SimProf: A Sampling Framework for Data Analytic Workloads

Abstract

Today, there is a steep rise in the amount of data being collected from diverse applications. Consequently, data analytic workloads are gaining popularity to gain insight that can benefit the application, e.g., financial trading, social media analysis. To study the architectural behavior of the workloads, architectural simulation is one of the most common approaches. However, because of the long-running nature of the workloads, it is not trivial to identify which parts of the analysis to simulate. In the current work, we introduce SimProf, a sampling framework for data analytic workloads. Using this tool, we are able to select representative simulation points based on the phase behavior of the analysis at a method level granularity. This provides a better understanding of the simulation point and also reduces the simulation time for different input sets. We present the framework for Apache Hadoop and Apache Spark frameworks, which can be easily extended to other data analytic workloads.