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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Recent Developments of Thermal Protection Materials to Enable Lower Cost Space Missions

Introduction: Starting with the Commercial Crew Program, a new paradigm has emerged at NASA. Rather than designing rockets and spacecrafts for every mission optimized to achieve science, NASA has begun to use a service-based model and utilizing public-private partnership in developing the vehicles that can bring broader benefits as well as lower the cost for NASA missions. Commercial companies own and operate those vehicles. This allows NASA to not design missions from the bottom up, and has cost, risk, and schedule savings implications. On the other side, the constraints require meeting the requirements in terms of mass, volume, power, etc. By leveraging NASA developed technologies, commercial companies can quickly demonstrate the commercial mission concept, and, through technology transfer, adopt needed technology to address supply chain problems. A downside is that the technology has to be sufficiently mature to be transferred by NASA, which means that it requires significant investment, expertise, and time to develop. Space entities are focused on rapid development with an emphasis on manufacturing and integration innovation with reduced cost and schedule and quick entrance into the market. Thermal Protection Systems (TPS) are mission critical, but their development takes years, and involve access to arc jets or unique test facilities. Therefore, their development is both risky and investment heavy. NASA ARC developed several new TPS materials over the last decade (C-PICA, HEEET, 3MDCP, 3DMAT, ADEPT woven TPS) and brought them to high TPS maturity, making them enablers for commercials space missions from LEO, Lunar Sample Return, Mars, and Venus missions. LEO missions are relevant to future Mars missions due to the comparable entry conditions. External Partners' Missions: C-PICA is a recent improvement on NASA’s heritage PICA lightweight TPS ablator. C-PICA is now considered an enabling technology for New Frontiers and other NASA missions. C-PICA was infused into several missions from external partners. Varda Space Industries’ Winnebago-1 spacecraft successfully returned to Earth from LEO on Feb. 21st, 2024, using a C-PICA heatshield. Inversion Space’s Ray vehicle will test both ARC’s C-PICA and SIRCA TPS materials on a LEO return mission later in 2024. The Kentucky Re-Entry Probe Experiment (KREPE) is another example of a low-cost flight experiment to demonstrate the use of small entry capsules to gather data with three instrumented Kentucky Re-entry and Universal Payload System (KRUPS) capsules. NASA Arc provided C-PICA and Soft-PICA for two of the next KRUPS capsules scheduled to re-enter Earth from the ISS later in 2024. Finally, Rocket Lab’s low-cost mission to Venus, scheduled to launch in December 2024, will search for habitable conditions in Venus’ cloud layer, making use of NASA ARC provided HEEET insulation layer heat shield, and SIRCA backshell TPS materials. Future NASA Missions: NASA’s ability to help commercial missions can lead to future low-cost missions for several reasons: Competition encourages lower cost; technology maturation is now done at an integrated system level; and a common design architecture between commercial and scientific applications requires no specialized engineering design. From an engineering perspective, both of these commercial LEO capsules aforementioned are capable of a Mars entry; the commercial payload mut be replaced with as science payload. Finally, several NASA mission concepts, that could be candidates for future SIMPLEx program calls, such as VATMOS-SR and Nephele, both proposing to target the Venusian atmosphere, would make use of the HEEET insulation layer TPS for part of their heat shield.

TPS materials↗
NASA NTRSDate not supplied

ULTRASAT: NASA’s Role in Mission Development and Science

ULTRASAT is a near-ultraviolet imaging satellite with a wide field of view (200 square degrees) and an anticipated launch in late 2027. It is an international partnership led by Israel (Israel Space Agency & Weizmann Institute of Science) in partnership with the United States (NASA) and Germany (DESY). ULTRASAT will provide high cadence observations and rapid target-of-opportunity response, providing a powerful capability for time-domain and multimessenger astrophysics (TDAMM), and will have scientific applications from solar system studies to cosmology.

James E Rhoads↗
NASA NTRSDate not supplied

Improving GES Disc Data Search and Discovery Through AI Metadata Augmentation

NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.

Kendall Gilbert↗
DOE OSTI2020

Low Threshold Acquisition controller for Skipper CCDs

The development of the Skipper Charge Coupled Devices (Skipper-CCDs) has been a major technological breakthrough for sensing very weak ionizing particles. The sensor allows to reach the ultimate sensitivity of silicon material as a charge signal sensor by unambiguous determination of the charge signal collected by each cell or pixel, even for single electron-hole pair ionization. Extensive use of the technology was limited by the lack of specific equipment to operate the sensor at the ultimate performance. In this work a simple, single-board Skipper-CCD controller is presented, aimed for the operation of the detector in high sensitivity scientific applications. The article describes the main components and functionality of the Low Threshold Acquisition (LTA) together with experimental results when connected to a Skipper-CCD sensor. Measurements show unprecedented deep sub-electron noise of 0.039 e$^-_{rms}$/pix for 5000 pixel measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗
DOE OSTI2020

Locality-Aware Scheduling for Scalable Heterogeneous Environments

Heterogeneous computing promise boost performance of scientific applications by allowing massively parallel execution of computational tasks. However, manually managing extremely heterogeneous, multi-device systems is complicated and may result in sub-optimal performance. Specifically, data management is an extremely challenging problem on multi-device systems. In this work, we introduce two locality-aware schedulers for the Minos Computing Library (MCL), an asynchronous, task-based programming model and runtime for extremely heterogeneous systems. The first scheduler implements a pure locality-aware algorithm to maximize data reuse, though it might incur in ”hot-spots” that limit system utilization. The second scheduler mitigates this drawback by dynamically targeting between locality-awareness and system utilization based on the current workload and available computing devices. Our results show that locality-awareness greatly benefit applications that exhibit data reuse, providing up to 6.9x and 7.9x over the original MCL scheduler and equivalent OpenCL implementations, respectively. Moreover, our schedulers introduce negligible overhead compared with the original MCL scheduler and achieve similar performance for applications that don’t benefit from data locality.

Architecture, co-design, Task-based programming mo↗
DOE OSTI2021

A prediction interval method for uncertainty quantification of regression models

This paper considers calculation of prediction intervals (PIs) by neural networks (NNs) for quantifying uncertainty in regression tasks, so as to provide fast, accurate and robust emulators to accelerate scientific simulations. We propose a novel method to learn lower and upper bounds of the PI using independent NNs without defining an exclusive loss. Our method requires no distributional assumption, does not introduce extra hyper-parameters, and can effectively identify out-of-distribution samples and quantify their uncertainty. We demonstrate advantages of our method using a benchmark problem and two real-world scientific applications.

Zhang, Pei↗
DOE OSTI2021

A Community Roadmap for Scientific Workflows Research and Development

The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges and lay the groundwork for transforming workflows research and development, the WorkflowsRI and ExaWorks projects partnered to bring the international workflows community together. This paper reports on discussions and findings from two virtual “Workflows Community Summits” (January and April, 2021). The overarching goals of these workshops were to develop a view of the state of the art, identify crucial research challenges in the workflows community, articulate a vision for potential community efforts, and discuss technical approaches for realizing this vision. To this end, participants identified six broad themes: FAIR computational workflows; AI workflows; exascale challenges; APIs, interoperability, reuse, and standards; training and education; and building a workflows community. We summarize discussions and recommendations for each of these themes.

Ferreira Da Silva, Rafael↗
DOE OSTI2022

Accelerating Collective Communication in Data Parallel Training across Deep Learning Frameworks

This work develops new techniques within Horovod, a generic communication library supporting data parallel training across deep learning frameworks. In particular, we improve the Horovod control plane by implementing a new coordination scheme that takes advantage of the characteristics of the typical data parallel training paradigm, namely the repeated execution of collectives on the gradients of a fixed set of tensors. Using a caching strategy, we execute Horovod’s existing coordinator-worker logic only once during a typical training run, replacing it with a more efficient decentralized orchestration strategy using the cached data and a global intersection of a bitvector for the remaining training duration. Next, we introduce a feature for end users to explicitly group collective operations, enabling finer grained control over the communication buffer sizes. To evaluate our proposed strategies, we conduct experiments on a world-class supercomputer — Summit. We compare our proposals to Horovod’s original design and observe 2x performance improvement at a scale of 6000 GPUs; we also compare them against tf.distribute and torch.DDP and achieve 12% better and comparable performance, respectively, using up to 1536 GPUs; we compare our solution against BytePS in typical HPC settings and achieve about 20% better performance on a scale of 768 GPUs. Finally, we test our strategies on a scientific application (STEMDL) using up to 27,600 GPUs (the entire Summit) and show that we achieve a near-linear scaling of 0.93 with a sustained performance of 1.54 exaflops (with standard error +- 0.02) in FP16 precision.

Romero, Joshua↗
DOE OSTI2021

Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operationson Spatial Accelerators

To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these“domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually. To address this challenge as a whole, in this work, we present an HW-SW co-design ecosystem for spatial accelerators called Union within the popular MLIR compiler infrastructure. Our framework allows exploring different algorithms and their mappings on several accelerator cost models. Union also includes a plug-and-play library of accelerator cost models and mappers which can easily be extended. The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper. We demonstrate the value of Union for the community with several case studies which examine offloading different tensor operations (CONV/GEMM/Tensor Contraction) on diverse accelerator architectures using different mapping schemes.

Jeong, Geonhwa↗
DOE OSTI2024

Towards Precision Photometric Type ia Supernova Cosmology With Machine Learning

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia and the upcoming Rubin Legacy Survey of Space and Time (LSST) and Roman Space Telescope promise to deliver millions in the next decade. This unprecedented data volume can produce the required precision to unambiguously test concordance cosmology which could represent a monumental shift in our understanding of dark energy and its role in cosmic history. However, extracting a pure SN Ia sample with accurate redshifts for such a large dataset will be a challenge. Specifically, spectroscopic classification will not be possible for the vast majority of discovered objects, and only ~25% will have spectroscopic redshifts. This thesis presents a series of observational and methodological studies designed to address the questions associated with this new era of photometric SN Ia cosmology. First, we present a machine learning method for photometric classification of SNe, Supernova Classification with a COnvolutional Neural Network (SCONE). Photometric classification enables SNe with no spectroscopic information to be confidently categorized, and is a critical component of current and future analysis pipelines. SCONE achieves >99% accuracy distinguishing simulated SNe Ia from non-Ia SNe, and has been integrated into DES, LSST, and Roman analysis pipelines. We also demonstrate the efficacy of SCONE on early-time photometric classification, which will be vital for optimal allocation of spectroscopic resources. We show that SCONE can distinguish between 6 SN types with 75% accuracy on the night of initial discovery, comparable to results in the literature for full-phase SNe. Next, we study current methods for estimating SN Ia redshifts and propose a machine learning alternative that uses SN photometry alone to extract redshift information. Most SNe Ia inherit redshift information from their host galaxy, but the process of matching SNe to the correct host galaxy can be challenging. We systematically analyze the impact of incorrect redshifts from host galaxy mismatch on 5 years of DES SN data, and conclude that improved host matching or redshift estimation methods can reduce our systematic errors by ~10%. In response to this finding, we present a SN photometry-only method for estimating redshifts independent of host galaxy information, Photo-zSNthesis. We show that Photo-zSNthesis redshift estimates are accurate to within 2% across the full redshift range of LSST, a first in the literature. Finally, we focus on the robustness of machine learning (ML) algorithms for real-world and scientific applications. ML models generalize poorly beyond their training set and often experience severe performance degradation when deployed on new data. We demonstrate a general method for improving robustness that achieves new state-of-the-art results on astronomical object classification, wildlife identification, and tumor detection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces

Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, including many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process using exact (as opposed to variational) analysis. We relate the theory to the existing continuous-state Gaussian diffusion as well as other approaches to discrete diffusion, and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce “Blackout Diffusion”, which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10, Binarized MNIST, and CelebA datasets confirm the feasibility of our approach. Generalizing from specific (Gaussian) forward processes to discrete-state processes without a variational approximation sheds light on how to interpret diffusion models, which we discuss.

Santos, Javier E.↗
DOE OSTI2025

Performance Portability Evaluation of Fluid-Structure Interaction Simulations on Heterogeneous Platforms

The rapid proliferation of heterogeneous programming languages and multi-vendor hardware has underscored the critical need to evaluate the performance portability of scientific applications. In this work, we present the systematic porting and optimization of a massively parallel fluid-structure interaction code across multiple heterogeneous programming frameworks for deployment on leadership-class supercomputers from major vendors. Our analysis focuses on at-scale performance for simulations involving hundreds of millions of deformable cells, executed on a combination of CPUs and GPUs spanning thousands of nodes on exascale machines. We benchmark the performance of each implementation, highlighting the trade-offs inherent in adopting diverse programming models. Key insights regarding the portability of CUDA on multi-vendor platforms, the superior multi-core CPU performance from SYCL, and architectural considerations on performance optimization are distilled from our experience, offering guidance to other users of high performance computing based on our findings.

Martin, Aristotle [Duke University]↗
DOE OSTI2025

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications

In this work we introduce a novel two-level overlapping additive Schwarz preconditioner for accelerating the training of scientific machine learning applications. The design of the proposed preconditioner is motivated by the nonlinear two-level overlapping additive Schwarz preconditioner. The neural network parameters are decomposed into groups (subdomains) with overlapping regions. In addition, the network’s feed-forward structure is indirectly imposed through a novel subdomain-wise synchronization strategy and a coarse-level training step. Through a series of numerical experiments, which consider physicsinformed neural networks and operator learning approaches, we demonstrate that the proposed two-level preconditioner significantly speeds up the convergence of the standard (LBFGS) optimizer while also yielding more accurate machine learning models. Moreover, the devised preconditioner is designed to take advantage of model-parallel computations, which can further reduce the training time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Viskores: Integrating Parallel Scientific Visualization Research into Applications

Viskores is a scientific visualization library that is the primary deployment of such algorithms to the parallel accelerated processors of modern DOE supercomputers. In this paper, we review the capabilities provided by Viskores and how these capabilities are leveraged by other software in the high-performance computing ecosystem. We discuss the Viskores data representation and pay particular attention to array management. Through this array management we describe how data is adapted between Viskores and other software along with strategies for converting dynamic, polymorphic objects to static representations better suited to GPU processing. We conclude with several examples of Viskores integrating with high-performance software that is used in production today.

Moreland, Ken [ORNL] (ORCID:0000000270513288)↗
NASA NTRS1986

Physics through the 1990s: Scientific interfaces and technological applications

The volume examines the scientific interfaces and technological applications of physics. Twelve areas are dealt with: biological physics-biophysics, the brain, and theoretical biology; the physics-chemistry interface-instrumentation, surfaces, neutron and synchrotron radiation, polymers, organic electronic materials; materials science; geophysics-tectonics, the atmosphere and oceans, planets, drilling and seismic exploration, and remote sensing; computational physics-complex systems and applications in basic research; mathematics-field theory and chaos; microelectronics-integrated circuits, miniaturization, future trends; optical information technologies-fiber optics and photonics; instrumentation; physics applications to energy needs and the environment; national security-devices, weapons, and arms control; medical physics-radiology, ultrasonics, MNR, and photonics. An executive summary and many chapters contain recommendations regarding funding, education, industry participation, small-group university research and large facility programs, government agency programs, and computer database needs.

Source record↗
DOE OSTI2023

Research and Development of Advanced Superconducting Radiofrequency (SRF) Accelerator Technology and Components for Scientific and Industrial Applications

The purpose of this CRADA is to provide for the joint conduct of research and development (R&D) investigations between Fermi Research Alliance, LLC (Contractor), as operator of the Fermi National Accelerator Laboratory (Fermilab) and Euclid TechLabs, LLC (Participant) related to Research and Development of Advanced Superconducting Radiofrequency (SRF) Accelerator Technology and Components for Scientific and Industrial Applications.

43 PARTICLE ACCELERATORS↗
NASA NTRS2016

Performance Evaluation of an Intel Haswell- and Ivy Bridge-Based Supercomputer Using Scientific and Engineering Applications

We present a performance evaluation conducted on a production supercomputer of the Intel Xeon Processor E5- 2680v3, a twelve-core implementation of the fourth-generation Haswell architecture, and compare it with Intel Xeon Processor E5-2680v2, an Ivy Bridge implementation of the third-generation Sandy Bridge architecture. Several new architectural features have been incorporated in Haswell including improvements in all levels of the memory hierarchy as well as improvements to vector instructions and power management. We critically evaluate these new features of Haswell and compare with Ivy Bridge using several low-level benchmarks including subset of HPCC, HPCG and four full-scale scientific and engineering applications. We also present a model to predict the performance of HPCG and Cart3D within 5%, and Overflow within 10% accuracy.

Ivy Bridge↗