Enabling In-Situ Data Analysis for Large Protein-Folding Trajectory Datasets

This paper presents a one-pass, distributed method that enables in-situ data analysis for large protein folding trajectory datasets by executing sufficiently fast, avoiding moving trajectory data, and limiting the memory usage. First, the method extracts the geometric shape features of each protein conformation in parallel. Then, it classifies sets of consecutive conformations into meta-stable and transition stages using a probabilistic hierarchical clustering method. Lastly, it rebuilds the global knowledge necessary for the intraand inter-trajectory analysis through a reduction operation. The comparison of our method with a traditional approach for a villin headpiece sub domain shows that our method generates significant improvements in execution time, memory usage, and data movement. Specifically, to analyze the same trajectory consisting of 20,000 protein conformations, our method runs in 41.5 seconds while the traditional approach takes approximately 3 hours, uses 6.9MB memory per core while the traditional method uses 16GB on one single node where the analysis is performed, and communicates only 4.4KB while the traditional method moves the entire dataset of 539MB. The overall results in this paper support our claim that our method is suitable for in-situ data analysis of folding trajectories.