Structural biology in the age of X-ray free-electron lasers and exascale computing
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
SEARCH · Search NASA
Results for “Exascale Computing”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Small-scale properties from exascale computations of turbulence on a $\mathbf{32\,768^3}$ periodic cube
To study the physics of small-scale properties of homogeneous isotropic turbulence at increasingly high Reynolds numbers, direct numerical simulation results have been obtained for forced isotropic turbulence at Taylor-scale Reynolds number R λ = 2500 on a 32 768 3 three-dimensional periodic domain using a GPU pseudo-spectral code on a 1.1 exaflop GPU supercomputer (Frontier). These simulations employ the multi-resolution independent simulation (MRIS) technique (Yeung & Ravikumar 2020, Phys. Rev. Fluids, vol. 5, 110517) where ensemble averaging is performed over multiple short segments initiated from velocity fields at modest resolution, and subsequently taken to higher resolution in both space and time. Reynolds numbers are increased by reducing the viscosity with the large-scale forcing parameters unchanged. Although MRIS segments at the highest resolution for each Reynolds number last for only a few Kolmogorov time scales, small-scale physics in the dissipation range is well captured – for instance, in the probability density functions and higher moments of the dissipation rate and enstrophy density, which appear to show monotonic trends persisting well beyond the Reynolds number range in prior works in the literature. Attainment of range of length and time scales consistent with classical scaling also reinforces the potential utility of the present high-resolution data for studies of short-time-scale turbulence physics at high Reynolds numbers where full-length simulations spanning many large-eddy time scales are still not accessible. A single snapshot of the 32 768 3 data is publicly available for further analyses via the Johns Hopkins Turbulence Database.
intermittency↗
Chapter 1: Looking Forward: The Promise and Challenge of Exascale Computing
The demand for HFM development and HPC resources for improved predictive modelling capability for wind energy will continue to grow as industry and government seek solutions to (1) mitigate risk and uncertainty of technology development at multimegawatt-turbine scales, (2) address new requirements driving renewable energy deployment and high -penetration scenarios, (3) understand the impacts of multiple gigawatt-scale plant deployments on microclimatology and grid architectures for both land-based and offshore installation, and (4) evolve the systems and processes needed for the dispatch of renewable energy generation on a national scale supporting a mix of power-generation technologies. When considering the cost of developing a new offshore multimegawatt commercial wind turbine product line or the financial risk of a large offshore wind plant deployment, predictive HPC and HFM simulations that incorporate integrated design, cost analysis, and O&M predictions will become an essential part of the early design and development process.
97 MATHEMATICS AND COMPUTING↗
DOE takes another step toward exascale computing
New supercomputers will allow more detailed simulations for nuclear weapons research and a range of scientific applications.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Distributed Multi-GPU Community Detection on Exascale Computing Platforms
Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.
graph algorithms, high performance comptuing↗
The Exascale Computing Project
Explore the source record for details and available documents.
Messina, Paul↗
Simulating the weak death of the neutron in a femtoscale universe with near-Exascale computing
The fundamental particle theory called Quantum Chromodynamics (QCD) dictates everything about protons and neutrons, from their intrinsic properties to interactions that bind them into atomic nuclei. Quantities that cannot be fully resolved through experiment, such as the neutron lifetime (whose precise value is important for the existence of light-atomic elements that make the sun shine and life possible), may be understood through numerical solutions to QCD. We directly solve QCD using Lattice Gauge Theory and calculate nuclear observables such as neutron lifetime. We have developed an improved algorithm that exponentially decreases the time-tosolution and applied it on the new CORAL supercomputers, Sierra and Summit. We use run-time autotuning to distribute GPU resources, achieving 20% performance at low node count. We also developed optimal application mapping through a job manager, which allows CPU and GPU jobs to be interleaved, yielding 15% of peak performance when deployed across large fractions of CORAL.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Energy Exascale Computational Fluid Dynamics Simulations With the Spectral Element Method
Development and application of the open-source GPU-based fluid-thermal simulation code, NekRS, are described. Time advancement is based on an efficient kth-order accurate timesplit formulation coupled with scalable iterative solvers. Spatial discretization is based on the high-order spectral element method (SEM), which affords the use of fast, low-memory, matrix-free operator evaluation. Further, recent developments include support for nonconforming meshes using overset grids and for GPU-based Lagrangian particle tracking. Results of large-eddy simulations of atmospheric boundary layers for wind-energy applications as well as extensive nuclear energy applications are presented.
42 ENGINEERING↗
Exascale computing and big data
Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics. The tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both; unification is essential to address a spectrum of major research domains. The challenges of scale tax our ability to transmit data, compute complicated functions on that data, or store a substantial part of it; new approaches are required to meet these challenges. Finally, the international nature of science demands further development of advanced computer architectures and global standards for processing data, even as international competition complicates the openness of the scientific process.
97 MATHEMATICS AND COMPUTING↗
The UPC++ PGAS library for exascale computing. Extended Abstract
We describe UPC++ V1.0, a C++11 library that supports APGAS programming. UPC++ targets distributed data structures where communication is irregular or fine-grained. The key abstractions are global pointers, asynchronous programming via RPC, and futures. Global pointers incorporate ownership information useful in optimizing for locality. Futures capture data readiness state, are useful for scheduling and also enable the programmer to chain operations to execute asynchronously as high-latency dependencies become satisfied, via continuations. The interfaces for moving non-contiguous data and handling memories with different optimal access methods are composable and closely resemble those used in modern C++. Communication in UPC++ runs at close to hardware speeds by utilizing the low-overhead GASNet-EX communication library.
97 MATHEMATICS AND COMPUTING↗
MCNP 2020: Preparing LANL Monte Carlo for Exascale Computer Systems (White Paper)
The MCNP Monte Carlo code for simulating particle transport is the product of over 60 years of Monte Carlo code development at LANL. MCNP is the most-used code worldwide for particle transport in nuclear systems, with over 10,000 users in dozens of application areas. At LANL, MCNP is used extensively in applications for nuclear criticality safety, stockpile stewardship, homeland security, radiological safety, experiment design and analysis, etc., and is considered a critical technology resource for national defense. MCNP is often referred to as the “gold standard” for radiation transport calculations, based on the long development history and extensive validation work. Today, however, this critical technology and its well-deserved reputation are at risk. We propose a targeted effort over the next several years to upgrade the core software portions of the MCNP code. This effort will focus on fundamental structural changes to the underlying MCNP algorithms, data storage, and code architecture. These changes will directly improve code maintainability, reduce the development time for new features, improve the code performance, and make the code more flexible and adaptable for expected future computer systems. To move forward with new computer systems and additional new features, it is vital to have a solid base.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Final Report on XStack: Software Synthesis for High Productivity ExaScale Computing
The goal of the project was to develop a programming model that would significantly improve productivity in the high-performance computing domain by bringing together three components: a) Automated equivalence checking, b) Sketch-based program synthesis, and c) Autotuning. The report provides an executive summary of the research accomplished through this project. At the end of the report is appended a paper that describes in more detail the key technical accomplishments from this project, and which was published in SC 2014.
97 MATHEMATICS AND COMPUTING↗
LDRD-DR midterm review - High-order hydrodynamic methods for exascale computing
We propose to develop the first Lagrange+remap ALE discontinuous Galerkin (DG) hydrodynamic method on high-order elements for simulating gas and solid dynamics.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Exascale Computing Facility Modification (ECFM) Project Requirements Document
The objective of the project is to modify and upgrade the civil, structural, architectural, mechanical, and electrical systems of B453 and an adjacent approximately 3.5 acres of previously developed site. These modifications and upgrades would allow for continued use of B453 to support the increasing demands of the next generation high-performance computers. These modifications and upgrades would also provide enhanced power and cooling for the current and future ATS computers.
42 ENGINEERING↗
Progress Report on An Exascale Computational Simulation Capability for Pervasive Fracture & Failure
How does a structure fail in an explosion? Is there an engineering design opportunity to mitigate the effects of a blast? These are questions that may be addressed with simulation; however, a computational simulation capability to answer these questions must include developments in material physics modeling and numerical algorithms that we believe are possible, but do not yet exist. This LDRD project proposes to develop the physics models and computational approaches to create a computational simulation approach to answer these physics and engineering questions.
42 ENGINEERING↗
CEED-MS34: Improve performance and capabilities of CEED-enabled ECP applications on Summit/Sierra. Exascale Computing Project Milestone Report
The main goal of this milestone was to help CEED-enabled ECP applications, including ExaSMR, MARBL, ExaWind and ExaAM, to improve their performance and capabilities on GPU systems like Summit and Lassen/Sierra. In addition, the CEED team also worked to: add and improve support for additional hardware and programming models in the CEED software components; release the next version of the CEED software stack, CEED-3.0; and demonstrate performance of libParanumal kernels in libCEED, Nek and MFEM. These additional tasks contributed directly to the main CEED-MS34 goal and will also play an important role in CEED’s future milestones.
97 MATHEMATICS AND COMPUTING↗
Towards Use of Mixed Precision in ECP Math Libraries [Exascale Computing Project]
The use of multiple types of precision in mathematical software has the potential to increase its performance on new heterogeneous architectures. The xSDK project focuses both on the investigation and development of multiprecision algorithms as well as their inclusion into xSDK member libraries. This report summarizes current efforts on including and/or using mixed precision capabilities in the math libraries Ginkgo, heFFTe, hypre, MAGMA, PETSc/TAO, SLATE, SuperLU, and Trilinos, including KokkosKernels. It contains both numerical results from libraries that already provide mixed precision capabilities, as well as descriptions of the strategies to incorporate multiprecision into established libraries.
97 MATHEMATICS AND COMPUTING↗
Why We Need Strategies for Working Remotely: the Exascale Computing Project (ECP) Panel Series.
Abstract not provided.
Raybourn, Elaine M.↗