Publications

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A memo concerning the status of the Exascale Computing Project (ECP) STDA05-54 is given.

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The field of visualization encompasses a wide range of techniques, from infographics to isosurfaces. An important subfield called "scientific visualization" is specifically dedicated to data sets with spatial components, i.e., (X, Y, Z) locations. Furthermore, this subfield's name is inspired by the fact that the data in question often come from the sciences, i.e., physics simulations or sensor networks.

The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. Here, they discuss these axes, evaluate existing systems within the axes, and explore how currently used terms relate to the axes.

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. The results of this project will be delivered in tools like ParaView, 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. One of the biggest recent changes in high-performance computing is the increasing use of accelerators. Accelerators contain processing cores that independently are inferior to a core in a typical CPU, but these cores are replicated and grouped such that their aggregate execution provides a very high computation rate at a much lower power. Current and future CPU processors also require much more explicit parallelism. Each successive version of the hardware packs more cores into each processor, and technologies like hyper threading and vector operations require even more parallel processing to leverage each core's full potential. VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures. The ECP/VTK-m project is building up the VTK-m codebase with the necessary visualization algorithm implementations that run across the varied hardware platforms to be leveraged at the Exascale. We will be working with other ECP projects, such as ALPINE, to integrate the new VTK-m code into production software to enable visualization on our HPC systems.

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The Exascale Computing Project STDA05-53 milestone status is summarized.

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In the case for each of the tasks, implementation started in a private topic branch. That branch was later submitted as a merge request where the code was run through regression tests across multiple test platforms. The merge requests were also subjected to human reviewers for approval. After necessary modifications were made, the code was merged to VTK-m's master branch. Subsequently, documentation was written for the VTK-m User's Guide.

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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. The results of this project will be delivered in tools like ParaView, 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.

The milestone tasks and completion proofs are provided.

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Scientific computing is no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/0 limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. The XVis project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis. This report reviews the accomplishments of the XVis project to prepare scientific visualization for Exascale computing.

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The following table provides evidence for each implemented feature with links to the completed merge requests (evidence that the implementation is merged into the master branch) and a link to the excerpt from the VTK-m User's Guide documenting the feature

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An short overview, status, and accomplishments of the VTK-m contribution are provided.

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The STDA05-17 milestone comprises the following 3 deliverables. VTK-m Release 2 We will provide a release of VTK-m software and associated documentation. The source code repository will be tagged at a stable state, and, at a minimum, tarball captures of the source code will be made available from the web site. A version of the VTK-m User's Guide documenting this release will also be made available. Productionize zfp compression The "ZFP: Compressed Floating-Point Arrays" project (WBS 1.3.4.13) is creating an implementation of ZFP compression in VTK-m. Their implementation will be focused on operating in CUDA. The VTK-m project will assist by generalizing the implementation to other devices (such as multi-core CPUs). We will also assist in productionizing the code such that it can be used by external projects and products. Clip Clip operations intersect meshes with implicit functions. It is the foundation of spatial subsetting algorithms, such as "box," and the foundation of data-based subsetting, such as "isovolume." The algorithm requires considering thousands of possible cases, and is thus quite difficult to implement. This milestone will implement clipping to be sufficient for Visit's and ParaView's needs.

The STDA05-16 milestone comprises the following 3 distinct deliverables. OpenMP VTK-m currently supports three types of devices: serial CPU, TBB, and CUDA. To run algorithms on multicore CPU-type devices (such as Xeon and Xeon Phi), TBB is required. However, there are known issues with integrating a software product using TBB with another one using OpenMP. Therefore, we will add an OpenMP device to the VTK-m software. When engaged, this device will run parallel algorithms using OpenMP directives. This will mesh more nicely with other code also using OpenMP. Rendering Topological Entities VTK-m currently supports surface rendering by tessellation of data structures,and rendering the resulting triangles. We will extend current functionality to include face, edge, and point rendering. Better Dynamic Types Impl For the best efficiency across all platforms, VTK-m algorithms use static typing with C++ templates. However, many libraries like VTK, ParaView, and Visit use dynamic types with virtual functions because data types often cannot be determined at compile time. We have an interface in VTK-m to merge these two typing mechanisms by generating all possible combinations of static types when faced with a dynamic type. Although this mechanism works, it generates very large executables and takes a very long time to compile. As we move forward, it is clear that these problems will get worse and become infeasible at exascale. We will rectify the problem by introducing some level of virtual methods, which require only a single code path, within VTK-m algorithms. This first milestone produces a design document to propose an approach to the new system.

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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 ParaView, 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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Running visualization and analysis algorithms on ATS-1 platforms is a critical step for supporting ATDM apps at the exascale level. We are leveraging VTK-m to port our algorithms to the ATS-specific hardware and ensuring that it runs well.

ParaView Catalyst is an API for accessing the scalable visualization infrastructure of ParaView in an in-situ context. In-situ visualization allows simulation codes to access data post-processing operations while the simulation is running. In-situ techniques can reduce data post-processing time, allow computational steering, and increase the resolution and frequency of data output. For a simulation code to use ParaView Catalyst, adapter code needs to be created that interfaces the simulations data structures to ParaView/VTK data structures. Under ATDM, Catalyst is to be integrated with SPARC, a code used for simulation of unsteady reentry vehicle flow.

A key component of most large-scale rendering systems is a parallel image compositing algorithm, and the most commonly used compositing algorithms are binary swap and its variants. Although shown to be very efficient, one of the classic limitations of binary swap is that it only works on a number of processes that is a perfect power of 2. Multiple variations of binary swap have been independently introduced to overcome this limitation and handle process counts that have factors that are not 2. To date, few of these approaches have been directly compared against each other, making it unclear which approach is best. This paper presents a fresh implementation of each of these methods using a common software framework to make them directly comparable. These methods to run binary swap with odd factors are directly compared. The results show that some simple compositing approaches work as well or better than more complex algorithms that are more difficult to implement.

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