We examine the I/O behavior of thousands of supercomputing applications "in the wild," by analyzing the Darshan logs of over a million jobs representing a combined total of six years of I/O behavior across three leading high-performance computing platforms. We mined these logs to analyze the I/O behavior of applications across all their runs on a platform; the evolution of an application's I/O behavior across time, and across platforms; and the I/O behavior of a platform's entire workload. Our analysis techniques can help developers and platform owners improve I/O performance and I/O system utilization, by quickly identifying underperforming applications and offering early intervention to save system resources. We summarize our observations regarding how jobs perform I/O and the throughput they attain in practice.

Modern cosmology and plasma physics codes are now capable of simulating trillions of particles on petascale systems. Each timestep output from such simulations is on the order of 10s of TBs. Summarizing and analyzing raw particle data is challenging, and scientists often focus on density structures, whether in the real 3D space, or a high-dimensional phase space. In this work, we develop a highly scalable version of the clustering algorithm Dbscan, and apply it to the largest datasets produced by state-of-the-art codes. Our system, called Bd-Cats, is the first one capable of performing end-to-end analysis at trillion particle scale (including: loading the data, geometric partitioning, computing kd-trees, performing clustering analysis, and storing the results). We show analysis of 1.4 trillion particles from a plasma physics simulation, and a 10,2403 particle cosmological simulation, utilizing ~100,000 cores in 30 minutes. Bd-Cats is helping infer mechanisms behind particle acceleration in plasma physics and holds promise for qualitatively superior clustering in cosmology. Both of these results were previously intractable at the trillion particle scale.