Publications

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The STDA05-15 milestone comprises the following 4 distinct deliverables: 1) External Surface, 2) Locate Point, 3) Locate Cell and 4) Point Movement.

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Predefined functions are necessary components of scientific visualization. This milestone will ensure that these core functions will be implemented in VTK-m and made available to ParaView, and Visit.

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Achieving computational rates beyond the petascale requires an increasing amount of heterogeneity in our highperformance computing (HPC) hardware. This heterogeneity, in turn, means that a node of an HPC system can no longer be considered a monolithic resource. Rather, a node has many individual components such as processors, cores, SMT threads, accelerators, and tierd memories that must be further allocated and managed by... something. Currently, that something is an ad-hoc mix of arguments and environments when launching jobs. We follow the same process used on prior HPC systems with nodes of uniform components; unfortunately, the shims introduced to provide the additional specification of node-level components are inconsistent and unwieldy. As we shall see, even at our current moderate level of heterogeneity our effective utilization of HPC software is hampered by poor resource management. Future systems will continue to grow in heterogeneity both in number and type of resources. Our current approach to resource management cannot scale. We need a more cohesive approach to managing heterogeneous resources in HPC systems.

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The ECP/VTK-m project is providing the core capabilities to perform scientific visualization on exascale architectures. The ECP/VTK-m project fills the critical feature gap of performing visualization and analysis on processors like graphics-based processors and many integrated core. The results of this project will be delivered in tools like Para View, Vislt, and Ascent as well as in stand-alone form. Moreover, these projects are depending on this ECP effort to be able to make effective use of ECP architectures.

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The SNL ATDM Data and Visualization work consolidates existing ATDM activities in scalable data management and visualization. Part of the responsibilities of the SNL ATDM Data and Visualization Project is the maintenance and development of visualization resources for ATDM applications on Exascale platforms. The ATDM Scalable Visualization project provides visualization and analysis required to satisfy the needs of the ASC/ATDM applications on next-generation, many-core platforms. This involves many activities including the re-engineering of visualization algorithms, services, and tools that enable ASC customers to carry out data analysis on ASC systems and ACES platforms. Current tools include scalable data analysis software released open source through ParaView, VTK, and Catalyst. We are also both leveraging and contributing to VTK-m, a many-core visualization library, to satisfy our visualization needs on advanced architectures. The scope of the Scalable Visualization under ATDM at SNL is R&D for the programming model and implementation of visualization code for ASC/ATDM projects and ASC/ATDM application support.

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Today’s computational, experimental, and observational sciences rely on computations that involve many related tasks. The success of a scientific mission often hinges on the computer automation of these workflows. In April 2015, the US Department of Energy (DOE) invited a diverse group of domain and computer scientists from national laboratories supported by the Office of Science, the National Nuclear Security Administration, from industry, and from academia to review the workflow requirements of DOE’s science and national security missions, to assess the current state of the art in science workflows, to understand the impact of emerging extreme-scale computing systems on those workflows, and to develop requirements for automated workflow management in future and existing environments. This article is a summary of the opinions of over 50 leading researchers attending this workshop. We highlight use cases, computing systems, workflow needs and conclude by summarizing the remaining challenges this community sees that inhibit large-scale scientific workflows from becoming a mainstream tool for extreme-scale science.

The FY18Q1 milestone of the ECP/VTK-m project includes the implementation of a multiblock data set, the completion of a gradients filtering operation, and the release of version 1.1 of the VTK-m software. With the completion of this milestone, the new multiblock data set allows us to iteratively schedule algorithms on composite data structures such as assemblies or hierarchies like AMR. The new gradient algorithms approximate derivatives of fields in 3D structures with finite differences. Finally, the release of VTK-m version 1.1 tags a stable release of the software that can more easily be incorporated into external projects.

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