Performance Modeling of In Situ Rendering

Larsen, Matthew C.; Harrison, Cyrus; Kress, James; Pugmire, David; Meredith, Jeremy; Childs, Hank

doi:10.1109/sc.2016.23

Cited by 20 publications

(6 citation statements)

References 42 publications

(25 reference statements)

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“…Similarly, for the data movements between components, users can specify an element size and a data transfer scaling factor to artificially change the transferred data size (i.e., simulate subset, transform, or derived output types). This workload parameter can also derive from a performance model [20].…”

Section: Sim-situ Architecturementioning

confidence: 99%

SIM-SITU: A Framework for the Faithful Simulation of in situ Processing

Honoré

Anh²,

Pottier³

et al. 2022

2022 IEEE 18th International Conference on E-Science (E-Science)

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The amount of data generated by numerical simulations in various scientific domains led to a fundamental redesign of how the analysis and visualization of simulation outputs are performed. The throughput and capacity of storage subsystems have not evolved as fast as the computing power in extreme-scale supercomputers, making the classical post-hoc approach highly inefficient. In situ processing has then emerged as a solution in which simulation and data analysis/visualization are intertwined for better performance and greater interactivity.Determining the best allocation, i.e., how many resources to allocate to simulation and analysis respectively, mapping, i.e., where and at which frequency to run the analysis/visualization, and data transfer mode is a complex task whose performance assessment is crucial to the efficient execution of in situ processing. However, such a performance evaluation of different strategies usually relies either on directly running them on the targeted execution environments, which can rapidly become extremely time-and resource-consuming, or on resorting to simplified models of the components of an in situ application, which can lack of realism. In both cases, the validity of the performance evaluation is limited.In this paper, we present SIM-SITU, a framework for the faithful performance evaluation of in situ processing strategies. We designed SIM-SITU to reflect the typical features of in situ processing systems. Thanks to its modular design, SIM-SITU has the necessary flexibility to easily and faithfully evaluate the behavior and performance of various allocation, mapping, and data transfer strategies. We illustrate the capabilities of SIM-SITU on a Molecular Dynamics use case. We study the impact of different strategies on performance and show how users can leverage SIM-SITU to determine interesting tradeoffs when adding analysis/visualization components to their application.

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Section: Sim-situ Architecturementioning

confidence: 99%

SIM-SITU: A Framework for the Faithful Simulation of in situ Processing

Honoré

Anh²,

Pottier³

et al. 2022

2022 IEEE 18th International Conference on E-Science (E-Science)

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“…Therefore, ML should understand the design space of in-situ workflows and decide accordingly. For example, the performance model of algorithms for analysis and visualization can be provided by ML through their characteristics [149]. This is useful to estimate the runtime for each task and decide to process them in-situ or postpone for post-hoc.…”

Section: In-situ Workflowsmentioning

confidence: 99%

Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges

Nouri¹,

Davis²,

Subedi³

et al. 2021

Preprint

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In this survey, we discuss the challenges of executing scientific workflows as well as existing Machine Learning (ML) techniques to alleviate those challenges. We provide the context and motivation for applying ML to each step of the execution of these workflows. Furthermore, we provide recommendations on how to extend ML techniques to unresolved challenges in the execution of scientific workflows. Moreover, we discuss the possibility of using ML techniques for in-situ operations. We explore the challenges of in-situ workflows and provide suggestions for improving the performance of their execution using ML techniques.

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“…User-centered design is potentially even more important in the field of in situ visualization. The evaluation of the effectiveness of an in situ visualization technique has traditionally been focused on algorithmic performance, as researchers are developing new performance models specifically for in situ visualization and analysis [8,37]. A complete solution of in situ visualization often involves two parts.…”

Section: Related Workmentioning

confidence: 99%

A User-Centered Design Study in Scientific Visualization Targeting Domain Experts

Sauer

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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The development and design of visualization solutions that are truly usable is essential for ensuring both their adoption and effectiveness. User-centered design principles, which focus on involving users throughout the entire development process, are well suited for visualization and have been shown to be effective in numerous information visualization endeavors. In this paper, we report a two year long collaboration with combustion scientists that, by applying these design principles, generated multiple results including an in situ visualization technique and a post hoc probability distribution function (PDF) exploration tool. Furthermore, we examine the importance of user-centered design principles and describe lessons learned over the design process in an effort to aid others who also seek to work with scientists for developing effective and usable scientific visualization solutions.

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Performance Modeling of In Situ Rendering

Cited by 20 publications

References 42 publications

SIM-SITU: A Framework for the Faithful Simulation of in situ Processing

SIM-SITU: A Framework for the Faithful Simulation of in situ Processing

Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges

A User-Centered Design Study in Scientific Visualization Targeting Domain Experts

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