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Composable Infrastructures for an Academic Research Environment: Lessons Learned

Composable Infrastructures for an Academic Research Environm...

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ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Lance Long Timothy Bargo Luc Renambot Maxine Brown Andrew E. Johnson Electronic Visualization Lab Computer Science Univ Illinois Chicago Chicago Illinois USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Composable infrastructure holds the promise of accelerating the pace of academic research and discovery by enabling researchers to tailor the resources of a machine (e.g., GPUs, storage, NICs), on-demand, to address application needs. We were first introduced to composable infrastructure in 2018, and at the same time, there was growing demand among our College of Engineering faculty for GPU systems for data science, artificial intelligence / machine learning / deep learning, and visualization. Many purchased their own individual desktop or deskside systems, a few pursued more costly cloud and HPC solutions, and others looked to the College or campus computer center for GPU resources which, at the time, were scarce. After surveying the diverse needs of our faculty and studying product offerings by a few nascent startups in the composable infrastructure sector, we applied for and received a grant from the National Science Foundation in November 2019 to purchase a mid-scale system, configured to our specifications, for use by faculty and students for research and research training. This paper describes our composable infrastructure solution and implementation for our academic community. Given how modern workflows are progressively moving to containers and cloud frameworks (using Kubernetes) and to programming notebooks (primarily Jupyter), both for ease of use and for ensuring reproducible experiments, we initially adapted these tools for our system. We have since made it simpler to use our system, and now provide our users with a public facing JupyterHub server. We also added an expansion chassis to our system to enable composable co-location, which is a shared central architecture in which our researchers can insert and integrate specialized resources (GPUs, accelerators, networking cards, etc.) needed for their research. In February 2020, installation of our system was finalized and made operational and we began providing access to faculty in the College of Engineer

关键词： Training Industries Codes graphics processing units Metals Containers Writing

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parallel Particle Advection and Lagrangian Analysis for 3D-PLI Fiber Orientation Maps 9

Parallel Particle Advection and Lagrangian Analysis for 3D-P...

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9th ieee symposium on Large Data Analysis and visualization (LDAV)

作者： Demiralp, Ali C. Zielasko, Daniel Axer, Markus Vierjahn, Tom Kuhlen, Torsten W. Rhein Westfal TH Aachen Visual Comp Inst JARA HPC Aachen Germany Univ Trier Trier Germany Julich Res Ctr Julich Germany Westphalian Univ Appl Sci Gelsenkirchen Germany

ISBN: (纸本)9781728126050

3D-Polarized Light Imaging (3D-PLI) is a recent neuroimaging technique which is able to record the orientation of nerve fibers in unprecedented detail, reaching micrometer scale. Particle advection, which streamline tractography is based upon, is an established tool to study the structure of fiber tracts within the brain. This work presents a distributed particle advection pipeline for 3D-PLI fiber orientation maps, which are time-invariant vector fields describing the dominant orientation of nerve fibers at each point. Due to the high resolution, 3D-PLI creates large datasets ranging from few gigabytes up to hundreds of terabytes, which is addressed through a data-parallel approach to advection. Furthermore, we propose Lagrangian analysis of time-invariant brain data through computation of Finite-Time Lyapunov Exponents (FTLE), which may aid identification of macroscopic fiber bundles.

关键词： D.1.3 [Software]: Programming Techniques Concurrent programming I.3.5 [Computer graphics]: Computational Geometry and Object Modeling Curve, surface, solid and object representations

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Interactive visualization of Terascale Data in the Browser: Fact or Fiction?

Interactive Visualization of Terascale Data in the Browser: ...

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ieee symposium on Large Data Analysis and visualization (LDAV)

作者： Will Usher Valerio Pascucci SCI Institute University of Utah

ISBN: (数字)9781728184685

ISBN: (纸本)9781728184692

Information visualization applications have become ubiquitous, in no small part thanks to the ease of wide distribution and deployment to users enabled by the web browser. Scientific visualization applications, relying on native code libraries and parallel processing, have been less suited to such widespread distribution, as browsers do not provide the required libraries or compute capabilities. In this paper, we revisit this gap in visualization technologies and explore how new web technologies, WebAssembly and WebGPU, can be used to deploy powerful visualization solutions for large-scale scientific data in the browser. In particular, we evaluate the programming effort required to bring scientific visualization applications to the browser through these technologies and assess their competitiveness against classic native solutions. As a main example, we present a new GPU-driven isosurface extraction method for block-compressed data sets, that is suitable for interactive isosurface computation on large volumes in resource-constrained environments, such as the browser. We conclude that web browsers are on the verge of becoming a competitive platform for even the most demanding scientific visualization tasks, such as interactive visualization of isosurfaces from a 1TB DNS simulation. We call on researchers and developers to consider investing in a community software stack to ease use of these upcoming browser features to bring accessible scientific visualization to the browser.

关键词： Browsers Isosurfaces graphics processing units Servers Rendering (computer graphics) Libraries Arrays

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parallel Partial Reduction for Large-Scale Data Analysis and visualization 8

Parallel Partial Reduction for Large-Scale Data Analysis and...

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8th ieee symposium on Large-Scale Data Analysis and visualization (LDAV)

作者： He, Wenbin Guo, Hanqi Peterka, Tom Di, Sheng Cappello, Franck Shen, Han-Wei Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA

ISBN: (纸本)9781538668733

We present a novel partial reduction algorithm to aggregate sparsely distributed intermediate results that are generated by data-parallel analysis and visualization algorithms. Applications of partial reduction include flow trajectory analysis, big data online analytical processing, and volume rendering. Unlike traditional full parallel reduction that exchanges dense data across all processes, the purpose of partial reduction is to exchange only intermediate results that correspond to the same query, such as line segments of the same flow trajectory. To this end, we design a three-stage algorithm that minimizes the communication cost: (1) partitioning the result space into groups;(2) constructing and optimizing the reduction partners for each group;and (3) initiating collective reduction operations for all groups concurrently. Both theoretical and empirical analyses show that our algorithm outperforms the traditional methods when the intermediate results are sparsely distributed. We also demonstrate the effectiveness of our algorithm for flow visualization, big log data analysis, and volume rendering.

关键词： I.3.1 [COMPUTER graphics]: Hardware Architecture-parallel processing I.3.2 [COMPUTER graphics]: graphics Systems-Distributed/network graphics

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Making a Case for Green High-Performance visualization via Embedded graphics Processors 32

Making a Case for Green High-Performance Visualization via E...

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32nd ieee International parallel and Distributed Processing symposium (IPDPS)

作者： Adhinarayanan, Vignesh Dutta, Bishwajit Feng, Wu-chun Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Virginia Tech Dept Elect & Comp Engn Blacksburg VA 24061 USA Virginia Tech Dept Comp Sci & Elect & Comp Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781538655559

This paper makes a case for using low-power embedded GPUs for the purpose of executing high-performance scientific visualization tasks. We compare the greenness (i.e., power, energy, and energy-delay product -> EDP) of an embedded GPU with a CPU for commonly encountered visualization tasks using two real-world applications: (1) Modeling for Prediction Across Scale Ocean (MPAS-O) and (2) Particular Ensembles (PE). Our preliminary results show that the low-power embedded GPU is capable of handling complex visualization tasks while consuming less than 50% of the energy consumed by a CPU server. In addition, we find that the embedded GPU outperforms the CPU with dynamic voltage-frequency scaling (DVFS) enabled in a majority of the cases.

关键词： graphics processing units Data visualization Task analysis Central Processing Unit Pipelines"

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Comparing Binary-Swap Algorithms for Odd Factors of Processes 8

Comparing Binary-Swap Algorithms for Odd Factors of Processe...

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8th ieee symposium on Large-Scale Data Analysis and visualization (LDAV)

作者： Moreland, Kenneth Sandia Natl Labs Livermore CA 94550 USA

ISBN: (纸本)9781538668733

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.

关键词： Computing methodologies Computer graphics Rendering Computing methodologies parallel computing methodologies parallel algorithms Massively parallel algorithms

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A Proposed Framework for Interactive Virtual Reality In Situ visualization of parallel Numerical Simulations

A Proposed Framework for Interactive Virtual Reality In Situ...

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ieee symposium on Large Data Analysis and visualization (LDAV)

作者： Aryaman Gupta Ulrik Günther Pietro Incardona Ata Deniz Aydin Raimund Dachselt Stefan Gumhold Ivo F. Sbalzarini Technische Universität Dresden & Center for Systems Biology Dresden (CSBD) & MPI-CBG Dresden Technische Universität Dresden Interactive Media Lab Dresden Cluster of Excellence Physics of Life Technische Universität Dresden Cluster of Excellence Physics of Life Technische Universität Dresden CSBD & MPI-CBG Dresden Cluster of Excellence Physics of Life

As computer simulations progress to increasingly complex, non-linear, and three-dimensional systems and phenomena, intuitive and immediate visualization of their results is becoming crucial. While Virtual Reality (VR) and Natural User Interfaces (NUIs) have been shown to improve understanding of complex 3D data, their application to live in situ visualization and computational steering is hampered by performance requirements. Here, we present the design of a software framework for interactive VR in situ visualization of parallel numerical simulations, as well as a working prototype implementation. Our design is targeted towards meeting the performance requirements for VR, and our work is packaged in a framework that allows for easy instrumentation of simulations. Our preliminary results inform about the technical feasibility of the architecture, as well as the challenges that remain.

关键词： Data visualization Rendering (computer graphics) Computational modeling Solid modeling Numerical models Prototypes Data models

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Galaxy: Asynchronous Ray Tracing for Large High-Fidelity visualization 8

Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Vis...

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8th ieee symposium on Large-Scale Data Analysis and visualization (LDAV)

作者： Abram, Greg Navratil, Paul Grossett, Pascal Rogers, David Ahrens, James Texas Adv Comp Ctr Austin TX 78758 USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781538668733

We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3x for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.

关键词： Human-Centered Computing visualization Computer graphics Rendering Ray Tracing

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ieee Pacific visualization symposium

IEEE Pacific Visualization Symposium

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11th ieee Pacific visualization symposium, PacificVis 2018

ISBN: (纸本)9781538614242

The proceedings contain 29 papers. The topics discussed include: optimal algorithms for compact linear layouts;development of an integrated visualization system for phenotypic character networks;image and distribution based volume rendering for large data sets;topologically controlled lossy compression;information guided data sampling and recovery using bitmap indexing;in situ prediction driven feature analysis in jet engine simulations;access pattern learning with long short-term memory for parallel particle tracing;dynamic data repartitioning for load-balanced parallel particle tracing;uncertainty visualization for secondary structures of proteins;and modeling and visualization of uncertainty-aware geometry using multi-variate normal distributions.

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SpRay: Speculative Ray Scheduling for Large Data visualization 8

SpRay: Speculative Ray Scheduling for Large Data Visualizati...

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8th ieee symposium on Large-Scale Data Analysis and visualization (LDAV)

作者： Park, Hyungman Fussell, Donald Navratil, Paul Univ Texas Austin Elect & Comp Engn Austin TX 78712 USA Univ Texas Austin Comp Sci Austin TX 78712 USA Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA

ISBN: (纸本)9781538668733

With modern supercomputers offering petascale compute capability, scientific simulations are now producing terascale data. For comprehensive understanding of such large data, ray tracing is becoming increasingly important for 3D-rendering in visualization due to its inherent ability to convey physically realistic visual information to the user. Implementing efficient parallel ray tracing systems on supercomputers while maximizing locality and parallelism is challenging because of the overhead incurred by ray communication across the cluster of compute nodes and data loading from storage. To address the problem, reordering rendering computations by means of ray batching and scheduling has been proposed to temporarily avoid inherent dependencies in the rendering computations and amortize the cost of expensive data moving operations over ray batches. In this paper, we introduce a novel speculative ray scheduling method that builds upon this insight but radically changes the approach to resolving dependencies by allowing redundant computations to a certain extent. To evaluate the method, we measure the performance of different implementations for both out-of-core and in situ rendering setups. Results show that compared to a well-known scheduling method, our approach on ambient occlusion and path tracing achieves up to 2.3x speedup for the scenes comprising up to billions of triangles extracted from terascale scientific data.

关键词： Human-Centered Computing visualization visualization Techniques Computing Methodologies Computer graphics Rendering Ray Tracing

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