Modeling Expected Application Runtime for Characterizing and Assessing Job Performance
Omar Aaziz, Jonathan Emdin Cook, Mohammed Tanash · 2018
In this paper, we present a methodology for modeling the expected runtime of a job based on historical application data and data from the job itself. This estimation model is useful for both for HPC users and administrators as a metric to compare the actual job runtime to, thus establishing a measure of performance of the job. We used job data, system data, and hardware performance counters in a near-zero overhead manner to model and assess job performance, in particular whether or not the job runtime was in line with expectations from historical application performance. We show over three proxy applications and three real applications that our estimations are within 5% of actual performance.