HPC Data Analysis Pipeline for Neuronal Cluster Detection

BSTRACT Obtaining neural clusters from data sets collected over different developmental stages poses a computational challenge that is complicated by the number of data sets, clustering methods, and hyperparameters. We used MATLAB parallel toolkit to parallelize the execution of the hyperparameter sweeps as well as developed a workflow for parallelizing the data processing. We present a run-time performance comparison of the workflow for two clustering methods on Stampede2 supercomputer. Our study explored the performance of MATLAB implementations of the K-means and Louvain algorithms for cluster detection, using covariance and cosine similarity matrices, and investigated hyperparameter settings for each algorithm.