Understanding HPC Application I/O Behavior Using System Level Statistics

The processor performance of high performance computing (HPC) systems is increasing at a much higher rate than storage performance. This imbalance leads to I/O performance bottlenecks in massively parallel HPC applications. Therefore, there is a need for improvements in storage and file system designs to meet the ever-growing I/O needs of HPC applications. Storage and file system designers require a deep understanding of how HPC application I/O behavior affects current storage system installations in order to improve them. In this work, we contribute to this understanding using application-agnostic file system statistics gathered on compute nodes as well as metadata and object storage file system servers. We analyze file system statistics of more than 4 million jobs over a period of three years on two systems at Lawrence Livermore National Laboratory that include a 15 PiB Lustre file system for storage. The results of our study add to the state-of-the-art in I/O understanding by providing insight into how general HPC workloads affect the performance of large-scale storage systems. Some key observations in our study show that reads and writes are evenly distributed across the storage system; applications which perform I/O, spread that I/O across similar to 78% of the minutes of their runtime on average; less than 22% of HPC users who submit write-intensive jobs perform efficient writes to the file system; and I/O contention seriously impacts I/O performance.