ParaView Catalyst

Ayachit, Utkarsh; Bauer, Andrew; Geveci, Berk; O’Leary, Patrick; Moreland, Kenneth D.; Fabian, Nathan D.; Mauldin, Jeffrey A.

doi:10.1145/2828612.2828624

Cited by 226 publications

(93 citation statements)

References 10 publications

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“…Well-known frameworks focused on visualization include ParaView Catalyst [14], Strawman [79], VisIt LibSim [153] and Damaris/Viz [43]. Besides visualization capabilities, some frameworks offer computational steering too.…”

Section: Framework and Infrastructurementioning

confidence: 99%

State of the Art and Future Trends in Data Reduction for High-Performance Computing

2020

JSFI

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Research into data reduction techniques has gained popularity in recent years as storage capacity and performance become a growing concern. This survey paper provides an overview of leveraging points found in high-performance computing (HPC) systems and suitable mechanisms to reduce data volumes. We present the underlying theories and their application throughout the HPC stack and also discuss related hardware acceleration and reduction approaches. After introducing relevant use-cases, an overview of modern lossless and lossy compression algorithms and their respective usage at the application and file system layer is given. In anticipation of their increasing relevance for adaptive and in situ approaches, dimensionality reduction techniques are summarized with a focus on non-linear feature extraction. Adaptive approaches and in situ compression algorithms and frameworks follow. The key stages and new opportunities to deduplication are covered next. An unconventional but promising method is recomputation, which is proposed at last. We conclude the survey with an outlook on future developments.

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Section: Framework and Infrastructurementioning

confidence: 99%

State of the Art and Future Trends in Data Reduction for High-Performance Computing

2020

JSFI

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“…Coupled applications that only act as either a data sink or a source for the main application are also major beneficiaries of burst buffers. A prominent example in HPC are in situ visualizations copying on demand snapshots [13,14] or accessing the primary observables directly [15,16,17].…”

Section: Staging Burst Buffers and I/o Backlogmentioning

confidence: 99%

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective

Huebl

Widera

Schmitt

et al. 2017

Lecture Notes in Computer Science

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We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as a major challenge for applications on today's and future HPC systems, we present a scaling law characterizing performance bottlenecks in state-of-the-art approaches for data reduction. Consequently, we propose, implement and verify multi-threaded data-transformations for the I/O library ADIOS as a feasible way to trade underutilized host-side compute potential on heterogeneous systems for reduced I/O latency.

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“…There are tools available on Paraview [14], a popular visualization framework, which can be used for ABMs. Paraview Catalyst [4, 6] was developed to process simulation output data in-situ according to the user’s co-processing script. An image-based approach built on top of Paraview Catalyst was presented in [2] to efficiently manage rendered images created in-situ by Paraview Catalyst.…”

Section: Background and Related Workmentioning

confidence: 99%

“…An image-based approach built on top of Paraview Catalyst was presented in [2] to efficiently manage rendered images created in-situ by Paraview Catalyst. As much as all these works [2, 4, 6, 17, 35] reduce I/O loads, none completely by-passes I/O or can be used to achieve anything close to the desired performance for our problem.…”

Section: Background and Related Workmentioning

confidence: 99%

In-Situ Visualization for 3D Agent-Based Vocal Fold Inflammation and Repair Simulation

Seekhao

JáJá

Mongeau

et al. 2017

JSFI

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A fast and insightful visualization is essential in modeling biological system behaviors and understanding underlying inter-cellular mechanisms. High fidelity models produce billions of data points per time step, making in situ visualization techniques extremely desirable as they mitigate I/O bottlenecks and provide computational steering capability. In this work, we present a novel high-performance scheme to couple in situ visualization with the simulation of the vocal fold inflammation and repair using little to no extra cost in execution time or computing resources. The visualization component is first optimized with an adaptive sampling scheme to accelerate the rendering process while maintaining the precision of the displayed visual results. Our software employs VirtualGL to perform visualization in situ. The scheme overlaps visualization and simulation, resulting in the optimal utilization of computing resources. This results in an in situ system biology simulation suite capable of remote simulation of 17 million biological cells and 1.2 billion chemical data points, remote visualization of the results, and delivery of visualized frames with aggregated statistics to remote clients in real-time.

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ParaView Catalyst

Cited by 226 publications

References 10 publications

State of the Art and Future Trends in Data Reduction for High-Performance Computing

State of the Art and Future Trends in Data Reduction for High-Performance Computing

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective

In-Situ Visualization for 3D Agent-Based Vocal Fold Inflammation and Repair Simulation

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