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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Electronic Structure Engine

INQ is an engine for electronic structure calculations. INQ is implemented as a C++ library of components designed to build a parallel, portable code for electronic structure calculations, including ground state and excited states. INQ is based on density functional theory (DFT). It can calculate ground state properties in DFT, and also excited states using time-dependent DFT (TDDFT) in real-time and linear-response. It implements different approximations for the exchange and correlation part: semi-local functionals like LDAs, GGAs and metaGGAs, and hybrid functionals that are implemented through the ACE approach for fast execution.

ANDRADE-VALENCIA, XAVIERI↗
DOE OSTI2013

Simulink/PARS Integration Support

The state of the art for signal processor hardware has far out-paced the development tools for placing applications on that hardware. In addition, signal processors are available in a variety of architectures, each uniquely capable of handling specific types of signal processing efficiently. With these processors becoming smaller and demanding less power, it has become possible to group multiple processors, a heterogeneous set of processors, into single systems. Different portions of the desired problem set can be assigned to different processor types as appropriate. As software development tools do not keep pace with these processors, especially when multiple processors of different types are used, a method is needed to enable software code portability among multiple processors and multiple types of processors along with their respective software environments. Sundance DSP, Inc. has developed a software toolkit called “PARS”, whose objective is to provide a framework that uses suites of tools provided by different vendors, along with modeling tools and a real time operating system, to build an application that spans different processor types. The software language used to express the behavior of the system is a very high level modeling language, “Simulink”, a MathWorks product. ORNL has used this toolkit to effectively implement several deliverables. This CRADA describes this collaboration between ORNL and Sundance DSP, Inc.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Evaluation of the OpenCL AES Kernel using the Intel FPGA SDK for OpenCL

The OpenCL standard is an open programming model for accelerating algorithms on heterogeneous computing system. OpenCL extends the C-based programming language for developing portable codes on different platforms such as CPU, Graphics processing units (GPUs), Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs). The Intel FPGA SDK for OpenCL is a suite of tools that allows developers to abstract away the complex FPGA-based development flow for a high-level software development flow. Users can focus on the design of hardware-accelerated kernel functions in OpenCL and then direct the tools to generate the low-level FPGA implementations. The approach makes the FPGA-based development more accessible to software users as the needs for hybrid computing using CPUs and FPGAs are increasing. It can also significantly reduce the hardware development time as users can evaluate different ideas with high-level language without deep FPGA domain knowledge. In this report, we evaluate the performance of the kernel using the Intel FPGA SDK for OpenCL and Nallatech 385A FPGA board. Compared to the M506 module, the board provides more hardware resources for a larger design exploration space. The kernel performance is measured with the compute kernel throughput, an upper bound to the FPGA throughput. The report presents the experimental results in details. The Appendix lists the kernel source code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Evaluation of CHO Benchmarks on the Arria 10 FPGA using Intel FPGA SDK for OpenCL

The OpenCL standard is an open programming model for accelerating algorithms on heterogeneous computing system. OpenCL extends the C-based programming language for developing portable codes on different platforms such as CPU, Graphics processing units (GPUs), Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs). The Intel FPGA SDK for OpenCL is a suite of tools that allows developers to abstract away the complex FPGA-based development flow for a high-level software development flow. Users can focus on the design of hardware-accelerated kernel functions in OpenCL and then direct the tools to generate the low-level FPGA implementations. The approach makes the FPGA-based development more accessible to software users as the needs for hybrid computing using CPUs and FPGAs are increasing. It can also significantly reduce the hardware development time as users can evaluate different ideas with high-level language without deep FPGA domain knowledge. Benchmarking of OpenCL-based framework is an effective way for analyzing the performance of system by studying the execution of the benchmark applications. CHO is a suite of benchmark applications that provides support for OpenCL [1]. The authors presented CHO as an OpenCL port of the CHStone benchmark. Using Altera OpenCL (AOCL) compiler to synthesize the benchmark applications, they listed the resource usage and performance of each kernel that can be successfully synthesized by the compiler. In this report, we evaluate the resource usage and performance of the CHO benchmark applications using the Intel FPGA SDK for OpenCL and Nallatech 385A FPGA board that features an Arria 10 FPGA device. The focus of the report is to have a better understanding of the resource usage and performance of the kernel implementations using Arria-10 FPGA devices compared to Stratix-5 FPGA devices. In addition, we also gain knowledge about the limitations of the current compiler when it fails to synthesize a benchmark application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures [Slides]

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming more difficult as the detectors increase in size to reach their physics goals. In liquid argon time projection chambers (TPCs) the charged particles from neutrino interactions produce ionization electrons which drift in an electric field towards a series of collection wires, and the signal on the wires is used to reconstruct the interaction. The MicroBooNE detector currently collecting data at Fermilab has 8000 wires, and planned future experiments like DUNE will have 100 times more, which means that the time required to reconstruct an event will scale accordingly. Modernization of liquid argon TPC reconstruction code, including vectorization, parallelization and code portability to GPUs, will help to mitigate these challenges. The liquid argon TPC hit finding algorithm within the L arSoft framework used across multiple experiments has been vectorized and parallelized. This increases the speed of the algorithm on the order of ten times within a stand alone version on Intel architectures. This new version has been incorporated back into LarSoft so that it can be generally used. These methods will also be applied to other low level reconstruction of the wire signals such as the deconvolution. The applications and performance of this modernized liquid argon TPC wire reconstruction will be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Contra: A New Language for Task- and Data-Parallellism [Slides]

A new language is beneficial to take advantage of emerging architectures, and existing and new software technologies. Contra is a new language to provide performance portable code and consists of two innovations, which are described. A current status and illustrations of the code are shown.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Simplified Interface to Complex Memory Hierarchies 1.x

Memory systems are expected to get evermore complicated in the coming years, and it isn't clear exactly what form that complexity will take. On the software side, a simple, flexible way of identifying and working with memory pools is needed. Additionally, most developers seek code portability and do not want to learn the intricacies of complex memory. Hence, we believe that a library for interacting with complex memory systems should expose two kinds of abstraction: First, a low-level, mechanism-based interface designed for the runtime or advanced user that wants complete control, with its focus on simplified representation but with all decisions left to the caller. Second, a high-level, policy-based interface designed for ease of use for the application developer, in which we aim for best-practice decisions based on application intent. We have developed such a library, called SICM: Simplified Interface to Complex Memory.

Lang, Michael↗
DOE OSTI2020

Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures.

Neutrinos are particles that interact rarely, so identifying them re-quires large detectors which produce lots of data. Processing this data with thecomputing power available is becoming more dicult as the detectors increasein size to reach their physics goals. In liquid argon time projection chambers(TPCs) the charged particles from neutrino interactions produce ionization elec-trons which drift in an electric field towards a series of collection wires, and thesignal on the wires is used to reconstruct the interaction. The MicroBooNEdetector currently collecting data at Fermilab has 8000 wires, and planned fu-ture experiments like DUNE will have 100 times more, which means that thetime required to reconstruct an event will scale accordingly. Modernization ofliquid argon TPC reconstruction code, including vectorization, parallelizationand code portability to GPUs, will help to mitigate these challenges. The liquidargon TPC hit finding algorithm within theLArSoftframework used acrossmultiple experiments has been vectorized and parallelized. This increases thespeed of the algorithm on the order of ten times within a standalone version onIntel architectures. This new version has been incorporated back intoLArSoftso that it can be generally used. These methods will also be applied to otherlow-level reconstruction algorithms of the wire signals such as the deconvolu-tion. The applications and performance of this modernized liquid argon TPCwire reconstruction will be presented.

Berkman, Sophie↗
DOE OSTI2020

CCAMP: An Integrated Translation and Optimization Framework for OpenACC and OpenMP

Heterogeneous computing and exploration into specialized accelerators are inevitable in current and future supercomputers. Although this diversity of devices is promising for performance, the array of architectures presents programming challenges. High-level programming strategies have emerged to face these challenges, such as the OpenMP offloading model and OpenACC. The varying levels of support for these standards, however, within vendor-specific and open-source tools, as well as the lack of performance portability across devices, have prevented the standards from achieving their goals. To address these shortcomings, we present CCAMP, an OpenMP and OpenACC interoperable framework. CCAMP provides two primary facilities: language translation between the two standards and device-specific directive optimization within each standard. We show that by using the CCAMP framework, programmers can easily transplant non-portable code into new ecosystems for new architectures. Additionally, by using CCAMP device-specific directive optimizations, users can achieve optimized performance across architectures using a single source code.

Lambert, Jacob↗
DOE OSTI2021

ExaWind: Predictive Wind Energy Simulations

This presentation describes the ExaWind project and the team's progress in creating a suite of performance-portable codes designed for predictive simulations of wind farms on next-generation exascale-class supercomputers. Such simulations will require the resolution of scales spanning many orders of magnitude, from blade boundary layers to wind farm flow structures. In the U.S., the first exascale systems will be GPU accelerated, and different GPU manufacturers have been chosen for the different systems. At the heart of the ExaWind software is a hybrid-solver approach based on the codes Nalu-Wind and AMR-Wind, which are computational fluid dynamics solvers for the incompressible Navier-Stokes equations. Nalu-Wind is an unstructured-grid code used to resolve wind turbine geometry and blade boundary layers, whereas AMR-Wind is a structured-grid background solver for atmospheric turbulent flow and turbine wake propagation. The models are coupled with overset meshes and global linear systems are approximated through a loose-coupling algorithm. Results will include validation-quality high-fidelity simulations and strong/weak scaling results from the Summit supercomputer.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2025

Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios

This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.

De Souza, Reubun [School of Electrical Engineering↗