Performance Analysis of Divide-and-Conquer strategies for Large scale Simulations in R

Abstract

As the volume of data and technical complexity of large-scale analysis increases, many domain experts desire powerful computational and familiar analysis interface to fully participate in the analysis workflow by just focusing on individual datasets, leaving the large-scale computation to the system. Towards this goal, we investigate and benchmark a family of Divide-and-Conquer strategies that can help domain experts perform large-scale simulations by scaling up their analysis code written in R, the most popular data science and interactive analysis language. We implement the Divide-and-Conquer strategies that use R as the analysis (and computing) language, allowing advanced users to provide custom R scripts and variables to be fully embedded into the large-scale analysis workflow in R. The whole process will divide large-scale simulations tasks and conquer tasks with Slurm array jobs and R. Simulations and final aggregations are scheduled as array jobs in parallel means to accelerate the knowledge discovery process. The objective is to provide a new analytics workflow for performing similar large-scale analysis loops where expert users only need to focus on the Divide-and-Conquer tasks with the domain knowledge.

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