The ParaView Coprocessing Library: A scalable, general purpose in situ visualization library

Fabian, Nathan D.; Moreland, Kenneth D.; Thompson, David C.; Bauer, Andrew; Marion, Pat; Gevecik, Berk; Rasquin, Michel; Jansen, Kenneth E.

doi:10.1109/ldav.2011.6092322

Cited by 189 publications

(100 citation statements)

References 16 publications

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“…Furthermore, work has been done in the broad research area of in situ visualization (e.g., the state-of-the-art analysis by Rivi et al [4]). The applications range from visualizing combustion problems (Yu et al [5]) to general procedures and software solutions (Fabian et al [6]) and extreme-scale scientific analysis (Bennett et al [7]). From reviewing these platforms and concepts, the authors decided to develop a new data structure, allowing one to solve some of the mentioned shortcomings and to provide a flexible environment for further model implementations.…”

Section: Introductionmentioning

confidence: 99%

Engineering-Based Thermal CFD Simulations on Massive Parallel Systems

et al. 2015

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The development of parallel Computational Fluid Dynamics (CFD) codes is a challenging task that entails efficient parallelization concepts and strategies in order to achieve good scalability values when running those codes on modern supercomputers with several thousands to millions of cores. In this paper, we present a hierarchical data structure for massive parallel computations that supports the coupling of a Navier-Stokes-based fluid flow code with the Boussinesq approximation in order to address complex thermal scenarios for energy-related assessments. The newly designed data structure is specifically designed with the idea of interactive data exploration and visualization during runtime of the simulation code; a major shortcoming of traditional high-performance computing (HPC) simulation codes. We further show and discuss speed-up values obtained on one of Germany's top-ranked supercomputers with up to 140,000 processes and present simulation results for different engineering-based thermal problems.

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Section: Introductionmentioning

confidence: 99%

Engineering-Based Thermal CFD Simulations on Massive Parallel Systems

et al. 2015

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“…One approach is to integrate simulation and VDA routines so that they can operate on in-memory simulation data. Examples of this approach can be found in VisIt Libsim [42], Paraview coprocessing [43], and other visualization [44] and analysis [45] systems.…”

Section: In Situ Workflowsmentioning

confidence: 99%

A characterization of workflow management systems for extreme-scale applications

Silva

Filgueira

Pietri

et al. 2017

Future Generation Computer Systems

124

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Automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies, workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today's computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. The paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems.

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“…Paraview Catalyst (also known as Paraview Coprocessing Library) [10] is one of the early attempts to visualize large scale data sets. It provides an adaptable API (application programming interface) between simulation and visualization tasks.…”

Section: Paraview Catalyst and In-situ Visualizationmentioning

confidence: 99%

A Real-Time Machine Learning and Visualization Framework for Scientific Workflows

Song

2017

Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact

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High-performance computing resources are currently widely used in science and engineering areas. Typical post-hoc approaches use persistent storage to save produced data from simulation, thus reading from storage to memory is required for data analysis tasks. For large-scale scientific simulations, such I/O operation will produce significant overhead. In-situ/in-transit approaches bypass I/O by accessing and processing in-memory simulation results directly, which suggests simulations and analysis applications should be more closely coupled. This paper constructs a flexible and extensible framework to connect scientific simulations with multi-steps machine learning processes and in-situ visualization tools, thus providing plugged-in analysis and visualization functionality over complex workflows at real time. A distributed simulation-time clustering method is proposed to detect anomalies from real turbulence flows.

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The ParaView Coprocessing Library: A scalable, general purpose in situ visualization library

Cited by 189 publications

References 16 publications

Engineering-Based Thermal CFD Simulations on Massive Parallel Systems

Engineering-Based Thermal CFD Simulations on Massive Parallel Systems

A characterization of workflow management systems for extreme-scale applications

A Real-Time Machine Learning and Visualization Framework for Scientific Workflows

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