Characterization of Large-scale HPC Workloads with non-naïve I/O Roofline Modeling and Scoring
Zhao-Bin Zhu, Sarah Neuwirth · 2023
This paper introduces a novel approach to characterize system and application performance in high performance computing (HPC) systems. Traditional metrics such as computations and memory accesses alone are no longer sufficient to evaluate the performance of such systems. To address this challenge, an empirical I/O Roofline model and corresponding workload analysis workflow are proposed that can be adapted in the future to enable multidimensional evaluation for application performance characterization across different HPC systems. The model focuses on commonly used performance metrics such as I/O operations per second (IOPS) and I/O bandwidth, and leverages the well-known Roofline modeling technique to intuitively characterize I/O performance and identify performance bottlenecks without requiring deep knowledge of the I/O stack. Furthermore, based on the I/O Roofline model, a scoring approach is described that provides a unified and comprehensible method for evaluating the performance of different systems and applications. The effectiveness of the approach is demonstrated by evaluating the performance of various application I/O kernels using the empirical Roofline model.