Search NASA⌕ Search

DOE OSTI · 1036270

Framework Application for Core Edge Transport Simulations (FACETS) Performance Analysis (Final Report)

Abstract

The goal of the FACETS project (Framework Application for Core-Edge Transport Simulations) was to provide a multiphysics, parallel framework application (FACETS) that will enable whole-device modeling for the U.S. fusion program, to provide the modeling infrastructure needed for ITER, the next step fusion confinement device. Through use of modern computational methods, including component technology and object oriented design, FACETS is able to switch from one model to another for a given aspect of the physics in a flexible manner. This enables use of simplified models for rapid turnaround or high-fidelity models that can take advantage of the largest supercomputer hardware. FACETS does so in a heterogeneous parallel context, where different parts of the application execute in parallel by utilizing task farming, domain decomposition, and/or pipelining as needed and applicable. ParaTools, Inc. was tasked with supporting the performance analysis and tuning of the FACETS components and framework in order to achieve the parallel scaling goals of the project. The TAU Performance System® was used for instrumentation, measurement, archiving, and profile / tracing analysis. ParaTools, Inc. also assisted in FACETS performance engineering efforts. Through the use of the TAU Performance System, ParaTools provided instrumentation, measurement, analysis and archival support for the FACETS project. Performance optimization of key components has yielded significant performance speedups. TAU was integrated into the FACETS build for both the full coupled application and the UEDGE component. The performance database provided archival storage of the performance regression testing data generated by the project, and helped to track improvements in the software development.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Malony, Allen D., Shende, Sameer S., Morris, Alan, Spear, Wyatt, Huck, Kevin A.. 2012-03-14. Framework Application for Core Edge Transport Simulations (FACETS) Performance Analysis (Final Report). https://doi.org/10.2172/1036270

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↗