Quantifying architectural requirements of contemporary extreme-scale scientific applications

Abstract

As detailed in recent reports, HPC architectures will continue to change over the next decade in an effort to improve energy efficiency, reliability, and performance. At this time of significant disruption, it is critically important to understand specific application requirements, so that these architectural changes can include features that satisfy the requirements of contemporary extreme-scale scientific applications. To address this need, we have developed a methodology supported by a toolkit that allows us to investigate detailed computation, memory, and communication behaviors of applications at varying levels of resolution. Using this methodology, we performed a broad-based, detailed characterization of 12 contemporary scalable scientific applications and benchmarks. Our analysis reveals numerous behaviors that sometimes contradict conventional wisdom about scientific applications. For example, the results reveal that only one of our applications executes more floating-point instructions than other types of instructions. In another example, we found that communication topologies are very regular, even for applications that, at first glance, should be highly irregular. These observations emphasize the necessity of measurement-driven analysis of real applications, and help prioritize features that should be included in future architectures.