ISAVS: Interactive scalable analysis and visualization system

Steve Petruzza, Aniketh Venkat, Attila Gyulassy, Giorgio Scorzelli, Frederick Federer, Alessandra Angelucci, Valerio Pascucci, Peer Timo Bremer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques con- tinue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy tech- niques generate larger data that are fundamental to gather biolog- ically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a sci- entific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical stream- ing data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Sec- ond, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime sys- tem automates mapping analysis algorithms to whatever compu- tational hardware is available, reducing the complexity of develop- ing scaling algorithms. We demonstrate the usability and perfor- mance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.
Original languageEnglish (US)
Title of host publicationSIGGRAPH Asia 2017 Symposium on Visualization
PublisherACM
ISBN (Print)9781450354110
DOIs
StatePublished - Nov 27 2017
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-28
Acknowledgements: This work is supported in part by NSF: CGV: Award:1314896, NSF:IIP Award :1602127 NSF:OAC Office of Advanced Cyberinfrastructure (OAC): Award 1649923, DOE/SciDAC DESC0007446, CCMSC DE-NA0002375, and PIPER: ER26142 DE-SC0010498. This material is based upon work supported by the Department of Energy, National Nuclear Security Administration, under Award Number(s) DE-NA0002375. For computer time this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. Thanks to Will Usher for the teaser image.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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