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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗
NASA NTRS1993

Computing in space: Issues and answers

Future plans for space exploration call for scientific instruments whose data is increasingly voluminous, far exceeding constraints set by telemetry rates, and therefore, an increased role for spaceborne computing. Three issues that greatly influence the realization of spaceborne computing systems are addressed. The first involves the identification of those research problems which are unique to space computing. Resources must be expended on these problems if efforts are to add value to, and have impact on, the technology of computing in space. The second addresses the transfer of new technology from the research laboratory to use in space. What can be done to expedite this process and shrink the technology gap that separates Earth and space based systems must be assessed. The third issue involves the user's point of view of what is required of new space computing systems. That is, what are the driving forces that most influence customers' decisions to use or not use new technology? Issues of design environment for spaceborne computers, tools for modeling and evaluation of flight systems, and validation of space flight hardware and software are also discussed.

Davidson, John M.↗
DOE OSTI2021

Toward a Seamless Integration of Computing, Experimental, and Observational Science Facilities: A Blueprint to Accelerate Discovery

The Department of Energy, Office of Science operates world-leading facilities for experimental, observational, and computational science. DOE supercomputing facilities will reach performance at the scale of ExaFLOPs in the coming years, enabling new vistas of scale and precision for large scale simulations and data analysis. Experimental scientific facilities are undergoing similar upgrades that will lead to higher data rates and correspondingly larger computational demands, and will increase the need for near-real-time processing and resilient support for more complex workflows. A transformation of science is underway, with workloads at supercomputing facilities increasingly driven by this explosion of data from instruments and experimental facilities, as well as the accelerating use of Artificial Intelligence (AI) as a tool for scientific discovery. A seamless integration of computing, networking, instruments, and experimental facilities is required to support these emerging workloads and open up a new frontier of U.S. leadership in scientific discovery. We propose to accomplish this by providing frictionless access to the ASCR supercomputing facilities. We describe our vision of combining the power of ASCR supercomputers and networking infrastructure into an integrated scalable fabric, available to end user scientists via interfaces that aim to automate and simplify access to high performance computing systems. This will enable unprecedented computational science capabilities for experimental and observational facilities, and will create new opportunities to combine large simulations and modeling with experimental facility data analysis. This blueprint for creating an integrated network of computational and experimental facilities will provide an enriched discovery environment and open doors for new scientific communities to access the DOE’s world-leading computing and networking capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Lithographic quantum dots for quantum computation and quantum simulation

Lithographic quantum dots (QDs) are highly controllable few-level quantum systems created in semiconductor nanoelectronic devices, with a variety of scientific applications. These include technologically-driven applications like quantum computing and more fundamental applications in which they serve as a platform for exploring basic many-body physics. This document is a brief summary of my Ph.D. research so far and the directions with which I intend to continue it. Highlights include theoretical efforts to understand and design qubits in germanium hole QDs, as well as explorations of the possibility of using QDs coupled to nearby baths for analog simulation of quantum impurity models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute

At the turn of the millennium the computational neuroscience community realized that neuroscience was in a software crisis: software development was no longer progressing as expected and reproducibility declined. The International Neuroinformatics Coordinating Facility (INCF) was inaugurated in 2007 as an initiative to improve this situation. The INCF has since pursued its mission to help the development of standards and best practices. In a community paper published this very same year, Brette et al. tried to assess the state of the field and to establish a scientific approach to simulation technology, addressing foundational topics, such as which simulation schemes are best suited for the types of models we see in neuroscience. In 2015, a Frontiers Research Topic “Python in neuroscience” by Muller et al. triggered and documented a revolution in the neuroscience community, namely in the usage of the scripting language Python as a common language for interfacing with simulation codes and connecting between applications. The review by Einevoll et al. documented that simulation tools have since further matured and become reliable research instruments used by many scientific groups for their respective questions. Open source and community standard simulators today allow research groups to focus on their scientific questions and leave the details of the computational work to the community of simulator developers. A parallel development has occurred, which has been barely visible in neuroscientific circles beyond the community of simulator developers: Supercomputers used for large and complex scientific calculations have increased their performance from ~10 TeraFLOPS (10 13 floating point operations per second) in the early 2000s to above 1 ExaFLOPS (10 18 floating point operations per second) in the year 2022. This represents a 100,000-fold increase in our computational capabilities, or almost 17 doublings of computational capability in 22 years. Moore's law (the observation that it is economically viable to double the number of transistors in an integrated circuit every other 18–24 months) explains a part of this; our ability and willingness to build and operate physically larger computers, explains another part. It should be clear, however, that such a technological advancement requires software adaptations and under the hood, simulators had to reinvent themselves and change substantially to embrace this technological opportunity. It actually is quite remarkable that—apart from the change in semantics for the parallelization—this has mostly happened without the users knowing. The current Research Topic was motivated by the wish to assemble an update on the state of neuroscientific software (mostly simulators) in 2022, to assess whether we can see more clearly which scientific questions can (or cannot) be asked due to our increased capability of simulation, and also to anticipate whether and for how long we can expect this increase of computational capabilities to continue.

biophysically detailed models↗
DOE OSTI2023

Finalizing Transition to the New Data Center at BNL

Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS Experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II Experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term. The Computing Facility Revitalization (CFR) project is aimed at repurposing the former National Synchrotron Light Source (NSLS-I) building as the new data center for BNL. The construction of the new data center was finished in 2021Q3, and it was delivered for production in early FY2022 for all collaborations supported by the Scientific Data and Computing Center (SDCC), including STAR, PHENIX and sPHENIX experiments at RHIC collider at BNL, the Belle II Experiment at KEK (Japan), and the Computational Science Initiative at BNL (CSI). This paper highlights the key mechanical, electrical, and networking components of the new data center in its final configuration as used in production since 2021Q4 and gives an overview for the extension of the central network systems into the new data center and the migration of a significant portion of IT load and services from the old data center to the new data center carried out in 20212023, with expected completion of the main phase of the gradual IT equipment replacement and migration from the old data center into the new one set to the end of FY2023 (Sep 30, 2023).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
DOE OSTI2025

Optical neural engine for solving scientific partial differential equations

Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

Tang, Yingheng (ORCID:0009000153622546)↗
DOE OSTI2018

Realizing High-Performance Parallel Computing Final Report CRADA No. TC-0319-92

The objectives of this project were to improve the programming environment for scientific applications on shared-memory (SMP) and massively-parallel (MMP) computer systems and to develop high-performance, parallel rational drug design applications. The collaborators, IBM Corp., BioNumerik Pharmaceuticals (BNP), and Lawrence Livermore National Laboratory (LLNL), each brought unique expertise to the project. IBM contributed state-of-the-art SMP and MMP computer systems; BNP provided proprietary quantum mechanical and statistical mechanical molecular dynamics codes and experimental data; and LLNL contributed its high-performance Sisal compilers, runtime systems, and programming tools.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

In-flight utilization of the Mariner 10 spacecraft computer

Mariner 10 in its mission to Venus and Mercury used an onboard control computer with a 512-word memory. An outline is presented of the general philosophy adopted with respect to the utilization of the available memory and a description is given of the actual in-flight operation of the machine. The onboard computer system provided automatic stored-program sequence control of all spacecraft scientific, engineering, and propulsive maneuver operations. The system consisted of the special purpose digital computer and a fixed hard-wired sequencer. Attention is given to the programming architecture, aspects of mission design, and computer performance.

Hooke, A. J.↗
NASA NTRS2002

Microgravity: Molecular Dynamics Simulations at the NCCS Probe the Behavior of Liquids in Low Gravity

The life of the very small, whether in something as complicated as a human cell or as simple as a drop of water, is of fundamental scientific interest: By knowing how a tiny amount of material reacts to changes in its environment, scientists maybe able to answer questions about how a bulk of material would react to comparable changes. NASA is in the forefront of computational research into a broad range of basic scientific questions about fluid dynamics and the nature of liquid boundary instability. For example, one important issue for the space program is how drops of water and other materials will behave in the low-gravity environment of space and how the low gravity will affect the transport and containment of these materials. Accurate prediction of this behavior is among the aims of a set of molecular dynamics experiments carried out on the NCCSs Cray supercomputers. In conventional computational studies of materials, matter is treated as continuous - a macroscopic whole without regard to its molecular parts - and the behavior patterns of the matter in various physical environments are studied using well-established differential equations and mathematical parameters based on physical properties such as compressibility density, heat capacity, and vapor pressure of the bulk material.

Source record↗
DOE OSTI2019

Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort

Scientific Named Entity Referent Extraction is often more complicated than traditional Named Entity Recognition (NER). For example, in polymer science, chemical structure may be encoded in a variety of nonstandard naming conventions, and authors may refer to polymers with conventional names, commonly used names, labels (in lieu of longer names), synonyms, and acronyms. As a result, accurate scientific NER methods are often based on task-specific rules, which are difficult to develop and maintain, and are not easily generalized to other tasks and fields. Machine learning models require substantial expert-annotated data for training. Here we propose polyNER: a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based word vector classifier. Evaluation on materials science publications shows that the polyNER approach enables improved precision or recall relative to a state-of-the-art chemical entity extraction system at a dramatically lower cost: it required just two hours of expert time, rather than extensive and expensive rule engineering, to achieve that result. This result highlights the potential for human-computer partnership for constructing domain-specific scientific NER systems.

Crowdsourcing↗
DOE OSTI2022

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

Artificial Intelligence↗
NASA NTRS2022

Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Computational models of the Earth System are critical tools for modern scientific inquiry. Effortstoward evaluating and improving errors in representations of physical and chemical processes inthese large computational systems are commonly stymied by highly nonlinear and complexerror behavior. Recent work has shown that these errors can be effectively predicted usingmodern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previousstudies to apply an interpretable A.I. technique to not only predict model errors but also movetoward understanding the underlying reasons for successful error prediction. We use XGBoostclassification trees and SHapley Additive exPlanations (SHAP) analysis to explore the errors inthe prediction of lightning occurrence in the NASA GEOS model, a widely used Earth SystemModel. This explainable error prediction system can effectively predict the model error andindicates that the errors are strongly related to convective processes and the characteristics ofthe land surface.

Artificial intelligence↗
NASA NTRS1986

White paper: A plan for cooperation between NASA and DARPA to establish a center for advanced architectures

Large, complex computer systems require many years of development. It is recognized that large scale systems are unlikely to be delivered in useful condition unless users are intimately involved throughout the design process. A mechanism is described that will involve users in the design of advanced computing systems and will accelerate the insertion of new systems into scientific research. This mechanism is embodied in a facility called the Center for Advanced Architectures (CAA). CAA would be a division of RIACS (Research Institute for Advanced Computer Science) and would receive its technical direction from a Scientific Advisory Board established by RIACS. The CAA described here is a possible implementation of a center envisaged in a proposed cooperation between NASA and DARPA.

Denning, P. J.↗
DOE OSTI2019

PCS: A Productive Computational Science Platform

As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators, software becomes a critical aspect necessary to fully exploit these accelerators while not increasing the effort and time required to make variable scientific discovery in the face of complicated programming environments introduced by these accelerators. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. The PCS platform by design follows a basic principle that, multiple programming models in a unified programming environment, is essential to facilitate large-scale, accelerator-aware, heterogeneous computing for next-generation scientific applications. We propose a new scheme — Edge Neural Network — for managing regularity in large dataflow graphs using higher-order bulk synchronous operators, providing a foundation for formalizing computational models for cluster-wide dataflow programming of heterogeneous clusters.

Ojika, David↗
DOE OSTI2020

Preparing sparse solvers for exascale computing

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.

97 MATHEMATICS AND COMPUTING↗