Lawrence Berkeley National Laboratory

The complex software and hardware I/O stack of HPC platforms makes it challenging for end-users to obtain superior I/O performance and to understand the root causes of I/O bottlenecks they encounter. Despite the continuous efforts from the community to profile I/O performance and propose new optimization techniques and tuning options for improving the performance, there is still a translation gap between profiling and tuning. In this paper, we propose Drishti, a solution to guide scientists in optimizing I/O in their applications by detecting typical I/O performance pitfalls and providing recommendations. We illustrate its applicability in two case studies and evaluate its robustness and performance by summarizing the issues detected in over a hundred thousand Darshan logs collected on the Cori supercomputer at the National Energy Research Scientific Computing Center (NERSC). Drishti can empower end-users and guide them in the I/O optimization journey by shedding some light on everyday I/O performance pitfalls and how to fix them.