SPIM Workflow Manager For HPC

Revision as of 02:45, 28 August 2018 by Jana (talk | contribs)
Revision as of 02:45, 28 August 2018 by Jana (talk | contribs)
SPIM Workflow Manager for HPC (Fiji)
Author Jan Kožusznik, Petr Bainar, Jana Klímová, Michal Krumnikl, Pavel Moravec, Václav Svatoň, Pavel Tomancak
Maintainer jan.kozusznik@vsb.cz
Source [1]
Initial release August 2018
Latest version August 2018
Category Transform, Registration, Deconvolution



Imaging techniques have emerged as a crucial means of understanding the structure and function of living organisms in primary research, as well as medical diagnostics. In order to maximize information gain, achieving as high spatial and temporal resolution as practically possible is desired. However, long-term time-lapse recordings at the single-cell level produce vast amounts of multidimensional image data, which cannot be processed on a personal computer in a timely manner, therefore requiring utilization of high-performance computing (HPC) clusters. For example, processing a 2.2 TB dataset of drosophila embryonic development, taking a week on a single computer, was brought down to 13 hours by employing an HPC cluster supporting parallel execution of individual tasks \cite{Schmied2014,Schmied2016}. Unfortunately, life scientists often lack access to such infrastructure.

Addressing this issue is particularly challenging as Fiji is an extraordinarily extensible platform and new plugins emerge incessantly. So far, plugin developers have typically implemented task parallelization within a particular plugin, but no universal approach has yet been incorporated into the SciJava architecture. In this paper we propose the concept of integrating parallelization support into one of the SciJava libraries, thereby enabling developers to access remote resources (e.g., remote HPC infrastructure) and delegate plugin-specific tasks to its compute nodes. As the cluster-specific details are hidden in respective interface implementations, the plugins can remain extensible and technology-agnostic. In addition, the proposed solution is highly scalable, meaning that any additional resources can be efficiently utilized.