Measuring the Resiliency of Extreme-Scale Computing Environments

This chapter presents a case study on how to characterize the resiliency of large-scale computers. The analysis focuses on the failures and errors of Blue Waters, the Cray hybrid (CPU/GPU) supercomputer at the University of Illinois at Urbana-Champaign. The characterization is performed by a joint analysis of several data sources, which include workload and error/failure logs as well as manual failure reports. We describe LogDiver, a tool to automate the data preprocessing and metric computation that measure the impact of system errors and failures on user applica-tions, i.e., the compiled programs launched by user jobs that can execute across one or more XE (CPU) or XK (CPU+GPU) nodes. Results include (i) a characterization of the root causes of single node failures; (ii) a direct assessment of the effectiveness of system-level failover and of memory, processor, network, GPU accelerator, and file system error resiliency; (iii) an analysis of system-wide outages; (iv) analysis of application resiliency to system-related errors; and (v) insight into the relationship between application scale and resiliency across different error categories. © Springer International Publishing Switzerland 2016.