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’.

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 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’.

scholarly journals Numerical algorithms for high-performance computational science

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190066 ◽  
Author(s):  
Jack Dongarra ◽  
Laura Grigori ◽  
Nicholas J. Higham
Keyword(s):  
High Performance ◽  
Numerical Algorithms ◽  
Computational Science ◽  
Floating Point ◽  
Important Criterion ◽  
Data Movement ◽  
Floating Point Arithmetic ◽  
High Performance Computers ◽  
Point Arithmetic ◽  
Speed And Accuracy

A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. 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.

scholarly journals Lattice QCD Application Development within the US DOE Exascale Computing Project

2018 ◽  
Vol 175 ◽  
pp. 09010 ◽  
Author(s):  
Richard Brower ◽  
Norman Christ ◽  
Carleton DeTar ◽  
Robert Edwards ◽  
Paul Mackenzie
Keyword(s):  
Lattice Qcd ◽  
Lattice Gauge Theory ◽  
Short Description ◽  
Department Of Energy ◽  
Application Development ◽  
Science And Engineering ◽  
Software Developers ◽  
Exascale Computing ◽  
Lattice Gauge ◽  
The Us

In October, 2016, the US Department of Energy launched the Exascale Computing Project, which aims to deploy exascale computing resources for science and engineering in the early 2020’s. The project brings together application teams, software developers, and hardware vendors in order to realize this goal. Lattice QCD is one of the applications. Members of the US lattice gauge theory community with significant collaborators abroad are developing algorithms and software for exascale lattice QCD calculations. We give a short description of the project, our activities, and our plans.

scholarly journals Workflow Systems for Science: Concepts and Tools

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Domenico Talia
Keyword(s):  
High Performance ◽  
Workflow Management ◽  
Large Data ◽  
Scientific Applications ◽  
Data Repositories ◽  
Workflow Systems ◽  
Simulation Techniques ◽  
Workflow System ◽  
Computing Platforms ◽  
High Level

The wide availability of high-performance computing systems, Grids and Clouds, allowed scientists and engineers to implement more and more complex applications to access and process large data repositories and run scientific experiments in silico on distributed computing platforms. Most of these applications are designed as workflows that include data analysis, scientific computation methods, and complex simulation techniques. Scientific applications require tools and high-level mechanisms for designing and executing complex workflows. For this reason, in the past years, many efforts have been devoted towards the development of distributed workflow management systems for scientific applications. This paper discusses basic concepts of scientific workflows and presents workflow system tools and frameworks used today for the implementation of application in science and engineering on high-performance computers and distributed systems. In particular, the paper reports on a selection of workflow systems largely used for solving scientific problems and discusses some open issues and research challenges in the area.

scholarly journals On the cost of iterative computations

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190050
Author(s):  
Erin Carson ◽  
Zdeněk Strakoš
Keyword(s):  
High Performance ◽  
A Priori ◽  
Numerical Algorithms ◽  
Computational Science ◽  
Rounding Errors ◽  
Performance Bottlenecks ◽  
Linear Algebraic Systems ◽  
Key Points ◽  
Practical Performance ◽  
The Cost

With exascale-level computation on the horizon, the art of predicting the cost of computations has acquired a renewed focus. This task is especially challenging in the case of iterative methods, for which convergence behaviour often cannot be determined with certainty a priori (unless we are satisfied with potentially outrageous overestimates) and which typically suffer from performance bottlenecks at scale due to synchronization cost. Moreover, the amplification of rounding errors can substantially affect the practical performance, in particular for methods with short recurrences. In this article, we focus on what we consider to be key points which are crucial to understanding the cost of iteratively solving linear algebraic systems. This naturally leads us to questions on the place of numerical analysis in relation to mathematics, computer science and sciences, in general. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

scholarly journals The physics of numerical analysis: a climate modelling case study

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190058 ◽  
Author(s):  
T. N. Palmer
Keyword(s):  
Numerical Analysis ◽  
Computer Science ◽  
High Performance ◽  
Numerical Algorithms ◽  
Climate Science ◽  
Computational Science ◽  
Climate Modelling

The case is made for a much closer synergy between climate science, numerical analysis and computer science. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

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