend of system architecture in extreme-scale systems is expected to continue into the exascale era. along with hierarchical-heterogeneous memory, the system typically has a high-performing network ad a compute accelerator. This system architecture is not only effective for running traditional High Performance Computing (HPC) applications (Big-Compute), but also for running data-intensive HPC applications and Big-Data applications. As a consequence, there is a growing desire to have a single system serve the needs of both Big-Compute and Big-Data applications. Though the system architecture supports the convergence of the Big-Compute and Big-Data, the programming models and software layer have yet to evolve to support either hierarchical-heterogeneous memory systems or the convergence. A programming abstraction to address this problem. The programming abstraction is implemented as a software library and runs on pre-exascale and exascale systems supporting current and emerging system architecture. Using distributed data-structures as a central concept, it provides (1) a simple, usable, and portable abstraction for hierarchical-heterogeneous memory and (2) a unified programming abstraction for Big-Compute and Big-Data applications.},
url = {https://www.osti.gov//servlets/purl/1439574},
doi = {},
url = {https://www.osti.gov/biblio/1439574},
year = {Fri Aug 18 00:00:00 EDT 2017},
month = {Fri Aug 18 00:00:00 EDT 2017},
note =
}