Predicting Performance Using Collaborative Filtering

Performance prediction of parallel applications across systems becomes increasingly important in today's diverse computing environments. A wide range of choices in execution platforms pose new challenges to researchers in choosing a system which best fits their workloads and administrators in scheduling applications to the best performing systems. While previous studies have employed simulation-or profile-based prediction approaches, such solutions are time-consuming to be deployed on multiple platforms. To address this problem, we use two collaborative filtering techniques to build analytical models which can quickly and accurately predict the performance of workloads across different multicore systems. The first technique leverages information gained from performance observed for certain applications on a subset of systems and use it to discover similarities among applications as well as systems. The second collaborative filtering based model learns latent features of systems and workloads automatically and use these features to characterize the performance of applications on different platforms. We evaluated both the methods using 30 workloads chosen from NAS Parallel Benchmarks, BOTS and Rodinia benchmarking suites on ten different systems. Our results show that such collaborative filtering methods can make predictions with RMSE as low as 0.6 and with an average RMSE of 1.6.