Search NASA⌕ Search

DOE OSTI · 1769130

Summarizing the interoperabilities between xSDK members

Abstract

Rapid, efficient production of high-quality, sustainable extreme-scale scientific applications is best accomplished using a rich ecosystem of state-of-the art reusable libraries, tools, lightweight frameworks, and defined software methodologies, developed by a community of scientists who are striving to identify, adapt, and adopt best practices in software engineering. The vision of the xSDK is to provide infrastructure for and interoperability of a collection of related and complementary software elements — developed by diverse, independent teams throughout the high-performance computing (HPC) community — that provide the building blocks, tools, models, processes, and related artifacts for rapid and efficient development of high-quality applications. A primary component, and challenge, of the xSDK is to improve interoperability among software libraries and domain components. This document summarizes the current and planned interoperabilities of the twenty-three xSDK member packages as of March 2021. Additionally, the status of the xSDK example codes that demonstrate and test the interoperabilities within the xSDK is provided.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Balos, Cody, Balay, Satish, Fisher, Aaron, Lusczcek, Piotr, Osborn, Sarah, Yang, Ulrike. 2021-03-04. Summarizing the interoperabilities between xSDK members. https://doi.org/10.2172/1769130

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING↗