To the Cloud! A Grassroots Proposal to Accelerate Brain Science Discovery (original) (raw)

Neuroscience Cloud Analysis As a Service

2020

A major goal of computational neuroscience is the development of powerful data analyses that operate on large datasets. These analyses form an essential toolset to derive scientific insights from new experiments. Unfortunately, a major obstacle currently impedes progress: novel data analyses have a hidden dependence upon complex computing infrastructure (e.g. software dependencies, hardware), acting as an unaddressed deterrent to potential analysis users. While existing analyses are increasingly shared as open source software, the infrastructure needed to deploy these analyses – at scale, reproducibly, cheaply, and quickly – remains totally inaccessible to all but a minority of expert users. In this work we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated analysis platform that makes state-of-the-art data analysis tools accessible to the neuroscience community. Based on modern large-scale computing advances, NeuroCAAS is an open source platform with a ...

Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing

Magnetic Resonance Insights, 2016

With larger data sets and more sophisticated analyses, it is becoming increasingly common for neuroimaging researchers to push (or exceed) the limitations of standalone computer workstations. Nonetheless, although high-performance computing platforms such as clusters, grids and clouds are already in routine use by a small handful of neuroimaging researchers to increase their storage and/or computational power, the adoption of such resources by the broader neuroimaging community remains relatively uncommon. Therefore, the goal of the current manuscript is to: 1) inform prospective users about the similarities and differences between computing clusters, grids and clouds; 2) highlight their main advantages; 3) discuss when it may (and may not) be advisable to use them; 4) review some of their potential problems and barriers to access; and finally 5) give a few practical suggestions for how interested new users can start analyzing their neuroimaging data using cloud resources. Although ...