SEARCH · Search NASA

Results for “scalable computing”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31

All sources NASA NTRS DOE OSTI

DOE OSTI2012

Improving the Performance Scalability of the Community Atmosphere Model

The Community Atmosphere Model (CAM), which serves as the atmosphere component of the Community Climate System Model (CCSM), is the most computationally expensive CCSM component in typical configurations. On current and next-generation leadership class computing systems, the performance of CAM is tied to its parallel scalability. Improving performance scalability in CAM has been a challenge, due largely to algorithmic restrictions necessitated by the polar singularities in its latitude-longitude computational grid. Nevertheless, through a combination of exploiting additional parallelism, implementing improved communication protocols, and eliminating scalability bottlenecks, we have been able to more than double the maximum throughput rate of CAM on production platforms. We describe these improvements and present results on the Cray XT5 and IBM BG/P. The approaches taken are not specific to CAM and may inform similar scalability enhancement activities for other codes.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

Efficient I/O for Neural Network Training with Compressed Data

FanStore is a shared object store that enables efficient and scalable neural network training on supercomputers. By providing a global cache layer on node-local burst buffers using a compressed representation, it significantly enhances the processing capability of deep learning (DL) applications on existing hardware. In addition, FanStore allows POSIX-compliant file access to the compressed data in user space. We investigate the tradeoff between runtime overhead and data compression ratio using real-world datasets and applications, and propose a compressor selection algorithm to maximize storage capacity given performance constraints. We consider both asynchronous (i.e., with prefetching) and synchronous I/O strategies, and propose mechanisms for selecting compressors for both approaches. Using FanStore, the same storage hardware can host 2-13x more data for example applications without significant runtime overhead. Empirically, our experiments show that FanStore scales to 512 compute nodes with near linear performance scalability.

Zhang, Zhao↗

DOE OSTI2022

Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP's computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today's noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices--which appears to be decades away--hinder practical applications of current QEMTP methods. Here, we devise a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) a shallow-depth QEMTP quantum circuit for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTPs performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Exploiting Kubernetes to Simplify the Deployment and Management of the Multi-purpose CMS Pilot Job Factory

GlideinWMS, a widely utilized workload management system in high-energy physics (HEP) research, serves as the backbone for efficient job provisioning across distributed computing resources. It is utilized by various experiments and organizations, including CMS, OSG, Dune, and FIFE, to create HTCondor pools as large as 600k cores. In particular, a shared factory service historically deployed at UCSD has been configured to interface with more than 500 routes to compute clusters. As part of our team’s initiative to modernize infrastructure and enhance scalability, we undertook the migration of the GlideinWMS factory service into the Kubernetes environment. Leveraging the flexibility and orchestration capabilities of Kubernetes, we successfully deployed the factory service within the OSG Tiger Kubernetes cluster. The major benefits Kubernetes gives us is it streamlines the management and monitoring of the factory infrastructure, and improves fault tolerance through its resilient deployment strategies. Through this case study, we aim to share insights, challenges, and best practices encountered during the migration process. Our experience underscores the benefits of embracing containerization and Kubernetes orchestration for HEP computing infrastructure, paving the way for scalability and resilience in distributed computing environments.

Dost, Jeffrey Michael [UC, San Diego (main)]↗

DOE OSTI2026

Materials Studies of Niobium Thin Films for Quantum Circuit Applications: Progress and Challenges

Niobium (Nb) films have emerged as a crucial material in the development of superconducting qubits, which are key components in quantum computing technology. Here, this review provides a comprehensive examination of Nb films from a materials perspective, focusing on their intrinsic properties, fabrication methods/techniques, and their influence on qubit performance, particularly through surface and interface driven loss mechanisms. We discuss the key material properties that are essential for qubit operation. Various deposition techniques for Nb thin films, such as sputtering, evaporation, molecular beam epitaxy, and atomic layer deposition, are explored, alongside their impact on film quality, uniformity, and qubit performance. Additionally, the influence of surface roughness, thin-film thickness, and substrate materials on quantum coherence is analyzed. Challenges such as defects and material degradation in Nb films are reviewed, along with strategies to mitigate these issues. Finally, we present the latest advancements and future directions in Nb film research, including potential improvements to enhance qubit coherence and scalability for large-scale quantum computing systems. Ultimately, a deeper understanding of surface and interface phenomena is essential for pushing the limits of qubit performance and realizing next-generation quantum technologies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2012

Scalable Algorithms for Distributed-Memory Adaptive Mesh Refinement

This paper presents scalable algorithms and data structures for adaptive mesh refinement computations. We describe a novel mesh restructuring algorithm for adaptive mesh refinement computations that uses a constant number of collectives regardless of the refinement depth. To further increase scalability, we describe a localized hierarchical coordinate-based block indexing scheme in contrast to traditional linear numbering schemes, which incur unnecessary synchronization. In contrast to the existing approaches which take O(P) time and storage per process, our approach takes only constant time and has very small memory footprint. With these optimizations as well as an efficient mapping scheme, our algorithm is scalable and suitable for large, highly-refined meshes. We present strong-scaling experiments up to 2k ranks on Cray XK6, and 32k ranks on IBM Blue Gene/Q.

Langer, Akhil↗

DOE OSTI2026

Harnessing the Second-Order Metal−Insulator Transition for Neuromorphic Computing

Vanadium oxides are widely studied phase change materials for brain-inspired computing architectures. Systems like VO 2 and V 2 O 3 exhibit first-order metal−insulator transitions (MITs) with hysteresis and percolative switching, increasing stochasticity and device variability. Here, we focus on the less explored Magnéli phase V 4 O 7 , which undergoes a continuous, non-hysteretic, second-order MIT. This surprisingly enables highly reproducible volatile resistive switching in spiking-neuron-type devices. We synthesize V 4 O 7 films, characterize their structural and transport properties, and demonstrate voltage and current-driven threshold switching with electrothermal feedback. In a Pearson–Anson oscillator, V 4 O 7 devices produce stable, tunable spiking across 20–200 kHz, with consistent operation among multiple devices. We introduce a numerical analog leaky-integrate-and-fire (aLIF) model that captures waveform shapes and their dependence on load resistance, temperature, and voltage. Furthermore, these findings suggest that second-order MIT materials like V 4 O 7 are promising for deterministic, scalable spiking neuron arrays for neuromorphic computing.

V4O7↗

DOE OSTI2018

Scalable Multilevel Uncertainty Quantification Concepts for Extreme-Scale Multiscale Problems

Our work in this project is aimed at making fundamental advances in multilevel and multiscale methods for problems with uncertainties related to flows in porous media. The main thrust of this research is to develop a systematic multiscale, multilevel parallel approaches for problems with uncertainties and use them in the uncertainty quantification. A key emphasis is on problems without an apparent scale separation in space, time, and uncertainty spaces. Multiscale solution methods are currently under active investigation. However, the develop- ment of multiscale methods for problems with uncertainties where the scales and uncertainties are tightly coupled is a research area that is less explored. During this project, we have made some significant contributions to the developments of multiscale methods, the uncertainty quantification for multiscale methods, multiscale model learning, uncertainty quantification in inverse problems, and applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Porting hypre to heterogeneous computer architectures: Strategies and experiences

We report that linear systems are occurring in many applications, and solving them can take a large amount of the total simulation time. The high performance library hypre provides a variety of interfaces and linear solvers, including various multigrid methods, that have achieved good scalability on a variety of homogeneous parallel computer architectures. Heterogeneous architectures with nodes that have both CPUs and accelerators provide new challenges, since they require more fine-grained parallelism and reduced data movement between different memories on a single node as well as across nodes. We will discuss our experiences and strategies to port hypre to heterogeneous computers with accelerators, including the design of a new memory model, the use of abstractions, the BoxLoop macros in the structured and semi-structured interfaces, and the restructuring of algebraic multigrid (AMG) into modular components. We present numerical experiments comparing CPU and GPU performance for several test problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Massively scalable Kerr comb-driven silicon photonic link

Abstract The growth of computing needs for artificial intelligence and machine learning is critically challenging data communications in today’s data-centre systems. Data movement, dominated by energy costs and limited ‘chip-escape’ bandwidth densities, is perhaps the singular factor determining the scalability of future systems. Using light to send information between compute nodes in such systems can dramatically increase the available bandwidth while simultaneously decreasing energy consumption. Through wavelength-division multiplexing with chip-based microresonator Kerr frequency combs, independent information channels can be encoded onto many distinct colours of light in the same optical fibre for massively parallel data transmission with low energy. Although previous high-bandwidth demonstrations have relied on benchtop equipment for filtering and modulating Kerr comb wavelength channels, data-centre interconnects require a compact on-chip form factor for these operations. Here we demonstrate a massively scalable chip-based silicon photonic data link using a Kerr comb source enabled by a new link architecture and experimentally show aggregate single-fibre data transmission of 512 Gb s −1 across 32 independent wavelength channels. The demonstrated architecture is fundamentally scalable to hundreds of wavelength channels, enabling massively parallel terabit-scale optical interconnects for future green hyperscale data centres.

Rizzo, Anthony (ORCID:000000034752797X)↗

DOE OSTI2025

Toucan: A performance portable, scalable implementation of the DECA algorithm

In the field of additive manufacturing (AM), cellular automata (CA) is extensively used to simulate microstructural evolution during solidification. However, while traditional CA approaches are relatively fast, they still require a substantial number of time steps, are limited to moderate volumes, and are relatively difficult to improve through parallelism due to the highly localized nature of the solidification front. Here, to address these issues of time to solution and load balancing, we introduce Toucan, a parallel, performance-portable, and scalable code written in C++ with the Kokkos library that leverages the discrete event inspired cellular automata (DECA) algorithm to perform parallel-in-time (PinT) grain growth simulations. Toucan effectively mitigates load balancing issues by distributing the computational workload more evenly across processors, enhancing scalability and efficiency. We conduct both strong and weak scaling studies on up to 64 GPUs on the Frontier supercomputer, demonstrating that Toucan significantly outperforms the current state-of-the-art, time-stepped CA code, ExaCA, on both single and multi-GPU simulations. Even in AM-specific weak scaling scenarios, Toucan maintains near-ideal scaling, in contrast to the linear increase observed with ExaCA due to the moving laser raster pattern. This study highlights Toucan’s potential to transform microstructural simulations in AM by radically improving both efficiency and scalability over existing methods.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Virtual Flow Simulator

Virtual Flow Simulator (VFS) is a state-of-the-art computational fluid mechanics (CFD) package that is capable of simulating multi-physics/multi-phase flows with the most advanced turbulence models (RANS, LES) over complex terrains. The flow solver is based on the Curvilinear Immersed Boundary (CURVIB) method to handle geometrically complex and moving domains. Different modules of the VFS package can provide different simulation capabilities for specific applications ranging from the fluid-structure interaction (FSI) of solid and deformable bodies, the two-phase free surface flow solver based on the level set method for ocean waves, sediment transport models in rivers and the large-scale models of wind farms based on actuator lines and surfaces. All numerical features of VFS package have been validated with known analytical and experimental data as reported in the related journal articles. VFS package is suitable for a broad range of engineering applications within different industries. VFS has been used in different projects with applications in wind and hydrokinetic energy, offshore and near-shore ocean studies, cardiovascular and biological flows, and natural streams and river morphodynamics. Over the last decade, the development of VFS has been supported and assisted with the help of various United States companies and federal agencies that are listed in the sponsor lists. In this version, VFS-Wind contains all the necessary modeling tools for wind energy applications, including land-based and offshore wind farms. VFS is highly scalable to run on either desktop computers or high performance clusters (up to 16,000 CPUs). This released version comes with a detailed user’s manual and a set of case studies designed to facilitate the learning of the various aspects of the code in a comprehensive manner. The included documentation and support material has been elaborated in a collaboration effort with Sandia National Labs under the contract DE-EE0005482. The VFS-Wind source code is distributed under the GNU General Public License (GPL 2.0), offering to the community the flexibility to use and expand the capabilities of the package. We note that future publications including academic journals that result from the use of VFS must be cited appropriately under the GPL v.2 license agreement.

Yang, Xiaolei↗

DOE OSTI2015

Virtual Flow Simulator

Calderer, Antoni↗

DOE OSTI2019

Early Career Award: An Enabling Computational Framework for Uncertainty Assimilation and Propagation in Complex PDE Systems: Sparse and Low-rank Techniques (Final Report)

This project aims to develop a set of new theories, algorithms, and computational tools that enable ultra-scalable representation and propagation of high-dimensional uncertainties to circumvent the issue of curse-of-dimensionality. The research components are motivated by the fact that highly scalable algorithms are needed to achieve exascale performance for uncertainty quantification and predictive simulation at extreme scales. Although these developments are generic and applicable to a wide range of high-dimensional, complex, stochastic, partial-differential-equation (PDE) systems, this research effort will focus on predictive simulation of high-energy-density lithium batteries. To create knowledge about the precise failure mechanisms of these batteries and ultimately predict their performance and reliability, a truly data-driven, multi-scale, multi-physics framework is needed that allows studying (i) dominant sources of uncertainty in the governing physical models at all scales of relevance and (ii) the interface between the interchanging physics and scales. The impact of the planned mathematical and numerical advancements in prediction of high-dimensional uncertain systems will be broad across areas of engineering and science such as climate modeling, fusion energy, combustion, and nanoscience, among others.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Atomistic non-adiabatic dynamics of the LH2 complex with a GPU-accelerated ab initio exciton model

We recently outlined an efficient multi-tiered parallel ab initio excitonic framework that utilizes time dependent density functional theory (TDDFT) to calculate ground and excited state energies and gradients of large supramolecular complexes in atomistic detail – enabling us to undertake non-adiabatic simulations which explicitly account for the coupled anharmonic vibrational motion of all the constituent atoms in a supramolecular system. Here we apply that framework to the 27 coupled bacterio-chlorophyll-a chromophores which make up the LH2 complex, using it to compute an on-the-fly nonadiabatic surface-hopping (SH) trajectory of electronically excited LH2. Part one of this article is focussed on calibrating our ab initio exciton Hamiltonian using two key parameters: a shift δ, which corrects for the error in TDDFT vertical excitation energies; and an effective dielectric constant ε, which describes the average screening of the transition-dipole coupling between chromophores. Using snapshots obtained from equilibrium molecular dynamics simulations (MD) of LH2, we tune the values of both δ and ε through fitting to the thermally broadened experimental absorption spectrum, giving a linear absorption spectrum that agrees reasonably well with experiment. In part two of this article, we construct a time-resolved picture of the coupled vibrational and excitation energy transfer (EET) dynamics in the sub-picosecond regime following photo-excitation. Assuming Franck–Condon excitation of a narrow eigenstate band centred at 800 nm, we use surface hopping to follow a single nonadiabatic dynamics trajectory within the full eigenstate manifold. Consistent with experimental data, this trajectory gives timescales for B800→B850 population transfer (τ B800→B850 ) between 650–1050 fs, and B800 population decay (τ 800→ ) between 10–50 fs. The dynamical picture that emerges is one of rapidly fluctuating LH2 eigenstates that are delocalized over multiple chromophores and undergo frequent crossing on a femtosecond timescale as a result of the atomic vibrations of the constituent chromophores. The eigenstate fluctuations arise from disorder that is driven by vibrational dynamics with multiple characteristic timescales. The scalability of our ab initio excitonic computational framework across massively parallel architectures opens up the possibility of addressing a wide range of questions, including how specific dynamical motions impact both the pathways and efficiency of electronic energy-transfer within large supramolecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2012

Petascale Data Storage Institute (Final Report)

pNFS is an extension to NFSv4 that helps clients overcome NFS scalability and performance barriers. Like NFS, pNFS is a client/server protocol implemented with secure and reliable remote procedure calls. pNFS departs from conventional NFS by allowing clients to access storage directly and in parallel. This helps overcome server bottlenecks inherent to NAS access methods. Making pNFS available to petascale researchers required coordinated progress in several dimensions, a confluence of multiple processes that took nearly ten years. The protocol had to be specified and published by the IETF. Nascent implementations had to track changes in the draft specification, changes in the Linux kernel, and interoperate with one another. The process by which modifications are accepted into the Linux kernel by maintainers and developers (and ultimately by Linus Torvalds himself) itself required consensus and compromise. Finally, pNFS support needed to be provided by the major Linux distributors (Red Hat, SUSE, etc.) and storage vendors (Netapp, EMC, IBM, Microsoft, etc.) on both the engineering and product sides. The specification requirement was met in December 2008, when the IETF approved the NFSv4.1 specification, which incorporates pNFS and pNFS file layouts, as a Proposed Standard as well as the (separate) specifications of pNFS object and block layouts. Throughout the PDSI project, CITI was the key contributor to the Linux-based, open source implementation of NFSv4.1 and pNFS. Considerable effort was devoted to the process of refining the IETF specification, which underwent dozens of preliminary drafts, rebasing implementations to the latest Linux kernel, which itself changed quarterly, and to testing interoperability with other developers. At last, the Linux pNFS implementation was incorporated into the Linux mainline kernel, although this was achieved after the PDSI project ended.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

A scalable approach to solving dense linear algebra problems on hybrid CPU-GPU systems

Aiming to fully exploit the computing power of all CPUs and all graphics processing units (GPUs) on hybrid CPU-GPU systems to solve dense linear algebra problems, in this paper we design a class of heterogeneous tile algorithms to maximize the degree of parallelism, to minimize the communication volume, and to accommodate the heterogeneity between CPUs and GPUs. The new heterogeneous tile algorithms are executed upon our decentralized dynamic scheduling runtime system, which schedules a task graph dynamically and transfers data between compute nodes automatically. The runtime system uses a new distributed task assignment protocol to solve data dependencies between tasks without any coordination between processing units. By overlapping computation and communication through dynamic scheduling, we are able to attain scalable performance for the double-precision Cholesky factorization and QR factorization. Finally, our approach demonstrates a performance comparable to Intel MKL on shared-memory multicore systems and better performance than both vendor (e.g., Intel MKL) and open source libraries (e.g., StarPU) in the following three environments: heterogeneous clusters with GPUs, conventional clusters without GPUs, and shared-memory systems with multiple GPUs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Reducing Communication in Algebraic Multigrid Using Additive Variants

Algebraic multigrid (AMG) has proven to be an effective scalable solver on many high performance computers. However, its increasing communication complexity on coarser levels has shown to seriously impact its performance on computers with high communication cost. Moreover, additive AMG variants provide not only increased parallelism as well as decreased numbers of messages per cycle but also generally exhibit slower convergence. Here we present various new additive variants with convergence rates that are significantly improved compared to the classical additive algebraic multigrid method and investigate their potential for decreased communication, and improved communication-computation overlap, features that are essential for good performance on future exascale architectures.

97 MATHEMATICS AND COMPUTING↗