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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures

Patient-specific hemodynamic simulations have the possibility to greatly improve both the diagnosis and treatment of a variety of vascular diseases. Portability will enable wider adoption of computational fluid dynamics (CFD) applications in the biomedical research community and targeting to platforms ideally suited to different vascular regions. Here, we introduce a case study in performance portability that assesses (1) the ease of porting an MPI application optimized for one specific architecture to new platforms using variants of hybrid MPI+X programming models; (2) performance portability seen when simulating blood flow in three different vascular regions on diverse heterogeneous architectures; (3) model-based performance prediction for future architectures; and (4) performance scaling of the hybrid MPI+X programming on parallel heterogeneous systems. We report on the lessons learned in porting HARVEY, a massively parallel CFD application, from traditional multicore CPUs to diverse heterogeneous architectures ranging from NVIDIA/AMD GPUs to Intel MICs and Altera FPGAs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Impact of detector simulation in particle physics collider experiments

Through the last three decades, precise simulation of the interactions of particles with matter and modeling of detector geometries has proven to be of critical importance to the success of the international high-energy physics experimental programs. For example, the detailed detector modeling and accurate physics of the Geant4-based simulation software of the CMS and ATLAS particle physics experiments at the European Center of Nuclear Research (CERN) Large Hadron Collider (LHC) was a determinant factor for these collaborations to deliver physics results of outstanding quality faster than any hadron collider experiment ever before. This review article highlights the impact of detector simulation on particle physics collider experiments. It presents numerous examples of the use of simulation, from detector design and optimization, through software and computing development and testing, to cases where the use of simulation samples made a difference in the accuracy of the physics results and publication turnaround, from data-taking to submission. It also presents the economic impact and cost of simulation in the CMS experiment. Future experiments will collect orders of magnitude more data, taxing heavily the performance of simulation and reconstruction software for increasingly complex detectors. Consequently, it becomes urgent to find solutions to speed up simulation software in order to cope with the increased demand in a time of flat budgets. The study ends with a short discussion on the potential solutions that are being explored, by leveraging core count growth in multicore machines, using new generation coprocessors, and re-engineering of HEP code for concurrency and parallel computing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data

Here, a newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides, important for metal extraction chemistry, are parametrized using ParFit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

MBX V1.2: Accelerating Data-Driven Many-Body Molecular Dynamics Simulations

The MBX software provides an advanced platform for molecular dynamics simulations, leveraging state-of-the-art MB-pol and MB-nrg data-driven many-body potential energy functions. Developed over the past decade, these potential energy functions integrate physics-based and machine-learned many-body terms trained on electronic structure data calculated at the "gold standard" coupled-cluster level of theory. Recent advancements in MBX have focused on optimizing its performance, resulting in the release of MBX v1.2. While the inherently many-body nature of MB-pol and MB-nrg ensures high accuracy, it poses computational challenges. MBX v1.2 addresses these challenges with significant performance improvements, including enhanced parallelism that fully harnesses the power of modern multicore CPUs. In conclusion, these advancements enable simulations on nanosecond time scales for condensed-phase systems, significantly expanding the scope of high-accuracy, predictive simulations of complex molecular systems powered by data-driven many-body potential energy functions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Advanced Potential Energy Surfaces for Molecular Simulation

Advanced potential energy surfaces are defined as theoretical models that explicitly include many-body effects that transcend the standard fixed-charge, pairwise-additive paradigm typically used in molecular simulation. However, several factors relating to their software implementation have precluded their widespread use in condensed-phase simulations: the computational cost of the theoretical models, a paucity of approximate models and algorithmic improvements that can ameliorate their cost, underdeveloped interfaces and limited dissemination in computational code bases that are widely used in the computational chemistry community, and software implementations that have not kept pace with modern high-performance computing (HPC) architectures, such as multicore CPUs and modern graphics processing units (GPUs). Here in this Feature Article we review recent progress made in these areas, including well-defined polarization approximations and new multipole electrostatic formulations, novel methods for solving the mutual polarization equations and increasing the MD time step, combining linear-scaling electronic structure methods with new QM/MM methods that account for mutual polarization between the two regions, and the greatly improved software deployment of these models and methods onto GPU and CPU hardware platforms. Finally, we have now approached an era where multipole-based polarizable force fields can be routinely used to obtain computational results comparable to state-of-the-art density functional theory while reaching sampling statistics that are acceptable when compared to that obtained from simpler fixed partial charge force fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Observation of Joule–Thomson photon-gas expansion

In recent years, a self-consistent optical thermodynamic framework has emerged that offers a systematic methodology to understand, harness and exploit the complex collective dynamics of multimode nonlinear systems. These developments now allow consideration of a series of longstanding problems in optics, including the prospect of funnelling the entire power flowing in a multimode system into its ground state, for which no methodology currently exists. Here, we demonstrate an all-optical Joule-Thomson expansion process mediated by photon-photon interactions whereby the temperature of the optical gas drops abruptly to zero. Our experiments in various configurations of coupled multicore nonlinear waveguide arrangements illustrate how light undergoing expansion-induced cooling can be channelled from arbitrary input states into the fundamental mode with near-unity efficiency. We show that the stability of the post-expansion state is ensured through an irreversible process of energy conversion. The all-optical thermodynamic phenomena explored in this study may enable innovative techniques where various uncorrelated but identical sources are merged into a unified spatially coherent state, offering a route for direct beam combining.

Kirsch, Marco S. (ORCID:0000000342798228)↗
DOE OSTI2024

Kubernetes for the Deep Underground Neutrino Experiment Data Acquisition

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino experiment based in the USA which is expected to start taking data in 2029. DUNE aims to precisely measure neutrino oscillation parameters by detecting neutrinos from the LBNF beamline (Fermilab) at the Far Detector, 1,300 kilometres away, in South Dakota at the Sanford Underground Research Facility. The Far Detector will consist of four cryogenic Liquid Argon Time Projection Chamber detectors of 17 kT, each producing more than 1 TB/sec of data. The main requirements for the data acquisition system are the ability to run continuously for extended periods of time, with a 99% up-time requirement, and the functionality to record both beam neutrinos and low energy neutrinos from the explosion of a neighbouring supernova, should one occur during the lifetime of the experiment. The key challenges are the high data rates that the detectors generate and the deep underground environment, which places constraints on power and space. To overcome these challenges, DUNE plans to use a highly optimised C++ software suite and a server farm of about 110 nodes continuously running about two hundred multicore processes located close to the detector, 1.5 kilometres underground. Thirty nodes will be at the surface and will run around two hundred processes simultaneously. DUNE is studying the use of the Kubernetes framework to manage containerised workloads and take advantage of its resource definitions and high up-time services to run the DAQ system. Progress in deploying these systems at the CERN neutrino platform on the prototype DUNE experiments is reported.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Porting ATLAS Fast Calorimeter Simulation to GPUs with Performance Portable Programming Models

FastCaloSim is a parameterized simulation of the particle energy response and of the energy distribution in the ATLAS calorimeter. It is a relatively small and self-contained package with massive inherent parallelism and captures the essence of GPU offloading via important operations like data transfer, memory initialization, floating point operations, and reduction. It was identified by the High Energy Physics Center for Computational Excellence project as a good testbed for evaluating the performance and ease of portability of programming models. In this paper, we will discuss the results of our evaluation of the porting process to Kokkos, SYCL, Alpaka, OpenMP and std::par (nvc++), and compare performance on NVIDIA, AMD and Intel GPUs, as well as multicore CPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

New generation of optical fibres

The growing need for information in contemporary society is the motivating force behind the development of fibre optics in general and optical fibre communications in particular. Intensive research effort has been concentrated on designing new types of optical fibres and extending their application field. This paper reviews results of research on new types of optical fibres: bismuthdoped active fibres, multicore fibres and hollow-core fibres, which can be used as key components of systems that ensure further increase in optical information transfer rate. (invited paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

QMCPACK : an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules and solids

QMCPACK is an open source quantum Monte Carlo package for ab-initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wave functions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit (CPU) and graphical processing unit (GPU) systems. We detail the program’s capabilities, outline its structure, and give examples of its use in current research calculations. The package is available at http://www.qmcpack.org.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

GRACC: New generation of the OSG accounting

Throughout the last decade the Open Science Grid (OSG) has been fielding requests from user communities, resource owners, and funding agencies to provide information about utilization of OSG resources. Requested data include traditional accounting - core-hours utilized - as well as users certicate Distinguished Name, their affiliations, and eld of science. The OSG accounting service, Gratia, developed in 2006, is able to provide this information and much more. However, with the rapid expansion and transformation of the OSG resources and access to them, we are faced with several challenges in adapting and maintaining the current accounting service. The newest changes include, but are not limited to, acceptance of users from numerous university campuses, whose jobs are flocking to OSG resources, expansion into new types of resources (public and private clouds, allocation-based HPC resources, and GPU farms), migration to pilot-based systems, and migration to multicore environments. In order to have a scalable, sustainable and expandable accounting service for the next few years, we are embarking on the development of the next-generation OSG accounting service, GRACC, that will be based on open-source technology and will be compatible with the existing system. It will consist of swappable, independent components, such as Logstash, Elasticsearch, Grafana, and RabbitMQ, that communicate through a data exchange. GRACC will continue to interface EGI and XSEDE accounting services and provide information in accordance with existing agreements. Lastly, we will present the current architecture and working prototype.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm

Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency and subjective human factors. We developed a novel data-driven geological feature characterization approach based on pre-stack seismic measurements. Our characterization method employs an efficient and accurate machine learning method to extract useful subsurface geologic features automatically. Specifically, we use kernel ridge regression to account for the nonlinear relationship between seismic data and geological features. We moreover employ kernel tricks to avoid the explicit nonlinear mapping and infinite dimension of feature space. Yet, the conventional kernel ridge regression can be computationally prohibitive because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nyström method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate characterization. We provide thorough computational cost analysis to show the efficiency of our new geological feature characterization methods. We validate the performance of our method in characterizing geologic fault zones because faults play an important role in various subsurface applications. Our numerical examples demonstrate that our new characterization method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we demonstrate that our approach yields a speedup ratio on the order of ~102 to ~103 in a multicore computational environment.

58 GEOSCIENCES↗
DOE OSTI2018

GALARIO: a GPU accelerated library for analysing radio interferometer observations

We present GALARIO, a computational library that exploits the power of modern graphical processing units (GPUs) to accelerate the analysis of observations from radio interferometers like Atacama Large Millimeter and sub-millimeter Array or the Karl G. Jansky Very Large Array. GALARIO speeds up the computation of synthetic visibilities from a generic 2D model image or a radial brightness profile (for axisymmetric sources). On a GPU, GALARIO is 150 faster than standard PYTHON and 10 times faster than serial C++ code on a CPU. Highly modular, easy to use, and to adopt in existing code, GALARIO comes as two compiled libraries, one for Nvidia GPUs and one for multicore CPUs, where both have the same functions with identical interfaces. GALARIO comes with PYTHON bindings but can also be directly used in C or C++. The versatility and the speed of GALARIO open new analysis pathways that otherwise would be prohibitively time consuming, e.g. fitting high-resolution observations of large number of objects, or entire spectral cubes of molecular gas emission. It is a general tool that can be applied to any field that uses radio interferometer observations. The source code is available online at http://github.com/mtazzari/galario under the open source GNU Lesser General Public License v3.

Astronomy & Astrophysics↗
DOE OSTI2019

GUNDAM: a toolkit for fast spatial correlation functions in galaxy surveys

ABSTRACT We describe the capabilities of a new software package to calculate two-point correlation functions (2PCFs) of large galaxy samples. The code can efficiently estimate 3D/projected/angular 2PCFs with a variety of statistical estimators and bootstrap errors, and is intended to provide a complete framework (including calculation, storage, manipulation, and plotting) to perform this type of spatial analysis with large redshift surveys. Gundam implements a very fast skip list/linked list algorithm that efficiently counts galaxy pairs and avoids the computation of unnecessary distances. It is several orders of magnitude faster than a naive pair counter, and matches or even surpass other advanced algorithms. The implementation is also embarrassingly parallel, making full use of multicore processors or large computational clusters when available. The software is designed to be flexible, user friendly and easily extensible, integrating optimized, well-tested packages already available in the astronomy community. Out of the box, it already provides advanced features such as custom weighting schemes, fibre collision corrections and 2D correlations. Gundam will ultimately provide an efficient toolkit to analyse the large-scale structure ‘buried’ in upcoming extremely large data sets generated by future surveys.

Donoso, E.↗
DOE OSTI2018

Scalable Proximity-Based Methods for Large-Scale Analysis of Atom Probe Data

Powered by recent advances in data acquisition technologies, today's state-of-the-art atom probe microscopes yield data sets with sizes ranging from a few million atoms to billions of atoms. Analysis of these atomic data sets within rea-sonable turnaround times is a pressing data analysis challenge for material scientists currently equipped with software systems that do not scale to these massive data sets. Here, we present the shared memory component of a larger ongoing effort to develop a multi-feature data analysis framework capable of analyzing atom probe data of all sizes and scales from desktop multicore machines to large-scale high-performance computing platforms with hybrid (shared and distributed memory) architectures. Our focus here is on a broad class of popular atom probe data analysis methods that rely on core time-consuming k-NN queries. We present a scalable, heuristic algorithm for k-NN queries using three-dimensional range trees. To demonstrate its efficacy, the k-NN algorithm is integrated with two use cases of atom probe data analysis methods and the resulting analysis times are shown to speedup by over 20X on a 32-core Cray XC40 node using workloads up to 8 million atoms, which is already beyond the at-scale capabilities of existing atom probe software. Using this k-NN algorithm, we also introduce a novel parameter estimation method for a class of cluster finding methods, called friends-of-friends (FoF) methods, to completely bypass their expensive pre-processing steps. In each case, we validate the results on a variety of control data sets.

Lu, Hao↗
DOE OSTI2025

Characterization and Optimization of the Fitting of Quantum Correlation Functions

This case study presents a characterization and optimization of an application code for extracting parton distribution functions from high energy electron-proton scattering data. Profiling this application code reveals that the phase-space density computation accounts for 93% of the overall execution time for a single iteration on a single core. When executing multiple iterations in parallel on a multicore system, the application spends 78% of its overall execution time idling due to load imbalance. We address these issues by first transforming the application code from Python to C++ and then tackling the application load imbalance via a hybrid scheduling strategy that combines dynamic and static scheduling. These techniques result in a 62% reduction in CPU idle time and a 2.46x speedup in overall execution time per node. In addition, the typically enabled power-management mechanisms in supercomputers (e.g., AMD Turbo Core, Intel Turbo Boost, and RAPL) can significantly impact intra-node scalability when more than 50% of the CPU cores are used. This finding underscores the importance of understanding system interactions with power management, as they can adversely impact application performance, and highlights the necessity of intra-node scaling tests to identify performance degradation that inter-node scaling tests might otherwise overlook.

Chuang, Pi-Yueh [Virginia Tech,Dept. of Computer S↗
DOE OSTI2016

Parallel k-means++ for Multiple Shared-Memory Architectures

In recent years k-means++ has become a popular initialization technique for improved k-means clustering. To date, most of the work done to improve its performance has involved parallelizing algorithms that are only approximations of k-means++. In this paper we present a parallelization of the exact k-means++ algorithm, with a proof of its correctness. We develop implementations for three distinct shared-memory architectures: multicore CPU, high performance GPU, and the massively multithreaded Cray XMT platform. We demonstrate the scalability of the algorithm on each platform. In addition we present a visual approach for showing which platform performed k-means++ the fastest for varying data sizes.

data clustering↗