Multiphysics coupling in the Exascale computing project

2021 ◽  
pp. 109434202110289
Author(s):  
Thomas M Evans ◽  
Julia C White
Keyword(s):  
High Performance ◽  
Computational Science ◽  
Department Of Energy ◽  
Special Issue ◽  
Computational Accuracy ◽  
Exascale Computing ◽  
The Us ◽  
Multiphysics Coupling ◽  
And Performance ◽  
Performance Computing

Multiphysics coupling presents a significant challenge in terms of both computational accuracy and performance. Achieving high performance on coupled simulations can be particularly challenging in a high-performance computing context. The US Department of Energy Exascale Computing Project has the mission to prepare mission-relevant applications for the delivery of the exascale computers starting in 2023. Many of these applications require multiphysics coupling, and the implementations must be performant on exascale hardware. In this special issue we feature six articles performing advanced multiphysics coupling that span the computational science domains in the Exascale Computing Project.

scholarly journals Preparing sparse solvers for exascale computing

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190053
Author(s):  
Hartwig Anzt ◽  
Erik Boman ◽  
Rob Falgout ◽  
Pieter Ghysels ◽  
Michael Heroux ◽  
...  
Keyword(s):  
High Performance ◽  
Numerical Algorithms ◽  
Computational Science ◽  
Department Of Energy ◽  
High Fidelity ◽  
Scientific Applications ◽  
Exascale Computing ◽  
Multi Scale ◽  
The Us ◽  
Computing Platforms

Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

scholarly journals Special Issue on Automatic Application Tuning for HPC Architectures

2014 ◽  
Vol 22 (4) ◽  
pp. 259-260 ◽  
Author(s):  
Siegfried Benkner ◽  
Franz Franchetti ◽  
Hans Michael Gerndt ◽  
Jeffrey K. Hollingsworth
Keyword(s):  
High Performance ◽  
Research Field ◽  
Performance Tuning ◽  
Full Potential ◽  
Research Groups ◽  
Special Issue ◽  
The World ◽  
And Performance ◽  
Application Tuning ◽  
Performance Computing

High Performance Computing architectures have become incredibly complex and exploiting their full potential is becoming more and more challenging. As a consequence, automatic performance tuning (autotuning) of HPC applications is of growing interest and many research groups around the world are currently involved. Autotuning is still a rapidly evolving research field with many different approaches being taken. This special issue features selected papers presented at the Dagstuhl seminar on “Automatic Application Tuning for HPC Architectures” in October 2013, which brought together researchers from the areas of autotuning and performance analysis in order to exchange ideas and steer future collaborations.

scholarly journals Exascale applications: skin in the game

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190056 ◽  
Author(s):  
Francis Alexander ◽  
Ann Almgren ◽  
John Bell ◽  
Amitava Bhattacharjee ◽  
Jacqueline Chen ◽  
...  
Keyword(s):  
High Performance ◽  
Data Science ◽  
Scientific Discovery ◽  
Numerical Algorithms ◽  
Department Of Energy ◽  
High Confidence ◽  
The Us ◽  
The Usa ◽  
Critical Problems ◽  
Performance Computing

As noted in Wikipedia, skin in the game refers to having ‘incurred risk by being involved in achieving a goal’, where ‘ skin is a synecdoche for the person involved, and game is the metaphor for actions on the field of play under discussion’. For exascale applications under development in the US Department of Energy Exascale Computing Project, nothing could be more apt, with the skin being exascale applications and the game being delivering comprehensive science-based computational applications that effectively exploit exascale high-performance computing technologies to provide breakthrough modelling and simulation and data science solutions. These solutions will yield high-confidence insights and answers to the most critical problems and challenges for the USA in scientific discovery, national security, energy assurance, economic competitiveness and advanced healthcare. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

2021 ◽  
pp. 109434202110293
Author(s):  
Francis J Alexander ◽  
James Ang ◽  
Jenna A Bilbrey ◽  
Jan Balewski ◽  
Tiernan Casey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
New Technologies ◽  
Scientific Discovery ◽  
Language Translation ◽  
Learning Technologies ◽  
Department Of Energy ◽  
Experimental Science ◽  
Exascale Computing ◽  
Self Driving Cars

Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

Loop Transient Performance With a Closed Loop sCO2 Brayton Cycle

2021 ◽  
Author(s):  
Stefan D. Cich ◽  
J. Jeffrey Moore ◽  
Meera Day Towler ◽  
Jason Mortzheim
Keyword(s):  
Atmospheric Pressure ◽  
Closed Loop ◽  
Operating Conditions ◽  
Department Of Energy ◽  
Brayton Cycle ◽  
Transient Performance ◽  
Start Up ◽  
The Us ◽  
Successful Testing ◽  
And Performance

Abstract Recent testing has been completed on a 1 MWe supercritical carbon dioxide (sCO2) closed loop recuperated cycle under funding from the US Department of Energy (DOE) Sunshot initiative and industry partners. Some of the goals of this funding included the development of a 1 MWe loop, a 10 MWe turbine, and performance and mechanical testing. One of the key challenges that presented itself was the filling, start-up, and shut down of the entire system. Understanding the loop transient performance is important when having to bring a turbine online, transitioning from peak to partial loading, and also managing routine and emergency shut downs. Due to large changes in density near the critical point for CO2 and its tendency to form dry ice when expanded to atmospheric pressure, managing loop filling and venting is critical in ensuring that components are not damaged. With successful testing up to 715°C and 234 bar, this paper will provide updated data to, “Loop Filling and Start Up with a Closed Loop sCO2 Brayton Cycle [1].” While the previous paper focused on early trips and start up challenges, this paper will focus on the specific challenges at maximum operating conditions, and how the loop was managed when getting up to these high temperatures and pressures and how the loop behaved during a high temperature trip when compared to a controlled shut down from maximum operating conditions.

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