Investigating In Situ Reduction via Lagrangian Representations for Cosmology and Seismology Applications

Sane, Sudhanshu; Johnson, Chris R.; Childs, Hank

doi:10.1007/978-3-030-77961-0_36

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…To further optimize the accuracy of reconstructed pathlines in settings of temporal sparsity, research has considered how varying the set of precomputed trajectories can improve performance and accuracy‐storage propositions. For example, the use of adaptive sampling [BT13], longer precomputed trajectories [SCB19], statistical sampling [RPD19], rank‐specific local flow maps [SYB * 21], and improved search structures for interpolation [COJ15] have been studied. Unstructured sampling strategies, however, could increase the cost when using standard post hoc interpolation techniques and diminish computational performance benefits.…”

Section: Algorithmic Optimizationsmentioning

confidence: 99%

State‐of‐the‐Art Report on Optimizing Particle Advection Performance

Yenpure

Sane²,

Binyahib

et al. 2023

Computer Graphics Forum

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The computational work to perform particle advection‐based flow visualization techniques varies based on many factors, including number of particles, duration, and mesh type. In many cases, the total work is significant, and total execution time (“performance”) is a critical issue. This state‐of‐the‐art report considers existing optimizations for particle advection, using two high‐level categories: algorithmic optimizations and hardware efficiency. The sub‐categories for algorithmic optimizations include solvers, cell locators, I/O efficiency, and precomputation, while the sub‐categories for hardware efficiency all involve parallelism: shared‐memory, distributed‐memory, and hybrid. Finally, this STAR concludes by identifying current gaps in our understanding of particle advection performance and its optimizations.

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Section: Algorithmic Optimizationsmentioning

confidence: 99%

State‐of‐the‐Art Report on Optimizing Particle Advection Performance

Yenpure

Sane²,

Binyahib

et al. 2023

Computer Graphics Forum

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“…By utilizing in situ processing, Lagrangian representations are computed using the complete spatial and temporal resolution of the simulation data. Studies have demonstrated reduced Lagrangian representations offer strong accuracy-storage propositions for exploration in temporally sparse settings (Agranovsky et al, 2014;Rapp et al, 2019;Sane et al, 2021a) as well as directly support feature extraction (Froyland and Junge, 2018;Froyland and Padberg-Gehle, 2015;Hadjighasem et al, 2017;Jakob et al, 2020;Schlueter-Kuck and Dabiri, 2017). Additionally, previous research has demonstrated the traditional Eulerian paradigm performs poorly in under-resolved temporal settings (Agranovsky et al, 2014;Da Costa and Blanke, 2004;Qin et al, 2014;Rockwood et al, 2019;Sane et al, 2018Sane et al, , 2021a.…”

Section: Lagragian Analysismentioning

confidence: 99%

“…Research related to reduced Lagrangian representations that enable time-varying vector fields has advanced along multiple axes. These include in situ sampling techniques (Agranovsky et al, 2014;Rapp et al, 2019;Sane et al, 2019Sane et al, , 2021b, post hoc reconstruction strategies (Agranovsky et al, 2015;Bujack and Joy, 2015;Chandler et al, 2014;Hlawatsch et al, 2010), theoretical and empirical error analysis (Chandler et al, 2016;Hummel et al, 2016;Sane et al, 2018), feature extraction (Froyland and Junge, 2018;Froyland and Padberg-Gehle, 2015;Hadjighasem et al, 2017;Jakob et al, 2020;Schlueter-Kuck and Dabiri, 2017), and application to various domains (Nardini et al, 2017;Sane et al, 2021a;Siegfried et al, 2019). In this paper, we study the use of deep learning to perform post hoc reconstruction.…”

Section: Lagragian Analysismentioning

confidence: 99%

“…A potential solution to perform accurate time-varying flow visualization is to consider a Lagrangian representation of the vector field. Lagrangian representations have been demonstrated to offer strong accuracy-storage propositions compared to traditional techniques (Agranovsky et al, 2014;Sane et al, 2021a). The approach involves two phases: in situ and post hoc.…”

Section: Introductionmentioning

confidence: 99%

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Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

Han

Sane

Johnson

2022

J Flow Vis Image Proc

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Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration. To address these challenges, reduced Lagrangian representations have been increasingly researched as a means to improve scientific time-varying vector field exploration capabilities. This paper presents a novel deep neural network based particle tracing method to explore time-varying vector fields represented by Lagrangian flow maps. In our workflow, in situ processing is first utilized to extract Lagrangian flow maps, and deep neural networks then use the extracted data to learn flow field behavior. Using a trained model to predict new particle trajectories offers a fixed-small memory footprint and fast inference. To demonstrate and evaluate the proposed method, we perform an in-depth study of performance using a well-known analytical data set, the double gyre. Our study considers two flow map extraction strategies as well as the impact of the number of training samples and integration durations on efficacy, evaluates multiple sampling options for training and testing, and informs hyperparameter settings. Overall, we find our method requires a small fixed-memory footprint of 10.5 MB to encode a Lagrangian representation of a time-varying vector field while maintaining accuracy. For post hoc analysis, loading the trained model costs only two seconds, significantly reducing the burden of I/O when reading data for visualization. Moreover, our parallel implementation can infer one hundred locations for each of two thousand new pathlines across the entire temporal resolution in 1.3 seconds using one NVIDIA Titan RTX GPU.

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“…Lagrangian Basis Flows By integrating VTK-m's particle advection code, the VTK-m and ALPINE teams demonstrated high performance with significant data reduction by computing lagrangian basis flows for subsequent flow visualization [213,214].…”

Section: Recent Progressmentioning

confidence: 99%

ECP Software Technology Capability Assessment Report V3.0

Heroux

McInnes

et al. 2022

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The Exascale Computing Project (ECP) Software Technology (ST) focus area is responsible for (1) developing critical software capabilities that will enable the successful execution of ECP applications and (2) providing key components of a productive and sustainable exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high-performance computing (HPC) community with a firm foundation for future extreme-scale computing capabilities.This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every 6-12 months). Highlights from this version of the report are presented here.This version of the CAR contains the following updates relative to the previous revision.• This report highlights the progress with the Extreme-scale Scientific Software Stack (E4S) efforts.In particular, this report discusses how E4S continues to gain traction as a first-class entity in the HPC ecosystem, enabling new conversations with users, facilities, vendors, other US agencies, and international partners.• The several-page summaries of each ECP Level 4 project were updated to reflect recent progress and next steps (Section 4). Of particular note are the experiences of our teams on early-access systems for Frontier.• The E4S is described further. E4S is now updated via quarterly releases. E4S is the primary integration and delivery vehicle for ECP ST capabilities (Section 2.1.1).• The ECP ST software development kit (SDK) effort further refined its groupings (Section 2.1.2).The ECP ST focus area represents the key bridge between exascale systems and the scientists developing applications that will run on those platforms. ECP ST efforts contribute to approximately 70 software products (Section 2.1.3) in six technical areas (Table 1). Since publishing the previous revision of the CAR, the team has continued to evolve the product dictionary of official product names, which enables more rigorous mapping of ECP ST deliverables to stakeholders (Section 2.1.4).Programming Models & Runtimes: In addition to developing key enhancements to MPI and OpenMP for scalable systems with accelerated node architectures, the team is working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and OpenACC software design and development that will enable applications to write much of their source code without needing to provide vendor-specific implementations for each exascale system. One legacy of ECP ST efforts is anticipated to be a software stack that supports Intel and AMD accelerators in addition to NVIDIA's accelerators (Section 4.1).Development Tools: The team is enhancing existing widely used compilers (e.g., LLVM) and performance tools for next-generation platforms. Compilers are critical for heterogeneous archi...

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Investigating In Situ Reduction via Lagrangian Representations for Cosmology and Seismology Applications

Cited by 6 publications

References 19 publications

State‐of‐the‐Art Report on Optimizing Particle Advection Performance

State‐of‐the‐Art Report on Optimizing Particle Advection Performance

Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

ECP Software Technology Capability Assessment Report V3.0

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