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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 235 records · Page 13

URBAN-NET: A Network-based Infrastructure Monitoring and Analysis System for Emergency Management and Public Safety

Abstract Critical Infrastructures (CIs) such as energy, water, and transportation are complex networks that are crucial for sustaining day-to-day commodity flows vital to national security, economic stability, and public safety. The nature of these CIs is such that failures caused by an extreme weather event or a man-made incident can trigger widespread cascading failures, sending ripple effects at regional or even national scales. To minimize such effects, it is critical for emergency responders to identify existing or potential vulnerabilities within CIs during such stressor events in a systematic and quantifiable manner and take appropriate mitigating actions. We present here a novel critical infrastructure monitoring and analysis system named URBAN-NET. The system includes a software stack and tools for monitoring CIs, pre-processing data, interconnecting multiple CI datasets as a heterogeneous network, identifying vulnerabilities through graph-based topological analysis, and predicting consequences based on what-if simulations along with visualization. As a proof-of-concept, we present several case studies to show the capabilities of our system. We also discuss remaining challenges and future work.

Lee, Sangkeun↗

High-Performance I/O: HDF5 for Lattice QCD

Practitioners of lattice QCD/QFT have been some of the primary pioneer users of the state-of-the-art high-performance-computing systems, and contribute towards the stress tests of such new machines as soon as they become available. As with all aspects of high-performance-computing, I/O is becoming an increasingly specialized component of these systems. In order to take advantage of the latest available high-performance I/O infrastructure, to ensure reliability and backwards compatibility of data files, and to help unify the data structures used in lattice codes, we have incorporated parallel HDF5 I/O into the SciDAC supported USQCD software stack. Here we present the design and implementation of this I/O framework. Our HDF5 implementation outperforms optimized QIO at the 10-20% level and leaves room for further improvement by utilizing appropriate dataset chunking.

97 MATHEMATICS AND COMPUTING↗

Linux containers for fun and profit in HPC

This article outlines options for user-defined software stacks from an HPC perspective. Here, we argue that a lightweight approach based on Linux containers is most suitable for HPC centers because it provides the best balance between maximizing service of user needs and minimizing risks. We also discuss how containers work and several implementations, including Charliecloud, our own open-source solution developed at Los Alamos.

97 MATHEMATICS AND COMPUTING↗

Experiences evaluating functionality and performance of IBM Power8+ systems

InpreparationforSummit,OakRidgeNationalLaboratory’s next generation supercomputer, two IBM Power-based systems were deployed in late 2016 at the Oak Ridge Leadership Computing Facility (OLCF). This paper presents a detailed description of the acceptance of the first IBM Power-based early access systems installed at the OLCF. The two systems, Summitdev and Tundra, contain IBM Power8+ processors with NVIDIA Pascal GPUs and were acquired to provide researchers with a platform to optimize codes for the Power architecture. In addition, this paper presents early functional and performance results obtained on Summitdev with the latest software stack available.

Melesse Vergara, Veronica↗

Quantum Accelerators for High-performance Computing Systems

We define some of the programming and system-level challenges facing the application of quantum processing to high-performance computing. Alongside barriers to physical integration, prominent differences in the execution of quantum and conventional programs challenges the intersection of these computational models. Following a brief overview of the state of the art, we discuss recent advances in programming and execution models for hybrid quantum-classical computing. We discuss a novel quantum-accelerator framework that uses specialized kernels to offload select workloads while integrating with existing computing infrastructure. We elaborate on the role of the host operating system to manage these unique accelerator resources, the prospects for deploying quantum modules, and the requirements placed on the language hierarchy connecting these different system components. We draw on recent advances in the modeling and simulation of quantum computing systems with the development of architectures for hybrid high-performance computing systems and the realization of software stacks for controlling quantum devices. Finally, we present simulation results that describe the expected system-level behavior of high-performance computing systems composed from compute nodes with quantum processing units. We describe performance for these hybrid systems in terms of time-to-solution, accuracy, and energy consumption, and we use simple application examples to estimate the performance advantage of quantum acceleration.

Humble, Travis↗

STAR'S Approach to Highly Efficient End-To-End Grid Production

STAR's RHIC computing facility provides over 15K dedicated slots for data reconstruction. This number of slots is not always sufficient to satisfy an ambitious and data-challenging physics program, and harvesting resources from outside facilities is paramount to scientific success. However, constraints of remote sites (e.g. CPU time limits) do not always provide the flexibility of a dedicated farm. STAR has a breadth of smaller datasets (both in runtime and size) that can be easily offloaded to remote facilities with many such limits. Scavenged resources can be run with efficiency comparable to that of the local production and contributes additional computing time to an experiment that runs every year and therefore needs fast turnaround. We will discuss STAR's software stack of our grid production framework including features dealing with multi-site submission, automated re-submission, job tracking, as well as new challenges and possible improvements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DXT: Darshan eXtended Tracing

As modern supercomputers evolve to exascale, their I/O subsystems are becoming increasingly complex, making optimization of I/O for scientific applications a daunting task. Although I/O profiling tools facilitate the process of optimizing application I/O performance, legacy profiling tools lack flexibility in their level of detail and ability to correlate traces with other sources of data. Additionally, a lack of robust trace analysis tools makes it difficult to derive actionable insights from large-scale I/O traces. Darshan is an HPC I/O characterization tool that records statistics in a lightweight manner that makes it appropriate for full-time production deployment. However, Darshan’s default characterization mechanism records information at a fixed granularity. We augment Darshan by proposing Darshan eXtended Tracing (DXT) for more detailed profiling of I/O software stacks. DXT enables users and administrators to vary the level of fidelity captured by Darshan at run time without modifying or recompiling applications. This capability facilitates systematic analysis on the I/O behavior of applications and can provide useful application kernel I/O traces to help advance parallel I/O research. We have demonstrated the power of DXT by obtaining a wide range of useful statistics for multiple case studies, and we further show that DXT is able to do so same with negligible overhead.

97 MATHEMATICS AND COMPUTING↗

HEP Software Foundation Community White Paper Working Group - Data Processing Frameworks

Data processing frameworks are an essential part of HEP experiments' software stacks. Frameworks provide a means by which code developers can undertake the essential tasks of physics data processing, accessing relevant inputs and storing their outputs, in a coherent way without needing to know the details of other domains. Frameworks provide essential core services for developers and help deliver a configurable working application to the experiments' production systems. Modern HEP processing frameworks are in the process of adapting to a new computing landscape dominated by parallel processing and heterogeneity, which pose many questions regarding enhanced functionality and scaling that must be faced without compromising the maintainability of the code. In this paper we identify a program of work that can help further clarify the key concepts of frameworks for HEP and then spawn R&D activities that can focus the community's efforts in the most efficient manner to address the challenges of the upcoming experimental program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Scaling the Summit: Deploying the World's Fastest Supercomputer

Summit, the latest flagship supercomputer deployed at Oak Ridge Leadership Computing Facility (OLCF), became the number one system in the Top500 list in June 2018 and remains in the top spot in the most recent edition of the list. An extensive acceptance test plan was developed to evaluate the unique features introduced in the Summit architecture and system software stack. The acceptance test also includes tests to ensure that the system is reliable, stable, and performant.

Melesse Vergara, Veronica↗

Requirements, Status, and Plans for Track Reconstruction at the sPHENIX Experiment

sPHENIX is a new experiment that is being constructed at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The primary physics goals of sPHENIX are to measure jets, their substructure, and the upsilon resonances in p+p, p+Au and Au+Au collisions. To realize these goals, a tracking system composed of a time projection chamber and several silicon detectors will be used to identify tracks that correspond to jets and upsilon decays. However, the sPHENIX experiment will collect approximately 200 PB of data utilizing a finite-sized computing center; thus, performing track reconstruction in a timely manner is a challenge due to the large occupancy of heavy-ion collisions. The sPHENIX experiment, its track reconstruction, and the need for implementing faster track-fitting algorithms, such as that provided by the A Common Tracking Software package, into the sPHENIX software stack are discussed.

Osborn, Joe↗

Comparison of Simulated and Experimentally Measured Detector Impulse Responses to Pulsed X-rays

The Gamma Array Simulation Toolkit (GAST) is a multi-physics software stack comprised of Geant4, MATLAB, and experimentally measured photomultiplier tube (PMT) impulse response waveforms that is used to model and inform the end-to-end performance of radiation detector systems. In order to validate the ability of GAST to predict the impulse response of a modeled detector configuration operated in current mode, experimental measurements using the Nevada National Security Site Transformational Diagnostics and Imaging 2 MeV endpoint energy electron linear accelerator (linac) with a tungsten target were performed. Seven different detector configurations consisting of unique combinations of scintillator material, size, reflectivity conditions, presence or absence of light guide, and PMT were evaluated. The resultant impulse response of each detector configuration was recorded and compared to a GAST simulation that emulated the respective linac experiment conditions. The comparison of the experimental and simulated detector impulse responses indicated that GAST can reliably predict detector impulse responses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

On the Marriage of Asynchronous Many Task Runtimes and Big Data: A Glance

The rise of the accelerator-based architectures and reconfigurable computing have showcased the weakness of software stack toolchains that still maintain a static view of the hardware instead of relying on a symbiotic relationship between static (e.g., compilers) and dynamic tools (e.g., runtimes). In the past decades, this need has given rise to adaptive runtimes with increasingly finer computational tasks. These finer tasks help to take advantage of the hardware by switching out when a long latency operation is encountered (because of the deeper memory hierarchies and new memory technologies that might target streaming instead of random access), thus trading off idle time for unrelated work. Examples of these finer task runtimes are Asynchronous Many Task (AMT) runtimes, in which highly efficient computational graphs run on a variety of hardware. Due to its inherent latency tolerant characteristics, Latency-sensitive applications, such as Graph Analytics and Big Data can effectively use these runtimes. This paper aims to present an example of how the careful design of an AMT can exploit the hardware substrate when faced with high latency applications such as the ones given in the Big Data domain. Moreover, with its introspection and adaptive capabilities, we aim to show the power of these runtimes when facing the changing requirements of the application workloads. We use the Performance Open Community Runtime (P-OCR) as our vehicle to demonstrate the concepts presented here.

adaptive runtime, big data analysis↗

Bringing OpenCL to Commodity RISC-V CPUs

The importance of open-source hardware has been increasing in recent years with the introduction of the RISC-V Open ISA. This has also accelerated the push for support of the open-source software stack from compiler tools to full-blown operating systems. Parallel computing with today’s Application Programming Interfaces such as OpenCL has proven to be effective at leveraging the parallelism in commodity multi-core processors and programmable parallel accelerators. However, to the best of our knowledge, there is currently no publicly available implementation of OpenCL targeting commodity RISC-V processors that is accessible to the open-source community. Besides opening RISC-V to the existing rich variety of scientific parallel applications, OpenCL also provides access to a unique genre of benchmarks useful in computer architecture research. In this work, we extended an Open-source implementation of OpenCL to target RISC-V CPUs. Our work not only cover commodity multi-core RISC-V processors, but also plethora of low- profile embedded RISC-V CPUs that often do not support atomic instructions or multi-threading.

Tine, Blaise↗

Science Federation Emulation Testbed: Demonstration of VFSIE Functionalities

The Virtual Federated Science Instrument Environment (VFSIE) is a digital twin of a federated infrastructure of multiple sites with science instruments and computing systems that are geographically dispersed. It emulates the federation using containers and virtual hosts (vhosts) connected over local and wide-area networks for the main purpose of supporting the development and testing of science workflows and software stacks, which will subsequently transition to the physical infrastructure. In this demo, we present VFSIE of a four sites scenario and illustrate the following use cases: (i) execution of federation stack components that expose resources to enable a science user to execute workflows, (ii) execution of remote control commands of beam-line instruments to collect measurements and position the sensor, and (iii) remote execution and access of Jupyter Notebook for tomographic image computations

Al Najjar, Anees↗

GradientGraph

Under this SBIR Phase II, Reservoir Labs has developed G2 Analytics, a new technology that allows network operators to analyze bottleneck and flow performance with high precision. G2 delivers a new analytical approach and framework to resolve a variety of key problems found in modern communication networks, including: traffic engineering, routing, flow scheduling, network design, capacity planning, resiliency analysis, network slicing, or service level agreement (SLA) management, among others. G2 leverages the bottleneck structure of congestion-controlled communication networks, a recent mathematical discovery by the Reservoir team [RL19b, RL20a, RL20b, RL21a]. Bottleneck structures reveal how perturbations on flows and links propagate through the network, providing an analytical framework to measure (qualitatively and quantitatively) the ripple effects induced as they traverse the network. Leveraging the mathematics of bottleneck structures, Reservoir Labs is developing the G2 technology to provide network operators with a framework to design, optimize and troubleshoot network performance. This delivery includes the G2 software stack.

Yellamraju, Sruthi↗

ExaWind: Opportunity for Discovery

The predictive simulation of the dynamics of modern wind turbines and wind farms is a high-performance-computing (HPC) grand challenge. Modern wind turbines are the largest rotating machines in the world, with rotor diameters exceeding 200 meters, and with heights well into the atmospheric boundary layer. To address this grand challenge, the U.S. Department of Energy (DOE) Wind Energy Technologies Office and the Exascale Computing Project have been supporting the creation of the ExaWind modeling and simulation environment since 2016. ExaWind is composed of the incompressible-flow computational-fluid-dynamics (CFD) solvers AMR-Wind and Nalu-Wind and the wind-turbine-dynamics solver OpenFAST. In our hybrid CFD approach, Nalu-Wind unstructured-grid models resolve the near-body flow around the turbine; those models are embedded in, and two-way coupled to, an AMR-Wind structured-grid mesh through overset meshes. In this talk I give an overview of ExaWind software stack and describe how it is being used to address important problems in wind energy.

computational fluid dynamics↗

ExaWind: Then and Now

The scientific goal of the ExaWind project is to advance our fundamental understanding of the flow physics governing whole wind plant performance, including wake formation, complex terrain impacts, and turbine-turbine-interaction effects. The primary application codes in the ExaWind environment are Nalu-Wind, an unstructured-grid computational fluid dynamics (CFD) code, AMR-Wind, a structured-grid CFD code, and OpenFAST, a whole-turbine simulation code. In this poster we present the current status of the ExaWind software stack in the context of the modeling and simulation capabilities when the project started in 2016.

computational fluid dynamics↗

Autonomous Robotic Manipulator Software

Autonomous robotic manipulation requires a deep and wide stack of supporting software. This paper presents Autonomous Robotic Manipulator Software (ARMS), a software suite designed at NASA Langley Research Center to support research and development of different algorithms for In-space Servicing, Assembly and Manufacturing (ISAM). ARMS solves common challenges along the autonomous manipulation software stack. Various challenges, such as integration with commercial hardware, simulation, and path planning, are solved through the use of Robot Operating System 2 and its community-developed packages. Other challenges, such as configuration management and task definition, and execution are solved in software built on those tools. The result is a modular approach to robotic system definition, agent actions, and assembly task definitions. ARMS has been used in two ISAM projects at NASA Langley Research Center, the Precision Assembled Space Structures project and the Built On-orbit Robotically assembled Gigatruss project.

Collin J Cresta↗