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egpgv 2021 - 21st eurographics symposium on parallel graphics and visualization

EGPGV 2021 - 21st Eurographics Symposium on Parallel Graphic...

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21st eurographics symposium on parallel graphics and visualization, egpgv 2021

ISBN: (纸本)9783038681380

The proceedings contain 8 papers. The topics discussed include: HyLiPoD: parallel particle advection via a hybrid of lifeline scheduling and parallelization-over-data;machine learning-based autotuning for parallel particle advection;scalable in situ computation of Lagrangian representations via local flow maps;evaluation of PyTorch as a data-parallel programming API for GPU volume rendering;faster RTX-accelerated empty space skipping using triangulated active region boundary geometry;performance tradeoffs in shared-memory platform portable implementations of a stencil kernel;UnityPIC: unity point-cloud interactive core;and interactive selection on calculated attributes of large-scale particle data.

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HyLiPoD: parallel Particle Advection Via a Hybrid of Lifeline Scheduling and parallelization-Over-Data

HyLiPoD: Parallel Particle Advection Via a Hybrid of Lifelin...

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21st eurographics symposium on parallel graphics and visualization, egpgv 2021

作者： Binyahib, Roba Pugmire, David Childs, Hank National Renewable Energy Laboratory United States Oak Ridge National Laboratory United States University of Oregon United States

ISBN: (纸本)9783038681380

Performance characteristics of parallel particle advection algorithms can vary greatly based on workload. With this short paper, we build a new algorithm based on results from a previous bake-off study which evaluated the performance of four algorithms on a variety of workloads. Our algorithm, called HyLiPoD, is a "meta-algorithm," i.e., it considers the desired workload to choose from existing algorithms to maximize performance. To demonstrate HyliPoD’s benefit, we analyze results from 162 tests including concurrencies of up to 8192 cores, meshes as large as 34 billion cells, and particle counts as large as 300 million. Our findings demonstrate that HyLiPoD’s adaptive approach allows it to match the best performance of existing algorithms across diverse workloads. © 2021 The Author(s).

关键词： Data visualization

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Performance Tradeoffs in Shared-memory Platform Portable Implementations of a stencil Kernel

Performance Tradeoffs in Shared-memory Platform Portable Imp...

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21st eurographics symposium on parallel graphics and visualization, egpgv 2021

作者： Bethel, E. Wes Heinemann, Colleen Perciano, Talita Lawrence Berkeley National Laboratory BerkeleyCA United States San Francisco State University San FranciscoCA United States University of Illinois Urbana-ChampaignIL United States

ISBN: (纸本)9783038681380

Building on a significant amount of current research that examines the idea of platform-portable parallel code across different types of processor families, this work focuses on two sets of related questions. First, using a performance analysis methodology that leverages multiple metrics including hardware performance counters and elapsed time on both CPU and GPU platforms, we examine the performance differences that arise when using two common platform portable parallel programming approaches, namely OpenMP and VTK-m, for a stencil-based computation, which serves as a proxy for many different types of computations in visualization and analytics. Second, we explore the performance differences that result when using coarser- and finer-grained parallelism approaches that are afforded by both OpenMP and VTK-m. © 2021 The Author(s).

关键词： parallel programming

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Interactive Selection on Calculated Attributes of Large-Scale Particle Data

Interactive Selection on Calculated Attributes of Large-Scal...

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21st eurographics symposium on parallel graphics and visualization, egpgv 2021

作者： Wollet, B. Reinhardt, S. Weiskopf, D. Eberhardt, B. University of Stuttgart Germany Hochschule der Medien Germany

ISBN: (纸本)9783038681380

We present a GPU-based technique for efficient selection in interactive visualizations of large particle datasets. In particular, we address multiple attributes attached to particles, such as pressure, density, or surface tension. Unfortunately, such intermediate attributes are often available only during the simulation run. They are either not accessible during visualization or have to be saved as additional information along with the usual simulation data. The latter increases the size of the dataset significantly, and the required variables may not be known in advance. Therefore, we choose to compute intermediate attributes on the fly. In this way, we are even able to obtain attributes that were not calculated by the simulation but may be relevant for data analysis or debugging. We present an interactive selection technique designed for such attributes. It leverages spatial regions of the selection to efficiently compute attributes only where needed. This lazy evaluation also works for intelligent and data-driven selection, extending the region to include neighboring particles. Our technique is evaluated by measurements of performance scalability and case studies for typical usage examples. © 2021 The Author(s).

关键词： visualization

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Evaluation of PyTorch as a Data-parallel Programming API for GPU Volume Rendering

Evaluation of PyTorch as a Data-Parallel Programming API for...

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21st eurographics symposium on parallel graphics and visualization, egpgv 2021

作者： Marshak, N.X. Grosset, A.V.P. Knoll, A. Ahrens, J. Johnson, C.R. SCI Institute University of Utah United States Los Alamos National Laboratory United States Intel Corporation United States

ISBN: (纸本)9783038681380

Data-parallel programming (DPP) has attracted considerable interest from the visualization community, fostering major software initiatives such as VTK-m. However, there has been relatively little recent investigation of data-parallel APIs in higher-level languages such as Python, which could help developers sidestep the need for low-level application programming in C++ and CUDA. Moreover, machine learning frameworks exposing data-parallel primitives, such as PyTorch and TensorFlow, have exploded in popularity, making them attractive platforms for parallel visualization and data analysis. In this work, we benchmark data-parallel primitives in PyTorch, and investigate its application to GPU volume rendering using two distinct DPP formulations: a parallel scan and reduce over the entire volume, and repeated application of data-parallel operators to an array of rays. We find that most relevant DPP primitives exhibit performance similar to a native CUDA library. However, our volume rendering implementation reveals that PyTorch is limited in expressiveness when compared to other DPP APIs. Furthermore, while render times are sufficient for an early "proof of concept", memory usage acutely limits scalability. © 2021 The Author(s).

关键词： Volume rendering

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Machine Learning-Based Autotuning for parallel Particle Advection

Machine Learning-Based Autotuning for Parallel Particle Adve...

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21st eurographics symposium on parallel graphics and visualization, egpgv 2021

作者： Schwartz, Samuel D. Childs, Hank Pugmire, David Department of Computer and Information Science University of Oregon EugeneOR United States Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9783038681380

Data-parallel particle advection algorithms contain multiple controls that affect their execution characteristics and performance, in particular how often to communicate and how much work to perform between communications. Unfortunately, the optimal settings for these controls vary based on workload, and, further, it is not easy to devise straight-forward heuristics that automate calculation of these settings. To solve this problem, we investigate a machine learning-based autotuning approach for optimizing data-parallel particle advection. During a pre-processing step, we train multiple machine learning techniques using a corpus of performance data that includes results across a variety of workloads and control settings. The best performing of these techniques is then used to form an oracle, i.e., a module that can determine good algorithm control settings for a given workload immediately before execution begins. To evaluate this approach, we assessed the ability of seven machine learning models to capture particle advection performance behavior and then ran experiments for 108 particle advection workloads on 64 GPUs of a supercomputer. Our findings show that our machine learning-based oracle achieves good speedups relative to the available gains. © 2021 The Author(s).

关键词： Machine learning

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Variable length coding for GPU-based direct volume rendering 21

Variable length coding for GPU-based direct volume rendering

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21st International symposium on Vision, Modeling and visualization, VMV 2016

作者： Guthe, S. Goesele, M. Graphics Capture and Massively Parallel Computing TU Darmstadt Germany

ISBN: (纸本)9783038680253

The sheer size of volume data sampled in a regular grid requires efficient lossless and lossy compression algorithms that allow for on-the-fly decompression during rendering. While all hardware assisted approaches are based on fixed bit rate block truncation coding, they suffer from degradation in regions of high variation while wasting space in homogeneous areas. On the other hand, vector quantization approaches using texture hardware achieve an even distribution of error in the entire volume at the cost of storing overlapping blocks or bricks. However, these approaches suffer from severe blocking artifacts that need to be smoothed over during rendering. In contrast to existing approaches, we propose to build a lossy compression scheme on top of a state-of-the-art lossless compression approach built on non-overlapping bricks by combining it with straight forward vector quantization. Due to efficient caching and load balancing, the rendering performance of our approach improves with the compression rate and can achieve interactive to real-time frame rates even at full HD resolution. © 2016 The Author(s) eurographics Proceedings © 2016 The eurographics Association.

关键词： Brick

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