Supercomputers generate vast amounts of data, typically organized into large directory hierarchies on parallel file systems. While the supercomputing applications are parallel, the tools used to process them requiring complete directory traversals, are typically serial. We present an algorithm framework and three fully distributed algorithms for traversing large parallel file systems, and performing file operations in parallel. The first algorithm introduces a randomized work-stealing scheduler; the second improves the first with proximity-awareness; and the third improves upon the second by using a hybrid approach. We have tested our implementation on Cielo, a 1.37 petaflop supercomputer at the Los Alamos National Laboratory and its 7 petabyte file system. Test results show that our algorithms execute orders of magnitude faster than state-of-the-art algorithms while achieving ideal load balancing and low communication cost. We present performance insights from the use of our algorithms in production systems at LANL, performing daily file system operations.
