Predicting HPC Job Run time with Realistic Data Using Application Input Parameters

It is difficult to accurately predict application run times in high performance computing (HPC), yet these predictions have useful applications in job scheduling and user feedback. User-led predictions can be inaccurate for a variety of factors, including inexperience, user burden, and an incentive to overpredict. Most automated efforts consider standardized job inputs from submission scripts but ignore application input parameters. Application input parameters can greatly enhance run time prediction accuracy but have typically been avoided due to the need for manual, per-application parameter collection.This work is an extension of our previous publication, which evaluated and compared the trade-offs between conventional, job script-based predictors and specialized, application input-based predictors. To extend the prior work, this paper provides prediction results as applied the Empire application suite, a suite of more realistic verification tests and regression runs, rather than the synthetically generated testing previously evaluated. As in that work and prior work, the random forest regressor and decision tree models offer the best trade-off between accuracy and training time among all tested model variants. We show that resource manager parameters alone, such as those used by Slurm, are insufficient to produce adequate predictions, while application input parameters provide excellent results, as high as 0.95 R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.