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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

OpenMP application experiences: Porting to accelerated nodes

As recent enhancements to the OpenMP specification become available in its implementations, there is a need to share the results of experimentation in order to better understand the OpenMP implementation’s behavior in practice, to identify pitfalls, and to learn how the implementations can be effectively deployed in scientific codes. We report on experiences gained and practices adopted when using OpenMP to port a variety of ECP applications, mini-apps and libraries based on different computational motifs to accelerator-based leadership-class high-performance supercomputer systems at the United States Department of Energy. Additionally, we identify important challenges and open problems related to the deployment of OpenMP. Through our report of experiences, we find that OpenMP implementations are successful on current supercomputing platforms and that OpenMP is a promising programming model to use for applications to be run on emerging and future platforms with accelerated nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Autonomous continuous flow reactor synthesis for scalable atom-precision

With new instrumentation design, robotics, and in-operando hyphenated analytical tool automation, the intelligent discovery of synthesis pathways is becoming feasible. It can potentially bridge the gap for the scale-up of new materials. In this article, we review current progress and describe a new system that uses an autonomous continuous flow chemistry framework to translate high-quality lead molecules and materials to quantities that can meet scalability demands. At the core is a continuous flow synthesis platform that can design its viable synthesis pathway to a particular molecule or material and then autonomously carry it out. This is realized by integrating: (1) A workflow/architecture for multimode chemical/materials characterization in-line. The in-line characterization modes are NMR, ESR, IR, Raman, UV-Vis, GC-MS, and HPLC, along with ex-situ modes for X-Ray and neutron scattering; (2) Integration for feedback/analysis/data storage of the control variables; (3) A core software stack that includes deep learning and reinforcement learning alongside quantum chemistry and molecular dynamics; (4) On-demand compute architectures that parse calculations to compute resources needed which include light-weight edge, mid-level edge (NVIDA DGX-2), and high-performance computing. We demonstrate preliminary results on how this autonomous reactor system can enhance our ability to deliver deuterated materials, copolymers, and site-substituted molecules.

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

Impact of Data Placement on Resilience in Large-Scale Object Storage Systems

Distributed object storage architectures have become the de facto standard for high-performance storage in big data, cloud, and HPC computing. Object storage deployments using commodity hardware to reduce costs often employ object replication as a method to achieve data resilience. Repairing object replicas after failure is a daunting task for systems with thousands of servers and billions of objects, however, and it is increasingly difficult to evaluate such scenarios at scale on realworld systems. Resilience and availability are both compromised if objects are not repaired in a timely manner. In this work we leverage a high-fidelity discrete-event simulation model to investigate replica reconstruction on large-scale object storage systems with thousands of servers, billions of objects, and petabytes of data. We evaluate the behavior of CRUSH, a well-known object placement algorithm, and identify configuration scenarios in which aggregate rebuild performance is constrained by object placement policies. After determining the root cause of this bottleneck, we then propose enhancements to CRUSH and the usage policies atop it to enable scalable replica reconstruction. We use these methods to demonstrate a simulated aggregate rebuild rate of 410 GiB/s (within 5% of projected ideal linear scaling) on a 1,024-node commodity storage system. We also uncover an unexpected phenomenon in rebuild performance based on the characteristics of the data stored on the system.

Carns, Philip↗
DOE OSTI2024

Contactless Position Measurement System for Remote Alignment of Highly Reflective Objects

The Contactless Position Measurement System (CPMS) permits automatic alignment of highly-reflecting components that cannot or should not be touched by human hands, such as those used in SRF cavities. CPMS also has application during maintenance of fusion reactors, when the components in the reactor are radioactive. The length of time for which humans can handle the components in order to carry out maintenance or repairs is limited. We would like to measure the relative positions of such objects so we can bring them into alignment with motorized stages, thus eliminating the need for human contact. The CPMS is a computer vision system that measures the location of highly reflective components to within a fraction of a millimeter by finding the edges of silhouette images. This is done by placing a uniform, infrared backlight behind the components, and viewing each component with two low-aberration, infrared cameras. Each of these stereoscopic cameras are located within a coordinate system we set up with reference platforms, light sources, and survey cameras distributed around the perimeter of the string assembly room, or fusion reactor. We obtain stereoscopic silhouette images of each component, and we use these to determine the position of the component within our string assembly coordinate system. The CPMS combines the silhouette images with knowledge of the dimensions of the objects to obtain the relative positions of components in its field of view. These position measurements then allow us to mechanically maneuver the components into contact. Once we know where they are, we can move the components to where they are supposed to be with motorized stages or other mechanical means, check they are in the right place, and bolt them together. In Phase I, we built a prototype infrared backlights and cameras, tested it, wrote an analysis program to fit the images. At a range of 50 cm, we are able to measure the relative positions of two stainless steel flanges with an accuracy of 150 μm rms. The next steps are to test the analysis program with motorized stages, build a full-size prototype, and implement it in an cleanroom with SRF cavity assembly. This can be done in a cost effective way given the materials with which the system is designed. As Open Source Instruments achieved more in Phase I than anticipated, despite not receiving a Phase II SBIR grant, we hope to move forward with product development and sales with a testing partner.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Phase I Final Technical Report on Energy-Efficient Reconfigurable Universal Accelerator Interconnect

It is well known that application specific computing systems, optimally designed and configured for a given workload, offer much higher energy-efficiency and throughput than general purpose systems. In modern computing systems, heterogeneous computing systems have emerged that exploit the energy and performance benefits of combining various different domain-specific processor architectures. Application domains such as high-performance computing and machine learning now process terabyte-sized data sets, requiring enormous processing and memory resources. These applications have very high-power consumption due to bottlenecks in the electrical interconnection between processing units. This project aims to reduce both communication energy and latency by integrating universally available accelerators with silicon photonics. It also aims to increase system throughput by exploiting emerging technologies in silicon photonic reconfigurable interconnects; this will allow the system to balance itself in real time to accommodate changes in workloads and data flows.

2.5D/3D integration↗
DOE OSTI2023

BMX: Biological modelling and interface exchange

Abstract High performance computing has a great potential to provide a range of significant benefits for investigating biological systems. These systems often present large modelling problems with many coupled subsystems, such as when studying colonies of bacteria cells. The aim to understand cell colonies has generated substantial interest as they can have strong economic and societal impacts through their roles in in industrial bioreactors and complex community structures, called biofilms, found in clinical settings. Investigating these communities through realistic models can rapidly exceed the capabilities of current serial software. Here, we introduce BMX, a software system developed for the high performance modelling of large cell communities by utilising GPU acceleration. BMX builds upon the AMRex adaptive mesh refinement package to efficiently model cell colony formation under realistic laboratory conditions. Using simple test scenarios with varying nutrient availability, we show that BMX is capable of correctly reproducing observed behavior of bacterial colonies on realistic time scales demonstrating a potential application of high performance computing to colony modelling. The open source software is available from the zenodo repository https://doi.org/10.5281/zenodo.8084270 under the BSD-2-Clause licence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Energy Systems Integration Facility Stewardship Summary: Fiscal Year 2023

A summary of NREL's good stewardship of the nationally unique Energy Systems Integration Facility (ESIF) highlighting performance metrics, infrastructure and capability upgrades, and examples of R&D impact. In fiscal year 2023, ESIF installed its third-generation high-performance computer, expanded capabailities for validating building energy controls, and demonstrated a leap in scale through virtual networking with another national laboratory. ESIF researchers made breakthroughs in cybersecurity for energy systems, efficient high-powered electric vehicle charging, leveraging reinforcement learning for grid resilience, and more.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2017

Toward General Software Level Silent Data Corruption Detection for Parallel Applications

Silent data corruption (SDC) poses a great challenge for high-performance computing (HPC) applications as we move to extreme-scale systems. Mechanisms have been proposed that are able to detect SDC in HPC applications by using the peculiarities of the data (more specifically, its “smoothness” in time and space) to make predictions. However, these data-analytic solutions are still far from fully protecting applications to a level comparable with more expensive solutions such as full replication. In this work, we propose partial replication to overcome this limitation. More specifically, we have observed that not all processes of an MPI application experience the same level of data variability at exactly the same time. Thus, we can smartly choose and replicate only those processes for which the lightweight data-analytic detectors would perform poorly. In addition, we propose a new evaluation method based on the probability that a corruption will pass unnoticed by a particular detector (instead of just reporting overall single-bit precision and recall). Here in our experiments, we use four applications dealing with different explosions. Finally, our results indicate that our new approach can protect the MPI applications analyzed with 7–70% less overhead (depending on the application) than that of full duplication with similar detection recall.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Tuyere: Enabling Scalable Memory Workloads for System Exploration

Memory technologies are under active development. Meanwhile, workloads on contemporary computing systems are increasing rapidly in size and diversity. Such dynamics in hardware and software further widen the gap between memory system design and performance evaluation. In this work, we propose a data-centric abstraction of high-performance computing applications for fast exploration of new memory technologies. We also provide a framework that uses a formal modeling language to describe the abstraction, automatically translates abstractions into memory traffic, and directly interfaces with cycle-accurate simulators. We evaluated the framework using 20 workloads and validated the memory traffic profile, the simulation results, and the relative memory changes of four memory technologies. Our results show that the data-centric abstraction can accurately capture application behavior adaptable to different input problems and can expedite system exploration.

Peng, Ivy Bo↗
DOE OSTI2025

SWARM: Reimagining scientific workflow management systems in a distributed world

Modern scientific workflows process massive amounts of data from diverse instruments and sensors, leveraging geographically distributed, heterogeneous compute and storage resources—from leadership-class systems to edge devices—connected by high-performance networks. The diversity of resources introduces challenges in harnessing their full potential, with resilience issues arising across applications, system software, networks, storage, and hardware. Today, workflow management systems (WMS) coordinate the execution of computation and data management tasks across target resources. However, WMS’s centralized nature makes them vulnerable to faults and scalability issues that may result in failures of entire computational campaigns. In conclusion, this paper introduces a novel agentic framework for workflow management, fully distributing and decentralizing the WMS functions and modeling them as swarm intelligence agents infused with advanced artificial intelligence solutions and traditional distributed computing algorithms that can make coordinated decisions in the presence of failures of the underlying cyberinfrastructure.

Swarm intelligence↗
DOE OSTI2022

DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing

Cluster schedulers are crucial in high-performance computing (HPC). They determine when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. An efficient training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by the system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. We implement DRAS into a HPC scheduling platform called CQGym. CQGym provides a common platform allowing users to flexibly evaluate DRAS and other scheduling methods such as heuristic and optimization methods. Furthermore, the experiments using CQGym with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 50%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

High-Fidelity Calibration and Characterization of Hyperspectral Computed Tomography System

This document archives the results developed by the Lab Directed Research and Development (LDRD) project sponsored by Sandia National Laboratories (SNL). In this work, a numerical study was performed to show the feasibility of approximating the non-linear operator of SNL's unique high-energy hyperspectral computed tomography system as a sequence of linear operators. The four main results gained from this work include the development of a simulation test-bed using a particle-transport Monte Carlo approach; the demonstration to assemble a linear operator of almost-arbitrary resolution for a given narrow energy window, developing a compression approach to dramatically reduce the size of the linear operator via a spline approach, and the demonstration of using the linear operator to perform processing of x-ray data; in this case, the development of an iterative reconstruction method. This numerical study has indicated that if these results can be replicated on the SNL system, the improved performance could be revolutionary as the method to approximate the nonlinear operator for a hyperspectral CT system is not feasible to perform on a traditional CT system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

COLLABORATIVE DEVELOPMENT PROJECTS - PHOTONIC MEMORY CONTROLLER MODULE (P-MCM)

As computational density for high-performance computing and big-data services continues to scale, performance scalability of next generation computing systems is becoming increasingly constrained by limitations in memory access, power dissipation and chip packaging. The processor-memory communication bottleneck, a major challenge in current multicore processors due to limited pin-out and power budget, presents a detrimental scaling barrier to data-intensive computing. A consortium team of small businesses and leading researchers that includes experts from photonics processor-memory architecture, III/V photonic laser design/fabrication, silicon photonics design/fabrication, photonics packaging and assembly, and FPGA-based high-performance memory controller IP development – to collaboratively develop a commercialization path for a Photonic Memory Controller Module (P-MCM).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Application Experiences on a GPU-Accelerated Arm-based HPC Testbed

This paper assesses and reports the experience of ten teams working to port, validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems, each one equipped with a server-class Arm CPU from Ampere Computing and two data center GPUs from NVIDIA Corp. The systems are connected together using InfiniBand interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.

Elwasif, Wael↗
DOE OSTI2019

Parallel Computing for Massive Dynamic Contingency Analysis

The first step towards running PSLF in a high-performance computing (HPC) environment was porting PSLF to the Linux operating system from the current shipping target of MS Windows. The target machine was the LLNL Quartz system which is a Linux cluster configured for large parallel jobs. It has 2,688 batch nodes with Dual Socket Intel E5-2695 v4 (Broadwell) CPUs with 18 cores each, with a total of 344,064 GB of memory, each node having 128 GB of memory. Nodes are connected with an Intel Omni-Path 100 GB/s interconnect. Quartz runs the Tri-Laboratory Operating System Software (TOSS) that includes Red Hat Enterprise Linux (RHEL) distribution used on the LLNL HPC machines.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Harnessing the power of supercomputers using the PanDA Pilot 2 in the ATLAS Experiment

The unprecedented computing resource needs of the ATLAS experiment at LHC have motivated the Collaboration to become a leader in exploiting High Performance Computers (HPCs). To meet the requirements of HPCs, the PanDA system has been equipped with two new components; Pilot 2 and Harvester, that were designed with HPCs in mind. While Harvester is a resource-facing service which provides resource provisioning and workload shaping, Pilot 2 is responsible for payload execution on the resource. The presentation focuses on Pilot 2, which is a complete rewrite of the original PanDA Pilot used by ATLAS and other experiments for well over a decade. Pilot 2 has a flexible and adaptive design that allows for plugins to be defined with streamlined workflows. In particular, it has plugins for specific hardware infrastructures (HPC/GPU clusters) as well as for dedicated workflows defined by the needs of an experiment. Examples of dedicated HPC workflows are discussed in which the Pilot either uses an MPI application for processing fine-grained event level service under the control of the Harvester service or acts like an MPI application itself and runs a set of job in an assemble. In addition to describing the technical details of these workflows, results are shown from its deployment on Titan (OLCF) and other HPCs in ATLAS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Transforming Energy Through Computational Excellence: A Virtual Test-Drive of Energy Scenarios

Researchers at the National Renewable Energy Laboratory (NREL) are conducting groundbreaking research to decarbonize transportation systems and reduce emissions. As they plan for growing populations and related demands on aging infrastructure and energy systems, local transportation departments and city planners are facing significant, complex challenges. New approaches to planning and operations, as well as capitalizing on rapidly advancing technology, can address mobility and energy-centric challenges, including cost and energy efficiency, emissions reduction, decarbonization, and congestion.

33 ADVANCED PROPULSION SYSTEMS↗