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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Exploring the Use of Novel Spatial Accelerators in Scientific Applications

Driven by the need to find alternative accelerators which can viably replace GPUs in next-generation Supercomputing systems, this paper proposes a methodology to enable agile application/hardware co-design. The application-first methodology provides the ability to come up with design of accelerators while working with real-world workloads, available accelerators, and system software. The iterative design process targets a set of kernels in a workload for performance estimates that can prune the design space for later phases of detailed architectural evaluations. To this effect, in this paper, a novel data-parallel device model is introduced that simulates the latency of performance-sensitive operations in an accelerator including data transfers and kernel computation using multi-core CPUs. The use of off-the-shelf simulators, such as pre-RTL simulator Aladdin or multiple tools available for exploring the design of deep neural network accelerators (e.g., Timeloop) is demonstrated for evaluation of various accelerator designs using applications with realistic inputs. Examples of multiple device configurations that are instantiable in a system are explored to evaluate the performance benefit of deploying novel accelerators. The proposed device is integrated with a programming model and system software to potentially explore the impacts of high-level programming languages/compilers and low-level effects such as task scheduling on multiple accelerators. We analyze our methodology for a set of applications that represent high-performance computing (HPC) and graph analytics. The applications include a computational chemistry kernel realized using tensor contractions, triangle counting, GraphSAGE and Breadth-first Search. These applications include kernels such as dense matrix-dense matrix multiplication, sparse matrix-spare matrix multiplication, and sparse matrix-dense vector multiplication. Our results indicate potential performance benefits and insights for system design by including accelerators that realize these kernels along-side general purpose accelerators.

AI, codesign, Accelerated Computing, Modeling and ↗
DOE OSTI2017

XACC - eXtreme-scale Accelerator Programming Framework

Hybrid programming models for beyond-CMOS technologies will prove critical for integrating new computing technologies alongside our existing infrastructure. Unfortunately the software infrastructure required to enable this is lacking or not available. XACC is a programming framework for extreme-scale, post-exascale accelerator architectures that integrates alongside existing conventional applications. It is a pluggable framework for programming languages developed for next-gen computing hardware architectures like quantum and neuromorphic computing. It lets computational scientists efficiently off-load classically intractable work to attached accelerators through user-friendly Kernel definitions. XACC makes post-exascale hybrid programming approachable for domain computational scientists.

McCaskey, AlexanderJ.↗
DOE OSTI2016

XACC - eXtreme-scale Accelerator Programming Framework

Hybrid programming models for beyond-CMOS technologies will prove critical for integrating new computing technologies alongside our existing infrastructure. Unfortunately the software infrastructure required to enable this is lacking or not available. XACC is a programming framework for extreme-scale, post-exascale accelerator architectures that integrates alongside existing conventional applications. It is a pluggable framework for programming languages developed for next-gen computing hardware architectures like quantum and neuromorphic computing. It lets computational scientists efficiently off-load classically intractable work to attached accelerators through user-friendly Kernel definitions. XACC makes post-exascale hybrid programming approachable for domain computational scientists.

McCaskey, Alexander J.↗
DOE OSTI2022

ExaCA: A performance portable exascale cellular automata application for alloy solidification modeling

Modeling the as-solidified grain structures that form during alloy processing is a critical component in understanding process-property relationships, particularly for additive manufacturing (AM) where grain structure is very sensitive to processing conditions. While cellular automata (CA)-based models have proven able to predict aspects of microstructure for several alloys and AM process conditions, long run times and large resource sets required limit the utility and the problem size to which existing CA models can be applied. As part of the ExaAM project, an initiative within the Exascale Computing Project (ECP) to develop, test, and optimize an exascale-capable coupled and self-consistent model of AM parts, we developed ExaCA (https://github.com/LLNL/ExaCA) for the liquid–solid phase transformation in the wake of AM melt pools. The CA-based code is parallelized using MPI and the Kokkos programming model, the latter enabling simulation on both CPUs and GPUs within a single-source implementation. Here, we detail the steps taken to transform a baseline, MPI-based CA code into one that is performant on CPUs and GPUs. Performance testing of ExaCA on Summit (a pre-exascale machine at Oak Ridge National Laboratory) was used to quantify CPU–GPU speedup comparing with equal numbers of nodes. Testing showed comparable CPU performance to the MPI-only CA code and a 5-20x speedup when running AM-based test problems using GPUs. The improved performance of CA through GPU utilization and the performance portable nature of ExaCA will enable accurate part-scale modeling by harnessing the power of current and future generations of high performance computing resources. Future work will include improving the strong scaling of ExaCA on GPUs by reducing load imbalance associated with the locality of the problem, and continuing performance optimization across exascale hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2022

hPIC2: A hardware-accelerated, hybrid particle-in-cell code for dynamic plasma-material interactions

The exascale era of high performance computing promises to bring the field of computational plasma physics ever closer to the goal of accurate multiscale modeling. Such computers will rely on hardware acceleration to offload work to dedicated components, notably general-purpose graphics processing units (GPUs). However, devices from different manufacturers require software to be written with different parallel programming models, greatly increasing the code maintenance burden of applications designed to perform on more than one such device. hPIC2 is a hybrid plasma simulation code developed with the Kokkos performance portability framework to target the architectures that will drive exascale computing for the foreseeable future. As a hybrid simulation code, hPIC2 investigates the simultaneous use of various plasma models on the same domain, at the same time. hPIC2 also optionally couples to RustBCA, which accurately models ion-material interactions using the binary collision approximation (BCA) method. In conclusion, hPIC2 therefore achieves scalable performance on a variety of computing architectures when simulating complex and diverse plasmas, particularly near plasma-material interfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Copy Hiding Application Interface

CHAI is a light-weight framework which abstracts the automated movement of data (e.g. to/from Host/Device) via RAJA like performance portability programming model constructs. It can be viewed as a utility framework and an adjunct to FAJA (A Performance Portability Framework). Performance Portability is a technique that abstracts the complexities of modern Heterogeneous Architectures while allowing the original program to undergo incremental minimally invasive code changes in order to adapt to the newer architectures.

Jones, Holger↗
DOE OSTI2018

A SIMPLIFIED ENERGY MODELING APPROACH FOR BUILDINGS

This paper introduces simplified energy modeling (SEM) approaches using DOE2 and EnergyPlus and proposes a validation method for simplified energy modeling. Simplified energy modeling can allow more design teams and owners to leverage the power of energy modeling to inform their designs and lead to greater energy savings and a lower first cost because the technologies and design strategies can be thoroughly tested against models before the decisions are made. By starting a discussion on how to develop simplified energy modeling approaches and methods to validate results, we hope to increase innovation in energy modeling software while decreasing variation between modelers. Simplified modeling is not yet widely accepted because it is unclear if the simplifications reduce the accuracy of the results. By starting an industry dialogue on a testing method for these modeling approaches, we hope to aid adoption of more streamlined tools when they can be shown to provide similar accuracy to more detailed tools. This paper proposes a method to test simplified energy modeling programs against the DOE prototype buildings. It builds on the testing procedure established for simulation engines by ASHRAE Standard 140. By comparing whole building and end use results from the accepted prototype buildings to any simplified modeling approach across multiple building types and climate zones we can determine if the simplifications create unwarranted error. We will present the SEM approach and results for a case study validation for a sample building types in 16 US climate zones from two such simplified energy modeling tools.

Energy Modeling, 90.1 Appendix G, Asset Score Tool↗
DOE OSTI2023

Joint Inversion of Surface Electrical Resistivity Tomography and Seismic Refraction Data between the 200 Areas

Geologic stratigraphy on the Hanford Site influences groundwater and contaminant migration through the aquifer system and the vadose zone. The current geologic framework model (GFM) relies heavily on a sparse distribution of borehole data in some locations to map geologic contacts and hydrologic properties in the subsurface. Non-invasive geophysical methods such as electrical resistivity tomography (ERT), transient electromagnetic surveying, and seismic imaging are being used at Hanford to map subsurface structure in areas with limited well observations. This is to develop and mature the capability of geophysical methods to aid in GFM refinement, to identify regions of subsurface complexity, and for optimal well siting. A joint inversion of co-located seismic refraction and ERT data was carried out for data collected on a ~2.3-km profile between the 200 Areas on the Hanford Site. While ERT and seismic refraction images have sensitivity to overlapping physical properties (porosity, moisture content, lithology), the resolution and physics used to acquire each of these datasets are different and therefore information can be different or mutually complementary. Performing a joint inversion provides a reasonable option for a coherent, coupled interpretation for mutually complementary datasets. Between the 200 Areas, there are few boreholes to interpret the geologic framework model, and these data sets were obtained to provide a first line of evidence toward identifying stratigraphic structure. The seismic refraction and ERT data were independently inverted during fiscal year 2022 and broadly showed a two-layer structure with a trough-like feature that is ~1 km wide and upwards of 150 m deep. The depth of the trough feature was greater in the ERT image compared to the seismic image, which indicated a maximum depth of approximately 110 m. The objective of the joint inversion described in this report was to invert the seismic refraction and ERT data together while constraining the ERT image to be structurally similar to the seismic refraction image. The approach was applied using the geophysical inverse modeling program E4D, which has the capability to invert first-arrival times from seismic refraction data and ERT resistances using a “cross-gradient” constraint. The application of cross-gradient constraints with different weights produces ERT models that show a high degree of similarity within the upper 100 m (above ~120 m elevation). None of the ERT models show an improved structural similarity to the seismic result; therefore, it is recommended that further attempts to jointly interpret these models focus on petrophysics and image resolution. Petrophysical measurements of core samples would improve knowledge of what drives the ERT response in this region and, along with downhole geophysical measurements, could be used to “ground truth” the surface-based geophysical results. Image resolution studies would provide insight into which regions of the inverted images are reliable and which regions are poorly constrained.

58 GEOSCIENCES↗
DOE OSTI2018

GASNet-EX: A High-Performance, Portable Communication Library for Exascale

Partitioned Global Address Space (PGAS) models, typified by such languages as Unified Parallel C (UPC) and Co-Array Fortran, expose one-sided communication as a key building block for High Performance Computing (HPC) applications. Architectural trends in supercomputing make such programming models increasingly attractive, and newer, more sophisticated models such as UPC++, Legion and Chapel that rely upon similar communication paradigms are gaining popularity. GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in future exascale machines. The library is an evolution of the popular GASNet communication system, building upon over 15 years of lessons learned. We describe and evaluate several features and enhancements that have been introduced to address the needs of modern client systems. Microbenchmark results demonstrate the RMA performance of GASNet-EX is competitive with several MPI-3 implementations on current HPC systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Robust Network Interdiction Considering Multiple Defense Resources

We extend the network interdiction problem in order to consider multiple options for defending arcs in a network. We apply our model to select an optimal set of countermeasures for nuclear material smuggling. In order account for the joint impact of multiple countermeasures, we consider multiple components of defense: situational awareness, detection, and interdiction. We develop a bi-level programming model, where the adversary's goal is to maximize damage to the system while the defender seeks to minimize damage by selecting countermeasure options under a restricted bud- get. Furthermore, since effectiveness of countermeasures is often difficult to assess with high accuracy, we use robust optimization to allow for parametric uncertainty. We test our method on a synthetic example of the U.S. supply chain network that includes rail, air, and maritime transportation between both domestic and international locations. For this test example, we find that increasing the budget for countermeasures has the greatest impact within the first 1-2% of the cost to implement all countermeasures and that the uncertainty tolerance has a greater impact on expected damage than the countermeasure budget does. Thus, when strategically selecting countermeasures it is important to precisely understand the effectiveness of each option and the adversary's probability of subverting them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Optimization models for integrated biorefinery operations

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US’s cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system’s reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. Furthermore, the sample average approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory’s biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution, increases utilization of the reactor and reduces operational costs.

09 BIOMASS FUELS↗
DOE OSTI2021

Delivery drone route planning over a battery swapping network

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.

drone battery swapping↗
DOE OSTI2019

A UPC++ Actor Library and Its Evaluation On a Shallow Water Proxy Application

Programmability is one of the key challenges of Exascale Computing. Using the actor model for distributed computations may be one solution. The actor model separates computation from communication while still enabling their overlap. Each actor possesses specified communication endpoints to publish and receive information. Computations are undertaken based on the data available on these channels. We present a library that implements this programming model using UPC++, a PGAS library, and evaluate three different parallelization strategies, one based on rank-sequential execution, one based on multiple threads in a rank, and one based on OpenMP tasks. In an evaluation of our library using shallow water proxy applications, our solution compares favorably against an earlier implementation based on X10, and a BSP-based approach.

Pöppl, Alexander↗
DOE OSTI2019

Photocurrent from Single Collision 14 MeV Neutrons in GaN and GaAs

Accurate predictions of device performance in 14 MeV neutron environments relies upon understanding the recoil cascades that may be produced. Recoils from 14 MeV neutrons impinging on both gallium nitride (GaN) and gallium arsenide (GaAs) devices were modeled and compared to the recoil spectra of devices exposed to 14 MeV neutrons. Recoil spectra were generated using nuclear reaction modeling programs and converted into an ionizing energy loss (IEL) spectrum. We measured the recoil IEL spectra by capturing the photocurrent pulses produced by single neutron interactions with the device. Good agreement, with a factor of two, was found between the model and experiment under strongly-depleted conditions. However, this range of agreement between model and experiment decreased significantly when the bias was removed, indicating partial energy deposition due to cascades that escape the active volume of the device not captured by the model. Consistent event rates across multiple detectors confirm the reliability of our neutron recoil detection method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Approximating Joint Chance Constraints in Two-Stage Stochastic Programs

We present various approximations to joint chance constraints arising in two-stage stochastic programming models. Our approximations are derived from three classical inequalities: Markov's inequality, Chebysev's inequality, and Chernoff's bound. We provide preliminary computational results illustrating the quality of our approximation using a two-stage joint-chance-constrained stochastic program from the literature. We also briefly introduce other alternatives for constructing approximations for joint-chance-constrained two-stage programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Proxy Applications for Converged Workloads: DMC LDRD Initiative

Modern scientific applications are complicated and require coordination of several components. Proxy application driven software-hardware co-design plays a vital role in driving innovation among the developments of applications, software infrastructure and hardware architecture. Proxy applications are self-contained and simplified codes that are intended to model the performance-critical computations within applications. Applications executing on modern High Performance Computing (HPC) systems are susceptible to network congestion, insufficient memory bandwidth within and across compute nodes, and inadvertent loss of performance due to bugs and unoptimized programming models. Modern numerical simulations and machine learning models play a critical role in studying physical phenomenon under myriad uncertainties. Such applications often exhibit irregular computation and memory accesses at specific regions of the application code, which can contribute to various performance bottlenecks at scale. To mitigate such issues and prepare the next generation hardware for a variety of computation and data movement contingencies, a well-known practice is to consider "proxy" applications as representative motifs for various classes of scientific applications. While there is disagreement in the HPC community on the mechanisms of construction of the proxy applications, there is a strong consensus on their positive impact in co-design. Proxy Applications for Converged Workloads (PACER) is about facilitating software-hardware co-design through proxy applications with the goal of improving the performance of converged science workflows on heterogeneous systems.

97 MATHEMATICS AND COMPUTING↗