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At least 199 records · Page 11

Advances in Mixed Precision Algorithms: 2021 Edition

Over the last year, the ECP xSDK-multiprecision effort has made tremendous progress in developing and deploying new mixed precision technology and customizing the algorithms for the hardware deployed in the ECP flagship supercomputers. The effort also has succeeded in creating a cross-laboratory community of scientists interested in mixed precision technology and now working together in deploying this technology for ECP applications. In this report, we highlight some of the most promising and impactful achievements of the last year. Among the highlights we present are: Mixed precision IR using a dense LU factorization and achieving a 1.8× speedup on Spock; results and strategies for mixed precision IR using a sparse LU factorization; a mixed precision eigenvalue solver; Mixed Precision GMRES-IR being deployed in Trilinos, and achieving a speedup of 1.4× over standard GMRES; compressed Basis (CB) GMRES being deployed in Ginkgo and achieving an average 1.4× speedup over standard GMRES; preparing hypre for mixed precision execution; mixed precision sparse approximate inverse preconditioners achieving an average speedup of 1.2×; and detailed description of the memory accessor separating the arithmetic precision from the memory precision, and enabling memory-bound low precision BLAS 1/2 operations to increase the accuracy by using high precision in the computations without degrading the performance. We emphasize that many of the highlights presented here have also been submitted to peer-reviewed journals or established conferences, and are under peer-review or have already been published.

97 MATHEMATICS AND COMPUTING↗

Advances in Mixed Precision Algorithms: 2021 Edition

Over the last year, the ECP xSDK-multiprecision effort has made tremendous progress in developing and deploying new mixed precision technology and customizing the algorithms for the hardware deployed in the ECP flagship supercomputers. The effort also has succeeded in creating a cross-laboratory community of scientists interested in mixed precision technology and now working together in deploying this technology for ECP applications. In this report, we highlight some of the most promising and impactful achievements of the last year. Among the highlights we present are • Mixed precision IR using a dense LU factorization and achieving a 1.8× speedup on Spock; • Results and strategies for mixed precision IR using a sparse LU factorization; • A mixed precision eigenvalue solver; • Mixed Precision GMRES-IR being deployed in Trilinos, and achieving a speedup of 1.4× over standard GMRES; • Compressed Basis (CB) GMRES being deployed in Ginkgo and achieving an average 1.4× speedup over standard GMRES; • Preparing hypre for mixed precision execution; • Mixed precision sparse approximate inverse preconditioners achieving an average speedup of 1.2×; • Detailed description of the memory accessor separating the arithmetic precision from the memory precision, and enabling memory-bound low precision BLAS 1/2 operations to increase the accuracy by using high precision in the computations without degrading the performance. We emphasize that many of the highlights presented here have also been submitted to peer-reviewed journals or established conferences, and are under peer-review or have already been published.

97 MATHEMATICS AND COMPUTING↗

Advanced Transport Delay Compensation Algorithms: Results of Delay Measurement and Piloted Performance Tests

This report summarizes the results of delay measurement and piloted performance tests that were conducted to assess the effectiveness of the adaptive compensator and the state space compensator for alleviating the phase distortion of transport delay in the visual system in the VMS at the NASA Langley Research Center. Piloted simulation tests were conducted to assess the effectiveness of two novel compensators in comparison to the McFarland predictor and the baseline system with no compensation. Thirteen pilots with heterogeneous flight experience executed straight-in and offset approaches, at various delay configurations, on a flight simulator where different predictors were applied to compensate for transport delay. The glideslope and touchdown errors, power spectral density of the pilot control inputs, NASA Task Load Index, and Cooper-Harper rating of the handling qualities were employed for the analyses. The overall analyses show that the adaptive predictor results in slightly poorer compensation for short added delay (up to 48 ms) and better compensation for long added delay (up to 192 ms) than the McFarland compensator. The analyses also show that the state space predictor is fairly superior for short delay and significantly superior for long delay than the McFarland compensator.

Guo, Liwen↗

Recent Advancements in Lightning Jump Algorithm Work

In the past year, the primary objectives were to show the usefulness of total lightning as compared to traditional cloud-to-ground (CG) networks, test the lightning jump algorithm configurations in other regions of the country, increase the number of thunderstorms within our thunderstorm database, and to pinpoint environments that could prove difficult for any lightning jump configuration. A total of 561 thunderstorms have been examined in the past year (409 non-severe, 152 severe) from four regions of the country (North Alabama, Washington D.C., High Plains of CO/KS, and Oklahoma). Results continue to indicate that the 2 lightning jump algorithm configuration holds the most promise in terms of prospective operational lightning jump algorithms, with a probability of detection (POD) at 81%, a false alarm rate (FAR) of 45%, a critical success index (CSI) of 49% and a Heidke Skill Score (HSS) of 0.66. The second best performing algorithm configuration was the Threshold 4 algorithm, which had a POD of 72%, FAR of 51%, a CSI of 41% and an HSS of 0.58. Because a more complex algorithm configuration shows the most promise in terms of prospective operational lightning jump algorithms, accurate thunderstorm cell tracking work must be undertaken to track lightning trends on an individual thunderstorm basis over time. While these numbers for the 2 configuration are impressive, the algorithm does have its weaknesses. Specifically, low-topped and tropical cyclone thunderstorm environments are present issues for the 2 lightning jump algorithm, because of the suppressed vertical depth impact on overall flash counts (i.e., a relative dearth in lightning). For example, in a sample of 120 thunderstorms from northern Alabama that contained 72 missed events by the 2 algorithm 36% of the misses were associated with these two environments (17 storms).

Schultz, Christopher J.↗

Quantum Computing Algorithms and Applications for Coherent and Strongly Correlated Chemical Systems

This project advanced quantum algorithms, quantum information theory, strongly correlated electronic structure methods, molecular quantum materials, and exciton transport imaging in coherent condensed phase systems. Across the award period, the team developed new methods for open-quantum-system simulation, Hamiltonian learning, state tomography, reduced-density-matrix and contracted-quantum eigensolver approaches, and quantum diagnostics for device capability and openness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Medical Data Architecture Prototype Development - Summary of Recent Work and Proposed Ideas for Upcoming Work

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions, and in ExMC Gap Med 10: We do not have the capability to provide computed medical decision support during exploration missions. These gaps recognize the need for a comprehensive medical data management system and the accompanying computational support to provide autonomous medical care during long duration exploration missions. As the MDA matures—including the capability to comprehensively process and discover medically-relevant information to support medical operations during exploration missions—project focus will shift to maturing and extending the MDA platform to enable clinical decision support and real-time guidance. To date, the MDA foundational architecture has recommended exploration medical system Level of Care IV requirements through a series of test bed prototype developments and analog demonstrations. The next stage in the development will focus on more autonomous clinical decision making necessary to address challenges in executing a self-contained medical system that enables health care both with and without assistance from ground support. A thorough understanding of current state of medical decision support systems, advanced machine learning algorithms and vast and varied data sources is required. The development of a clinical decision support for exploration missions (Level of Care V) roadmap is needed: one that assesses of current state of the art of clinical decision support systems (CDSS), interoperability issues, identification of challenges in health and performance monitoring, obtaining and processing information from biosensors, knowledge and data management, data integration and fusion, and advanced algorithm development. This roadmap must also include rapid prototype development in the areas of data processing, advanced analysis and prediction of medical events, and treatment based on medically relevant information processing and evidence-based best practices. In this presentation, an overview of the relevant issues and the beginning framework of a Level of Care V CDSS development roadmap will be provided.

Krihak, M.↗

Computation of the Streamfunction and Velocity Potential for Limited and Irregular Domains

An algorithm is proposed for the computation of streamfunction and velocity potential from given horizontal velocity vectors based on solving a minimization problem. To guarantee the uniqueness of the solution and computational reliability of the algorithm, a Tikhonov regularization is applied. The solution implies that the obtained streamfunction and velocity potential have minimal magnitude, while the given velocity vectors can be accurately reconstructed from the computed streamfunction and velocity potential. Because the formulation of the minimization problem allows for circumventing the explicit specification of separate boundary conditions on the streamfunction and velocity potential, the algorithm is easily applicable to irregular domains. By using an advanced minimization algorithm with the use of adjoint techniques, the method is computationally efficient and suitable for problems with large dimensions. An example is presented for coastal oceans to illustrate the practical application of the algorithm.

velocity↗

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗

Design and analysis of advanced flight planning concepts

The objectives of this continuing effort are to develop and evaluate new algorithms and advanced concepts for flight management and flight planning. This includes the minimization of fuel or direct operating costs, the integration of the airborne flight management and ground-based flight planning processes, and the enhancement of future traffic management systems design. Flight management (FMS) concepts are for on-board profile computation and steering of transport aircraft in the vertical plane between a city pair and along a given horizontal path. Flight planning (FPS) concepts are for the pre-flight ground based computation of the three-dimensional reference trajectory that connects the city pair and specifies the horizontal path, fuel load, and weather profiles for initializing the FMS. As part of these objectives, a new computer program called EFPLAN has been developed and utilized to study advanced flight planning concepts. EFPLAN represents an experimental version of an FPS. It has been developed to generate reference flight plans compatible as input to an FMS and to provide various options for flight planning research. This report describes EFPLAN and the associated research conducted in its development.

Sorensen, John A.↗

Advanced fuel fusion, phase space engineering, and structure-preserving geometric algorithms

Non-thermal advanced fuel fusion trades the requirement of a large amount of recirculating tritium in the system for that of large recirculating power. Phase space engineering technologies utilizing externally injected electromagnetic fields can be applied to meet the challenge of maintaining non-thermal particle distributions at a reasonable cost. The physical processes of the phase space engineering are studied from a theoretical and algorithmic perspective. It is emphasized that the operational space of phase space engineering is limited by the underpinning symplectic dynamics of charged particles. The phase space incompressibility according to the Liouville theorem is just one of many constraints, and Gromov's non-squeezing theorem determines the minimum footprint of the charged particles on every conjugate phase space plane. In this sense and level of sophistication, the mathematical abstraction of phase space engineering is symplectic topology. To simulate the processes of phase space engineering, such as the Maxwell demon and electromagnetic energy extraction, and to accurately calculate the minimum footprints of charged particles, recently developed structure-preserving geometric algorithms can be used. The family of algorithms conserves exactly, on discretized spacetime, symplecticity and thus incompressibility, non-squeezability, and symplectic capacities. The algorithms apply to the dynamics of charged particles under the influence of external electromagnetic fields as well as the charged particle–electromagnetic field system governed by the Vlasov–Maxwell equations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The Feasibility of Incorporating a 3D Velocity Model Into Earthquake Location Around Salt Lake City, UT Using a Physics Informed Neural Network

Earthquake location algorithms typically require travel time calculation. Doing this calculation in 3D, despite advances in algorithm efficiency and computational power, can still be prohibitively expensive in terms of resources and storage. Implementation of high-resolution 3D models in routine earthquake location would be a significant step forward in most of the world. Machine learning algorithms have potential to act as substitutes for travel time calculation algorithms or stored travel time tables. We investigate EikoNet - a physics informed neural network machine learning model that estimates travel times very quickly and comes with negligible memory-overhead. Specifically, we apply EikoNet to the Wasatch Fault Community Velocity Model (WFCVM), a highly detailed and complex 3D velocity model of the Salt Lake City, UT region. While routine locations in the area and studies of the 2020 Magna, UT earthquake sequence used a 1D velocity model, a 3D model may help better our understanding the structure of the major fault in the region. Our primary goal was to test the speed, memory requirements, and accuracy of EikoNet compared to a reference eikonal solver. We find that while the EikoNet is exceedingly fast and requires little memory overhead, achieving acceptable accuracy in estimated travel times is difficult and requires extensive computational resources.

58 GEOSCIENCES↗

The future of time and frequency dissemination

I will try to extrapolate the changes in the dissemination of time and frequency information that have taken place during the last 25 years to predict the future developments both in the methods of disseminating time and frequency and in the kinds of customers we will be asked to serve. Two important developments are likely to play pivotal roles in driving the evolution of dissemination. The first is the commercial availability of very high quality clocks -- devices whose performance may eventually rival that of the current generation of primary frequency standards. The widespread use of these devices may blur the traditional distinction between client and server, and may replace it with a more symmetrical interchange of data among peers. The second is the increasing demand for digital time and frequency information driven by the increasing sophistication of everything from traffic lights to electric power meters. The needs of these individual users may not tax the state of the art of primary frequency standards in principle, but their large numbers and wide geographical distribution present a technological challenge that is difficult to meet at a reasonable price using existing methods. Some of these problems may be solved (or at least addressed) using developments in communications and consumer electronics such as the increasing use of fiber-optic telephone circuits and the increasing bandwidth and sophistication of the cable network used to transmit television pictures. To be useful, these advances in hardware must stimulate parallel advances in software algorithms and methods. These advances are more difficult to predict with great confidence, but the developments of the last few years will be examined to provide some indications of the future.

Levine, Judah↗

NUCLEI (SciDAC-4) effort at The University of Tennessee, Knoxville

This Report summarizes the accomplishments of NUCLEI researchers at The University of Tennessee, Knoxville (UTK) and those supported via subcontracts at the University of North Carolina at Chapel Hill (UNC) and Iowa State University (ISU). The SciDAC-4 NUCLEI collaboration had the goal to advance the computation of atomic nuclei and to make reliable predictions including uncertainty estimates for observables of interest. These goals were achieved by developing improved nuclear interactions and currents that are input to the computations, and by advancing the algorithms and techniques used for computing nuclear observables of interest. The computed results advanced our understanding of how nuclei work; they helped to guide and interpret experiments at existing low-energy nuclear physics facilities, the Facility for Rare Isotope Beams, Jefferson Laboratory, and neutrino experiments. In addition, predictions were made that await confrontation with experimental results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

New Approach for Efficient Diagnosis of Large and Complex Space Systems

We propose a novel algorithmic approach and present a new algorithm for solving the diagnosis problem. We report the results of the performance of the new algorithm and compare them with the traditional and standard algorithms. These results show the strong performance of our new algorithm with several orders of magnitude improvement over the traditional approach.

advanced algorithms↗

Full-scale engine demonstration of an advanced sensor failure detection, isolation and accommodation algorithm: Preliminary results

The objective of the advanced detection, isolation, and accommodation (ADIA) program is to improve the overall demonstrated reliability of digital electronic control systems for turbine engines. For this purpose, algorithms were developed which detect, isolate, and accommodate sensor failures using analytical redundancy. Preliminary results of a full scale engine demonstration of the ADIA algorithm are presented. Minimum detectable levels of sensor failures for an F100 turbofan engine control system are determined and compared to those obtained during a previous evaluation of this algorithm using a real-time hybrid computer simulation of the engine.

Merrill, Walter C.↗

Full-scale engine demonstration of an advanced sensor failure detection isolation, and accommodation algorithm - Preliminary results

The objective of the advanced detection, isolation, and accommodation (ADIA) program is to improve the overall demonstrated reliability of digital electronic control systems for turbine engines. For this purpose, algorithms were developed which detect, isolate, and accommodate sensor failures using analytical redundancy. Preliminary results of a full scale engine demonstration of the ADIA algorithm are presented. Minimum detectable levels of sensor failures for an F100 turbofan engine control system are determined and compared to those obtained during a previous evaluation of this algorithm using a real-time hybrid computer simulation of the engine.

Merrill, Walter C.↗