Abstract
High-performance computing facilities used for scientific computing draw enormous energy, some of them consuming many megawatt-hours. Saving the energy consumption of computations on such facilities can dramatically reduce the total cost of their operation and help reduce environmental effects. Here, we focus on a way to reduce energy consumption in many ensembles of simulations. Using the method of simulation cloning to exploit parallelism while also significantly conserving the computational and memory requirements, we perform a detailed empirical study of energy consumed on a large supercomputer consisting of hardware accelerator cards (graphical processing units, GPUs). We build on previous insights from mathematical analysis and implementation of cloned simulations that result in computational and memory savings by several orders-of-magnitude. Using instrumentation to track the power drawn by thousands of accelerator cards, we report significant aggregate energy savings from cloned simulations.
Original language | English |
---|---|
Title of host publication | 2019 Winter Simulation Conference, WSC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2572-2582 |
Number of pages | 11 |
ISBN (Electronic) | 9781728132839 |
DOIs | |
State | Published - Dec 2019 |
Event | 2019 Winter Simulation Conference, WSC 2019 - National Harbor, United States Duration: Dec 8 2019 → Dec 11 2019 |
Publication series
Name | Proceedings - Winter Simulation Conference |
---|---|
Volume | 2019-December |
ISSN (Print) | 0891-7736 |
Conference
Conference | 2019 Winter Simulation Conference, WSC 2019 |
---|---|
Country/Territory | United States |
City | National Harbor |
Period | 12/8/19 → 12/11/19 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.