This article is part of our series: A day in the software life, in which we will be asking researchers from all disciplines to discuss the tools that make their research possible.
A large amount of today’s computational and data science involves combining the execution of many tasks that each run a model or transform data, to build up a multi-component model or a multi-stage data transformation. Most researchers initially do this manually, and then (if they have any programming experience) eventually move to using shell scripts when the manual process gets too painful. However, shell scripts tend to limit the work to single resources, as they don’t really work well with parallel computing.
An alternative method is to use a system that I’ve been involved in developing: the Swift Parallel Scripting Language. (Note that there’s no relation here to Apple’s Swift, other than that they reused our name.) Swift provides an implicitly parallel and deterministic programming model, which applies…Continue Reading