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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments

The annual Workshop on Workflows in Support of Large-Scale Science (WORKS) is a premier venue for the scientific workflow community to present the latest advances in research and development on the many facets of scientific workflows throughout their life-cycle. The Lightning Talks at WORKS focus on describing a novel tool, scientific workflow, or concept, which are work-in-progress and address emerging technologies and frameworks to foster discussion in the community. This paper summarizes the lightning talks at the 2023 edition of WORKS, covering five topics: leveraging large language models to build and execute workflows; developing a common workflow scheduler interface; scaling uncertainty workflow applications on exascale computing systems; evaluating a transcriptomics workflow for cloud vs. HPC systems; and best practices in migrating legacy workflows to workflow management systems.

Titov, Mikhail↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗
DOE OSTI2025

A Typology of Quantum-Classical Faults

This paper introduces an extended taxonomy of faults specific to hybrid quantum-classical systems, addressing the unique challenges that arise from integrating quantum accelerators into high-performance computing (HPC) infrastructures. Building on the foundational fault classification by Avizienis et al., we incorporate fault types unique to quantum computing-such as qubit decoherence, spontaneous gate errors, and photon loss-alongside traditional and human-induced faults including development errors, operational mistakes, and malicious attacks. Our taxonomy classifies faults by their origin (natural vs. human-made), intent (accidental, deliberate non-malicious, or malicious), system boundaries (internal vs. external), and persistence (transient to permanent). We also explore how different architectural integration patterns-ranging from tight coupling to loose on-premise and cloud-based configurations-shape the manifestation and propagation of faults. These scenarios are analyzed in terms of timing mismatches, interface inconsistencies, and security threats such as data tampering and denial-of-service attacks. Through this fault-centric lens, we aim to support the co-design of dependable quantum-classical systems and highlight the critical role that integration strategies play in ensuring reproducibility, resilience, and security across hybrid computing platforms.

Giusto, Edorado [University of Naples Federico II,↗