Automatic performance prediction of multithreaded programs: a simulation approach

The performance of multithreaded programs is often difficult to understand and predict. Multiple threads engage in synchronization operations and use hardware simultaneously. This results in a complex non-linear dependency between the configuration of a program and its performance. To better understand this dependency a performance prediction model is used. Such a model predicts the performance of a system for different configurations. Configurations reflect variations in the workload, different program options such as the number of threads, and characteristics of the hardware. Performance models are complex and require a solid understanding of the program's behavior. As a result, building models of large applications manually is extremely time-consuming and error-prone. In this paper we present an approach for building performance models of multithreaded programs automatically. We employ hierarchical discrete-event models. Different tiers of the model simulate different factors that affect performance of the program, while interaction between the model tiers simulates mutual influence of these factors on performance. Our framework uses a combination of static and dynamic analyses of a single representative run of a system to collect information required for building the performance model. This includes information about the structure of the program, the semantics of interaction between the program's threads, and resource demands of individual program's components. In our experiments we demonstrate that models accurately predict the performance of various multithreaded programs, including complex industrial applications.