Biblio

In high-performance computing (HPC), scientific applications often manage a massive amount of data using I/O libraries. These libraries provide convenient data model abstractions, help ensure data portability, and, most important, empower end users to improve I/O performance by tuning configurations across multiple layers of the HPC I/O stack. We propose SCTuner, an autotuner integrated within the I/O library itself to dynamically tune both the I/O library and the underlying I/O stack at application runtime. To this end, we introduce a statistical benchmarking method to profile the behaviors of individual supercomputer I/O subsystems with varied configurations across I/O layers. We use the benchmarking results as the built-in knowledge in SCTuner, implement an I/O pattern extractor, and plan to implement an online performance tuner as the SCTuner runtime. We conducted a benchmarking analysis on the Summit supercomputer and its GPFS file system Alpine. The preliminary results show that our method can effectively extract the consistent I/O behaviors of the target system under production load, building the base for I/O autotuning at application runtime.