SEARCH · Search NASA

Results for “Exascale computer”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Towards Ab-Initio Simulations of Crystalline Defects at the Exascale Using Spectral Quadrature Density Functional Theory

Defects in crystalline solids play a crucial role in determining properties of materials at the nano, meso- and macroscales, such as the coalescence of vacancies at the nanoscale to form voids and prismatic dislocation loops or diffusion and segregation of solutes to nucleate precipitates, phase transitions in magnetic materials via disorder and doping. First principles Density Functional Theory (DFT) simulations can provide a detailed understanding of these phenomena. However, the number of atoms needed to correctly simulate these systems is often beyond the reach of many widely used DFT codes. The aim of this article is to discuss recent advances in first principles modeling of crystal defects using the spectral quadrature method. The spectral quadrature method is linear scaling with respect to the number of atoms, permits spatial coarse-graining, and is capable of simulating non-periodic systems embedded in a bulk environment, which allows the application of appropriate boundary conditions for simulations of crystalline defects. In this article, we discuss the state-of-the-art in ab-initio modeling of large metallic systems of the order of several thousand atoms that are suitable for utilizing exascale computing resourses.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Optimizing Communication in 2D Grid-Based MPI Applications at Exascale

The new reality of exascale computing faces many challenges in achieving optimal performance on large numbers of nodes. A key challenge is the efficient utilization of the message-passing interface (MPI), a critical component for process communication. This paper explores communication optimization strategies to harness the GPU-accelerated architectures of these supercomputers. We focus on MPI applications where processors form a two-dimensional process grid, a common arrangement in applications involving dense matrix operations. This configuration offers a unique opportunity to implement innovative strategies to improve performance and maintain effective load distribution. We study two applications— Dist-FW (Apsp:all-pair-shortest-path) and HPL-MxP (LU factorization with Mixed precision)—on two accelerated systems: Summit (IBM Power and NVIDIA V100) and Frontier (AMD EPYC and MI250X). These supercomputers are operated by the Oak Ridge Leadership Computing Facility (OLCF) and are currently ranked #1 and #5 on the Top500 list. We show how to scale up both applications to exascale levels and tackle the MPI challenges related to implementation, synchronization, and performance. We also compare the performance of several communication strategies at an unprecedented scale. Accurately predicting application performance becomes crucial for cost reduction as the computational scale grows. To address this, we suggest a hyperbolic model as a better alternative to the traditional one-sided asymptotic model for predicting future application performance at such large scales.

Lu, Hao↗

DOE OSTI2025

ChemComp: A Compilation Framework for Computing with Chemical Reaction Networks

The acceleration of scientific computation, data analytics, and artificial intelligence is driving a surge in computational requirements. Yet, state-of-the-art high-performance computing systems are approaching physical limitations that impede further significant improvements in energy efficiency. As we move towards post-exascale computing systems, innovative approaches are necessary to overcome this barrier in power consumption. Novel analog and hybrid digital-analog architectures hold promise for enhancing energy efficiency by several orders of magnitude. Biochemical computation stands out among the various solutions being explored due to its potential to enable new classes of devices with immense computational capabilities. These devices can capitalize on the inherent efficacy of biological cells in solving optimization problems and are scalable through increasing reaction system size or vessel capacity, potentially satisfying scientific computing's high-performance requirements. Nonetheless, several theoretical and practical limitations persist, including problem formulation and mapping to chemical reaction networks (CRNs) and implementation of actual CRN devices. In this paper, we propose a framework for biochemical computation using systems chemistry. We present the initial components of our approach: an abstract chemical reaction dialect implemented as a multi-level intermediate representation (MLIR) compiler extension and a pathway to represent mathematical problems with CRNs. To showcase the potential of this approach, we emulate a simplified chemical reservoir device. This work lays the groundwork for leveraging chemistry's computing potential in creating energy-efficient, high-performance computing systems tailored to contemporary computational needs.

artificial intelligence↗

DOE OSTI2023

ExaWind at NREL: Upping the Ante

The objective of the ExaWind component of the Exascale Computing Project is to deliver many-turbine blade-resolved simulations in complex terrain. These simulations bring new challenges to both compute and analysis of the resulting data. In this paper/video, we visually explore the impact of ExaWind on wind simulations through two studies of a small wind farm under two atmospheric conditions. We then turn to analysis and review tools that visualization researchers at NREL use to answer the challenges that ExaWind brings.

collaborative visualization↗

DOE OSTI2024

Estimating the carbon footprint of computational fluid dynamics

In this study, computational resources have grown exponentially in the past few decades. These machines make possible research and design in fields as diverse as medicine, astronomy, and engineering. Despite ever-increasing computational capabilities, direct simulation of complex systems has remained challenging owing to the degrees of freedom involved. At the cusp of exascale computing, high-resolution simulation of practical problems with minimal model assumptions may soon experience a renaissance. However, growing reliance on modern computers comes at the cost of a growing carbon footprint. To illustrate this, we examine historic computations in fluid dynamics where larger computers have afforded the opportunity to simulate flows at increasingly relevant Reynolds numbers. Under a variety of flow configurations, the carbon footprint of such simulations is found to scale roughly with the fourth power of Reynolds number. This is primarily explained by the computation cost in core-hours, which is also described by similar scaling, though regional differences in renewable energy use also play a role. Using the established correlation, we examine a large database of simulations to develop estimates for the carbon footprint of computational fluid dynamics in a given year. Collectively, the analysis provides an additional benchmark for new computations where, in addition to balancing considerations of model fidelity, carbon footprint should also be considered.

42 ENGINEERING↗

DOE OSTI2024

Advances in ArborX to support exascale applications

ArborX is a performance portable geometric search library developed as part of the Exascale Computing Project (ECP). In this paper, we explore a collaboration between ArborX and a cosmological simulation code HACC. Large cosmological simulations on exascale platforms encounter a bottleneck due to the in-situ analysis requirements of halo finding, a problem of identifying dense clusters of dark matter (halos). This problem is solved by using a density-based DBSCAN clustering algorithm. With each MPI rank handling hundreds of millions of particles, it is imperative for the DBSCAN implementation to be efficient. In addition, the requirement to support exascale supercomputers from different vendors necessitates performance portability of the algorithm. We describe how this challenge problem guided ArborX development, and enhanced the performance and the scope of the library. We explore the improvements in the basic algorithms for the underlying search index to improve the performance, and describe several implementations of DBSCAN in ArborX. Further, we report the history of the changes in ArborX and their effect on the time to solve a representative benchmark problem, as well as demonstrate the real world impact on production end-to-end cosmology simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock". Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗

DOE OSTI2024

Refining HPCToolkit for application performance analysis at exascale

As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.

Adhianto, Laksono↗

DOE OSTI2020

Current Needs in Radiochemistry

There are a number of current needs in the development of the Pu and Am separations that underpin larger interest areas including nuclear waste issues, nuclear weapons, nuclear forensics, nuclear fuels and similarly related fields. In the large areas of interest, there are a number of sub-fields that include corrosion chemistry, pyrochemistry, solution-phase chemistry, rapid separations, environmental speciation, and other similar efforts. However, in all of the these fields it is important to realize that the underlying chemistry that underpins these efforts is similar in that Pu and Am are bound to ligands that can be in the solid, aqueous, or gas phase and separations occurs due to the preferential binding of the ligand to Pu or Am based on a number of factors that include pH, concentration, oxidation state, etc. In the past, most of the Pu and Am chemistry has occurred in limited scope within universities where micro-scale and bench-top scale chemistry are the dominate investigative theme. At US national laboratory and their international counterparts, larger scale reactions have been attempted such that industrial scale chemistry has been completed. Some of this technology has been harnessed in radiopharmaceuticals and nuclear fuel reprocessing. However, such bench-top research is expensive to due to the safety and security that is concerned when dealing with Pu, and scale-up of reactions is even more resource intensive. In an effort to offset this expensive, it would be of interest to explore the ability to predict selectivity based on the computational modeling of actinide chemistry. This has historically been limited by the abilities of large supercomputers, but with the emphasis on developing exascale computers within the US and similar efforts by other countries this predictive modeling has potential to produce significant results that would yield improved understanding of actinide chemistry.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach: Preprint

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock''. Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗

DOE OSTI2021

Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs

The fully electromagnetic particle-in-cell code WarpX is being developed by a team of the U.S. DOE Exascale Computing Project (with additional non-U.S. collaborators on part of the code) to enable the modeling of chains of tens to hundreds of plasma accelerator stages on exascale supercomputers, for future collider designs. The code is combining the latest algorithmic advances (e.g., Lorentz boosted frame and pseudo-spectral Maxwell solvers) with mesh refinement and runs on the latest computer processing unit and graphical processing unit (GPU) architectures. In this paper, we summarize the strategy that was adopted to port WarpX to GPUs, report on the weak parallel scaling of the pseudo-spectral electromagnetic solver, and then present solutions for decreasing the time spent in data exchanges from guard regions between subdomains. In Sec. IV, we demonstrate the simulations of a chain of three consecutive multi-GeV laser-driven plasma accelerator stages.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

EXAGRAPH: Graph and combinatorial methods for enabling exascale applications

Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. With tens of billions of hardware threads and deep memory hierarchies, the exascale computing systems in particular pose extreme challenges in scaling graph algorithms. The codesign center on combinatorial algorithms, ExaGraph, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a diverse set of exascale applications. Algebraic and combinatorial methods have a complementary role in the advancement of computational science and engineering, including playing an enabling role on each other. In this paper, we survey the algorithmic and software development activities performed under the auspices of ExaGraph from both a combinatorial and an algebraic perspective. In particular, we detail our recent efforts in porting the algorithms to manycore accelerator (GPU) architectures. We also provide a brief survey of the applications that have benefited from the scalable implementations of different combinatorial algorithms to enable scientific discovery at scale. We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A massively parallel time-domain coupled electrodynamics–micromagnetics solver

We present a high-performance coupled electrodynamics–micromagnetics solver for full physical modeling of signals in microelectronic circuitry. The overall strategy couples a finite-difference time-domain approach for Maxwell’s equations to a magnetization model described by the Landau–Lifshitz–Gilbert equation. The algorithm is implemented in the Exascale Computing Project software framework, AMReX, which provides effective scalability on manycore and GPU-based supercomputing architectures. Furthermore, the code leverages ongoing developments of the Exascale Application Code, WarpX, which is primarily being developed for plasma wakefield accelerator modeling. Our temporal coupling scheme provides second-order accuracy in space and time by combining the integration steps for the magnetic field and magnetization into an iterative sub-step that includes a trapezoidal temporal discretization for the magnetization. The performance of the algorithm is demonstrated by the excellent scaling results on NERSC multicore and GPU systems, with a significant (59×) speedup on the GPU using a node-by-node comparison. We demonstrate the utility of our code by performing simulations of an electromagnetic waveguide and a magnetically tunable filter.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗

DOE OSTI2023

Experiences Readying Applications for Exascale

The advent of Exascale computing invites an assessment of existing best practices for developing application readiness on the world's largest supercomputers. This work details observations from the last four years in preparing scientific applications to run on the Oak Ridge Leadership Computing Facility's (OLCF) Frontier system. This paper addresses a range of topics in software including programmability, tuning, and portability considerations that are key to moving applications from existing systems to future installations. A set of representative workloads provides case studies for general system and software testing. We evaluate the use of early access systems for development across several generations of hardware. Finally, we discuss how best practices were identified and disseminated to the community through a wide range of activities including user-guides and trainings. We conclude with recommendations for ensuring application readiness on future leadership computing systems.

exascale↗

DOE OSTI2021

A Brief Overview of Radiochemistry: Californium-252: Unique and Versatile Man-made Radioisotope

This chapter summarizes radiochemistry and its applications with a focus on areas where computational science can be beneficial. This is presented for a target audience of computational scientists and chemists, and the general public who are interested in radiochemistry topics that may benefit from the use of computational methods. Past, present, and future applications of radiochemistry are presented along with a brief examination of the field’s workforce. Discussions of the current state of several radiochemistry specialty areas are also included. These discussions generally exemplify topics that are readily amenable to computational tools. Topics discussed include medical uses for radioactive materials and related computational needs; the current and future trends in the separations of the f-block elements; production and uses of Californium-252; and the need for exascale computing to solve current problems in the chemistry of Americium and Plutonium.

Ezold, Julie↗

DOE OSTI2021

A Brief Overview of Radiochemistry: f-Element Separations: The State of the Art and Future Directions

Kimberlin, Ashleigh↗

DOE OSTI2023

PELE - Transforming Combustion Science and Technology with Exascale Simulations

This poster presents the status of the Pele Combustion project, an applications project in the Exascale Computing Project. It summarizes the goal of the projects, developments added to the Pele simulation capabilities over the last year, some example performance figures that target ultimate application on the Frontier supercomputer, and show a number of published and in-progress simulation results, by us and our collaborators.

combustion↗