4,061 research outputs found

    Computing and data processing

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    The applications of computers and data processing to astronomy are discussed. Among the topics covered are the emerging national information infrastructure, workstations and supercomputers, supertelescopes, digital astronomy, astrophysics in a numerical laboratory, community software, archiving of ground-based observations, dynamical simulations of complex systems, plasma astrophysics, and the remote control of fourth dimension supercomputers

    Parallel implementation of the TRANSIMS micro-simulation

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    This paper describes the parallel implementation of the TRANSIMS traffic micro-simulation. The parallelization method is domain decomposition, which means that each CPU of the parallel computer is responsible for a different geographical area of the simulated region. We describe how information between domains is exchanged, and how the transportation network graph is partitioned. An adaptive scheme is used to optimize load balancing. We then demonstrate how computing speeds of our parallel micro-simulations can be systematically predicted once the scenario and the computer architecture are known. This makes it possible, for example, to decide if a certain study is feasible with a certain computing budget, and how to invest that budget. The main ingredients of the prediction are knowledge about the parallel implementation of the micro-simulation, knowledge about the characteristics of the partitioning of the transportation network graph, and knowledge about the interaction of these quantities with the computer system. In particular, we investigate the differences between switched and non-switched topologies, and the effects of 10 Mbit, 100 Mbit, and Gbit Ethernet. keywords: Traffic simulation, parallel computing, transportation planning, TRANSIM

    Application of computational physics within Northrop

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    An overview of Northrop programs in computational physics is presented. These programs depend on access to today's supercomputers, such as the Numerical Aerodynamical Simulator (NAS), and future growth on the continuing evolution of computational engines. Descriptions here are concentrated on the following areas: computational fluid dynamics (CFD), computational electromagnetics (CEM), computer architectures, and expert systems. Current efforts and future directions in these areas are presented. The impact of advances in the CFD area is described, and parallels are drawn to analagous developments in CEM. The relationship between advances in these areas and the development of advances (parallel) architectures and expert systems is also presented

    Working Papers: Astronomy and Astrophysics Panel Reports

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    The papers of the panels appointed by the Astronomy and Astrophysics survey Committee are compiled. These papers were advisory to the survey committee and represent the opinions of the members of each panel in the context of their individual charges. The following subject areas are covered: radio astronomy, infrared astronomy, optical/IR from ground, UV-optical from space, interferometry, high energy from space, particle astrophysics, theory and laboratory astrophysics, solar astronomy, planetary astronomy, computing and data processing, policy opportunities, benefits to the nation from astronomy and astrophysics, status of the profession, and science opportunities

    Exploring the Performance Benefit of Hybrid Memory System on HPC Environments

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    Hardware accelerators have become a de-facto standard to achieve high performance on current supercomputers and there are indications that this trend will increase in the future. Modern accelerators feature high-bandwidth memory next to the computing cores. For example, the Intel Knights Landing (KNL) processor is equipped with 16 GB of high-bandwidth memory (HBM) that works together with conventional DRAM memory. Theoretically, HBM can provide 5x higher bandwidth than conventional DRAM. However, many factors impact the effective performance achieved by applications, including the application memory access pattern, the problem size, the threading level and the actual memory configuration. In this paper, we analyze the Intel KNL system and quantify the impact of the most important factors on the application performance by using a set of applications that are representative of scientific and data-analytics workloads. Our results show that applications with regular memory access benefit from MCDRAM, achieving up to 3x performance when compared to the performance obtained using only DRAM. On the contrary, applications with random memory access pattern are latency-bound and may suffer from performance degradation when using only MCDRAM. For those applications, the use of additional hardware threads may help hide latency and achieve higher aggregated bandwidth when using HBM

    Report from the Tri-Agency Cosmological Simulation Task Force

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    The Tri-Agency Cosmological Simulations (TACS) Task Force was formed when Program Managers from the Department of Energy (DOE), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF) expressed an interest in receiving input into the cosmological simulations landscape related to the upcoming DOE/NSF Vera Rubin Observatory (Rubin), NASA/ESA's Euclid, and NASA's Wide Field Infrared Survey Telescope (WFIRST). The Co-Chairs of TACS, Katrin Heitmann and Alina Kiessling, invited community scientists from the USA and Europe who are each subject matter experts and are also members of one or more of the surveys to contribute. The following report represents the input from TACS that was delivered to the Agencies in December 2018.Comment: 36 pages, 3 figures. Delivered to NASA, NSF, and DOE in Dec 201

    Idle Period Propagation in Message-Passing Applications

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    Idle periods on different processes of Message Passing applications are unavoidable. While the origin of idle periods on a single process is well understood as the effect of system and architectural random delays, yet it is unclear how these idle periods propagate from one process to another. It is important to understand idle period propagation in Message Passing applications as it allows application developers to design communication patterns avoiding idle period propagation and the consequent performance degradation in their applications. To understand idle period propagation, we introduce a methodology to trace idle periods when a process is waiting for data from a remote delayed process in MPI applications. We apply this technique in an MPI application that solves the heat equation to study idle period propagation on three different systems. We confirm that idle periods move between processes in the form of waves and that there are different stages in idle period propagation. Our methodology enables us to identify a self-synchronization phenomenon that occurs on two systems where some processes run slower than the other processes.Comment: 18th International Conference on High Performance Computing and Communications, IEEE, 201

    Enabling real-time multi-messenger astrophysics discoveries with deep learning

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    Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics
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