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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.

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Understanding and Modeling Lossy Compression Schemes on HPC Scientific Data

Scientific simulations generate large amounts of floating-point data, which are often not very compressible using the traditional reduction schemes, such as deduplication or lossless compression. The emergence of lossy floating-point compression holds promise to satisfy the data reduction demand from HPC applications; however, lossy compression has not been widely adopted in science production. We believe a fundamental reason is that there is a lack of understanding of the benefits, pitfalls, and performance of lossy compression on scientific data. In this paper, we conduct a comprehensive study on state-of-the-art lossy compression, including ZFP, SZ, and ISABELA, using real and representative HPC datasets. Our evaluation reveals the complex interplay between compressor design, data features and compression performance. The impact of reduced accuracy on data analytics is also examined through a case study of fusion blob detection, offering domain scientists with the insights of what to expect from fidelity loss. Furthermore, the trial and error approach to understanding compression performance involves substantial compute and storage overhead. To this end, we propose a sampling based estimation method that extrapolates the reduction ratio from data samples, to guide domain scientists to make more informed data reduction decisions.

Lu, Tao↗
DOE OSTI2017

Scaling deep learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors --- including NVIDIA, Intel, AMD, and IBM --- have architectural road-maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling --- sometimes encouraged by restricted GPU memory --- NVLink is less important.

Gawande, Nitin A.↗
DOE OSTI2024

Automated Calibration of Parallel and Distributed Computing Simulators: A Case Study

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or “calibrated,” based on ground-truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable tradeoffs between simulation accuracy and simulation speed.

Mc donald, Jesse↗
DOE OSTI2024

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net-Load Forecasting

Accurate net-load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net-load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net-load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.

Bhattacharjee, Kaustav↗
DOE OSTI2020

An Integrated Platform for Collaborative Data Analytics

While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management “how can organizations use knowledge more effectively?”. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.

Oesch, T↗
DOE OSTI2024

Transformational Regional-Scale Earthquake Simulations with the DOE EarthQuake SIMulation Exascale Framework

Earthquakes present worldwide risk to economic and human safety. The 2023 earthquakes in Turkiye provided a reminder of the potential for catastrophic consequences with 50,700 deaths and 15.7 million people affected. The ability to predict ground motions and infrastructure damage for earthquakes continues to be a challenging problem for scientists and engineers. Until now, estimates of ground motions have been performed empirically by looking at sparse data from past earthquakes. This approach can provide statistical information on intensity amplitudes but cannot inform site-specific ground motions essential to developing the most effective resilience. Interest has grown in large-scale computational models to simulate earthquakes at regional scale. The U.S. Department of Energy EarthQuake SIMulation (EQSIM) framework was developed for regional-scale earthquake simulations at unprecedented fidelity, taking advantage of emerging GPU-accelerated systems. This article describes the EQSIM workflow and demonstrates regional-scale simulations with the new computational capability available to scientists in their quest to mitigate future disasters.

58 GEOSCIENCES↗
DOE OSTI2022

Let Each Quantum Bit Choose Its Basis Gates

Near-term quantum computers are primarily limited by errors in quantum operations (or gates) between two quantum bits (or qubits). A physical machine typically provides a set of basis gates that include primitive 2-qubit (2Q) and 1-qubit (1Q) gates that can be implemented in a given technology. 2Q entangling gates, coupled with some 1Q gates, allow for universal quantum computation. In superconducting technologies, the current state of the art is to implement the same 2Q gate between every pair of qubits (typically an XX-or XY-type gate). This strict hardware uniformity requirement for 2Q gates in a large quantum computer has made scaling up a time and resource-intensive endeavor in the lab. We propose a radical idea – allow the 2Q basis gate(s) to differ between every pair of qubits, selecting the best entangling gates that can be calibrated between given pairs of qubits. This work aims to give quantum scientists the ability to run meaningful algorithms with qubit systems that are not perfectly uniform. Scientists will also be able to use a much broader variety of novel 2Q gates for quantum computing. We develop a theoretical framework for identifying good 2Q basis gates on “nonstandard” Cartan trajectories that deviate from “standard” trajectories like XX. We then introduce practical methods for calibration and compilation with nonstandard 2Q gates, and discuss possible ways to improve the compilation. To demonstrate our methods in a case study, we simulated both standard XY-type trajectories and faster, nonstandard trajectories using an entangling gate architecture with far-detuned transmon qubits. We identify efficient 2Q basis gates on these nonstandard trajectories and use them to compile a number of standard benchmark circuits such as QFT and QAOA. Furthermore, our results demonstrate an 8x improvement over the baseline 2Q gates with respect to speed and coherence-limited gate fidelity.

quantum computing↗
DOE OSTI2017

The Open Science Cyber Risk Profile: The Rosetta Stone for Open Science and Cybersecurity

The Open Science Cyber Risk Profile (OSCRP) working group has created a document that motivates scientists by demonstrating how improving their security posture reduces the risks to their science. This effort aims to both bridge the communication gap between scientists and IT security professionals and allows for the effective management of risks to open science caused by IT security threats.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

High-Performance Molecular Dynamics Simulation for Biological and Materials Sciences: Challenges of Performance Portability, Dallas, TX, November 2018.

Highly-optimized parallel molecular dynamics programs have allowed researchers to achieve ground-breaking results in biological and materials sciences. This type of performance has come at the expense of portability: a significant effort is required for performance optimization on each new architecture. Using a metric that emphasizes speedup, we assess key accelerating programming components of four different best-performing molecular dynamics programs - GROMACS, NAMD, LAMMPS and CP2K-- each having a particular scope of application, for contribution to performance and for portability. We use builds with and without these components, tested on HPC systems. We also analyze the code-bases to determine compliance with portability recommendations. We find that for all four programs, the contributions of the non-portable components to speed are essential to the programs' performances; without them we see a reduction in time-to-solution of a magnitude that is insufferable to domain scientists. This characterizes the performance efficiency that must be approached for good performance portability on a programmatic level, suggesting solutions to this difficult problem, which should come from developers, industry and funding institutions, and possibly new research in programming languages.

Computer Science↗
DOE OSTI2016

Analysis and Visualization of Discrete Fracture Networks Using a Flow Topology Graph

We present an analysis and visualization prototype using the concept of a flow topology graph (FTG) for characterization of flow in constrained networks, with a focus on discrete fracture networks (DFN), developed collaboratively by geoscientists and visualization scientists. Our method allows users to understand and evaluate flow and transport in DFN simulations by computing statistical distributions, segment paths of interest, and cluster particles based on their paths. The new approach enables domain scientists to evaluate the accuracy of the simulations, visualize features of interest, and compare multiple realizations over a specific domain of interest. Geoscientists can simulate complex transport phenomena modeling large sites for networks consisting of several thousand fractures without compromising the geometry of the network. However, few tools exist for performing higher-level analysis and visualization of simulated DFN data. The prototype system we present addresses this need. Here, we demonstrate its effectiveness for increasingly complex examples of DFNs, covering two distinct use cases - hydrocarbon extraction from unconventional resources and transport of dissolved contaminant from a spent nuclear fuel repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Computer science↗
DOE OSTI2026

Same Data, Different Audiences: Using Personas to Scope a Supercomputing Job Queue Visualization

Domain-specific visualizations sometimes focus on narrow, albeit important, tasks for one group of users. This focus limits the utility of a visualization to other groups working with the same data. While tasks elicited from other groups can present a design pitfall if not disambiguated, they also present a design opportunity—namely, the development of visualizations that support multiple groups. This development choice presents a trade-off of broadening the scope but limiting support for the more narrow tasks of any one group, which in some cases can enhance the overall utility of the visualization. We investigate this scenario through a design study where we develop Guidepost, a notebook-embedded visualization of data that helps scientists assess compute wait times, machine learning researchers understand prediction accuracy, and system maintainers analyze usage trends. We adapt the use of personas for visualization design from existing literature in the HCI and design domains, applying them to categorize tasks based on their uniqueness across stakeholder personas. Under this model, tasks shared between all groups should be supported by interactive visualizations and tasks unique to each group can be deferred to scripting with notebook-embedded visualization design. We evaluate our visualization through real-world case studies and a task-focused evaluation with nine participants. We observe that together, Guidepost's visual encodings, interactions, and export capabilities support the tasks of our differing personas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Codesign Framework for Online Data Analysis and Reduction

In this paper we discuss our design of a toolset for automating performance studies of composed HPC applications that perform online data reduction and analysis. We describe Cheetah, a new framework for performing parametric studies on coupled applications. Cheetah facilitates understanding the impact of various factors such as process placement, synchronicity of algorithms, and storage vs. compute requirements for online analysis of large data. Ultimately, we aim to create a catalog of performance results that can help scientists understand tradeoffs when designing next-generation simulations that make use of online processing techniques. We illustrate the design choices of Cheetah by using a reaction-diffusion simulation (Gray-Scott) paired with an analysis application to demonstrate initial results of fine-grained process placement on Summit, a pre-exascale supercomputer at Oak Ridge National Laboratory.

Mehta, Kshitij↗
DOE OSTI2018

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations, and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in-memory data size as well. With this work, we consider five categories of data reduction techniques based on their information loss: 1) truly lossless, 2) near lossless, 3) lossy, 4) mesh reduction, and 5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view, and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Not just about sunburn – the ozone hole's profound effect on climate has significant implications for Southern Hemisphere ecosystems

Abstract Climate scientists have concluded that stratospheric ozone depletion has been a major driver of Southern Hemisphere climate processes since about 1980. The implications of these observed and modelled changes in climate are likely to be far more pervasive for both terrestrial and marine ecosystems than the increase in ultraviolet‐B radiation due to ozone depletion; however, they have been largely overlooked in the biological literature. Here, we synthesize the current understanding of how ozone depletion has impacted Southern Hemisphere climate and highlight the relatively few documented impacts on terrestrial and marine ecosystems. Reviewing the climate literature, we present examples of how ozone depletion changes atmospheric and oceanic circulation, with an emphasis on how these alterations in the physical climate system affect Southern Hemisphere weather, especially over the summer season (December–February). These potentially include increased incidence of extreme events, resulting in costly floods, drought, wildfires and serious environmental damage. The ecosystem impacts documented so far include changes to growth rates of South American and New Zealand trees, decreased growth of Antarctic mosses and changing biodiversity in Antarctic lakes. The objective of this synthesis was to stimulate the ecological community to look beyond ultraviolet‐B radiation when considering the impacts of ozone depletion. Such widespread changes in Southern Hemisphere climate are likely to have had as much or more impact on natural ecosystems and food production over the past few decades, than the increased ultraviolet radiation due to ozone depletion.

Robinson, Sharon A.↗
DOE OSTI2019

Progress in multiple genetically modified minipigs for xenotransplantation in China

Abstract Pig‐to‐human organ transplantation provides an alternative for critical shortage of human organs worldwide. Genetically modified pigs are promising donors for xenotransplantation as they show many anatomical and physiological similarities to humans. However, immunological rejection including hyperacute rejection (HAR), acute humoral xenograft rejection (AHXR), immune cell–mediated rejection, and other barriers associated with xenotransplantation must be overcome with various strategies for the genetic modification of pigs. In this review, we summarize the outcomes of genetically modified and cloned pigs achieved by Chinese scientists to resolve the above‐mentioned problems in xenotransplantation. It is now possible to knockout several porcine genes associated with the expression of sugar residues, antigens for (naturally) existing antibodies in humans, including GGTA1, CMAH, and β4GalNT2, and thereby preventing the antigen‐antibody response. Moreover, insertion of human complement‐ and coagulation‐regulatory transgenes, such as CD46, CD55, CD59, and hTBM, can further overcome effects of the humoral immune response and coagulation dysfunction, while expression of regulatory factors of immune responses can inhibit the adaptive immune rejection. Furthermore, transgenic strategies have been developed by Chinese scientists to reduce the potential risk of infections by endogenous porcine retroviruses (PERVs). Breeding of multi‐gene low‐immunogenicity pigs in China is also presented in this review. Lastly, we will briefly mention the preclinical studies on pig‐to‐non‐human primate xenotransplantation conducted in several centers in China.

Pan, Dengke↗