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Tools for Memory Hierarchy Optimization on Pre-Exascale HPC Architectures

Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA118P0028
Agency Tracking Number: T172-003-0052
Amount: $149,984.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DTRA172-003
Solicitation Number: 2017.2
Timeline
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-04-04
Award End Date (Contract End Date): 2018-11-03
Small Business Information
12121 Scripps Summit Dr
San Diego, CA 92131
United States
DUNS: 962467747
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ananta Tiwari
 (858) 695-9027
 ananta.tiwari@epanalytics.com
Business Contact
 Ronald Hawkins
Phone: (858) 695-9027
Email: ronald.hawkins@epanalytics.com
Research Institution
N/A
Abstract

DTRA uses High Fidelity Computer Codes (HFCC) to investigate weapon effects and techniques for countering Weapons of Mass Destruction (WMD). End-to-end HFCC simulations in support of the DTRA Agent Defeat Warfighter Capability will require calculations including multiple phenomena that occur in vastly different time scales (-sec to hours). As DTRA becomes increasingly reliant on computational modeling and simulation for these security-critical tasks, efficient use of existing computational resources and planning and optimizing for next generation architectures becomes paramount. HFCCs typically are memory-bound codes and much CPU time is wasted waiting for the data needed to perform a computation. Emerging innovations in memory technology that mitigate memory performance issues, therefore, come at a critical time and optimizing HFCCs for pre-exascale systems that feature such technologies must start now. To enable such optimizations, this project proposes to build the MemInsight tool for analyzing application memory address streams of HFCCs to identify access patterns, the extent to which different levels of memory are exploited, memory footprint, and key data structures. The resultant data forms the basis for issuing guidance for HFCC refactoring and optimization.

* Information listed above is at the time of submission. *

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