Parallelization, Modeling, and Performance Prediction in the Multi-/Many Core Area: A Systematic Literature Review

Abstract

Context: Software developers face complex, connected, and large software projects. The development of such systems involves design decisions that directly impact the quality of the software. For an early decision making, software developers can use model-based prediction approaches for (non-)functional quality properties. Unfortunately, the accuracy of these approaches is challenged by newly introduced hardware features like multiple cores within a single CPU (multicores) and their dependence on shared memory and other shared resources. Objectives: Our goal is to understand whether and how existing model-based performance prediction approaches face this challenge. We plan to use gained insights as foundation for enriching existing prediction approaches with capabilities to predict systems running on multicores. Methods: We perform a Systematic Literature Review (SLR) to identify current model-based prediction approaches in the context of multicores. Results: Our SLR covers the software engineering, embedded systems, High Performance Computing, and Software Performance Engineering domains for which we examined 34 sources in detail. We found various performance prediction approaches which tries to increase prediction accuracy for multicore systems by including shared memory designs to the prediction models. Conclusion: However, our results show that the memory designs models are only in an initial phase. Further research has to be done to improve cache, memory, and memory bandwidth model as well as to include auto tuner support.