22th Meeting

Practice talk by Alex Tarvo on his accepted paper to this year’s ICPE.

Predicting the performance of a computer program facilitates its efficient design, deployment, and problem detection. However, predicting performance of multithreaded programs is complicated by complex locking behavior and concurrent usage of computational resources. Existing performance models either require running the program in many different configurations or impose restrictions on the types of programs that can be modeled. We present an approach towards building performance models that do not require vast amounts of training data. Our models are built using a combination of queuing networks and probabilistic call graphs. All necessary information is collected using static and dynamic analyses of a single run of the program. In our experiments these models were able to accurately predict performance of different types of multithreaded programs and detected those configurations that result in the programs’ high performance.