HPC I/O Throughput Bottleneck Analysis with Explainable Local Models
Reading group: Mouhamadou Tidiane MANGASSOUBA presented "HPC I/O Throughput Bottleneck Analysis with Explainable Local Models" (SC'20) at 4A312 the 5/11/2021 at 10h30.
With the growing complexity of high-performance computing (HPC) systems, achieving high performance can be difficult because of I/O bottlenecks. We analyze multiple years’ worth of Darshan logs from the Argonne Leadership Computing Facility’s Theta supercomputer in order to understand causes of poor I/O throughput. We present Gauge: a data-driven diagnostic tool for exploring the latent space of supercomputing job features, understanding behaviors of clusters of jobs, and interpreting I/O bottlenecks. We find groups of jobs that at first sight are highly heterogeneous but share certain behaviors, and analyze these groups instead of individual jobs, allowing us to reduce the workload of domain experts and automate I/O performance analysis. We conduct a case study where a system owner using Gauge was able to arrive at several clusters that do not conform to conventional I/O behaviors, as well as find several potential improvements, both on the application level and the system level.