Scaling big data neuroscience: From interactive analytics to HPC platforms

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


High-throughput microscopy techniques generate an ever growing amount of data that are fundamental to gather scientific, biologically and medically relevant insights. This growing amount of data dramatically affects the scientific workflow at every step. Visualization and analysis tasks are performed with limited interactivity and the implementations often require HPC skills and lack of portability, usability and maintainability. In this work we explore a software infrastructure that simplifies end-to-end visualization and analysis of massive data. Data management and movement is performed using a hierarchical streaming data access layer which enable interactive exploration of remote data. The analysis tasks are expressed and performed using a library for rapid prototyping of algorithms using an Embedded Domain Specific Language which enables portable deployment in both desktop and HPC environments. Finally, we use a scalable runtime system (Charm++) to automate the mapping of the analysis algorithm to the computational resources available, reducing the complexity of developing scaling algorithms. We present large scale experimentations using tera-scale microscopy data executing some of the most common neuroscience use cases: data filtering, visualization using two different image compositing algorithms, and image registration.
Original languageEnglish (US)
Pages (from-to)53-68
Number of pages16
JournalAdvances in Parallel Computing
StatePublished - Jan 1 2018
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-10
Acknowledgements: This work is supported in part by NSF: CGV: Award:1314896, NSF:IIP Award:1602127 NSF:OAC O ce 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 Super- computing Laboratory at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Computer Science(all)


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