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The transition from petascale to exascale computers is characterized bysubstantial changes in the computer architectures and technologies. Theresearch community relying on computational simulations is being forced torevisit the algorithms for data generation and analysis due to variousconcerns, such as higher degrees of concurrency, deeper memory hierarchies,substantial I/O and communication constraints. Simulations today typically saveall data to analyze later. Simulations at the exascale will require us toanalyze data as it is generated and save only what is really needed foranalysis, which must be performed predominately in-situ, i.e., executedsufficiently fast locally, limiting memory and disk usage, and avoiding theneed to move large data across nodes.  In this paper, we present a distributed method that enables in-situ dataanalysis for large protein folding trajectory datasets. Traditional trajectoryanalysis methods currently follow a centralized approach that moves thetrajectory datasets to a centralized node and processes the data only aftersimulations have been completed. Our method, on the other hand, capturesconformational information in-situ using local data only while reducing thestorage space needed for the part of the trajectory under consideration. Thismethod processes the input trajectory data in one pass, breaks from thecentralized approach of traditional analysis, avoids the movement of trajectorydata, and still builds the global knowledge on the formation of individual$\alpha$-helices or $\beta$-strands as trajectory frames are generated.

In-Situ Data Analysis of Protein Folding Trajectories