Automated and accurate cache behavior analysis for codes with irregular access patterns

The memory hierarchy plays an essential role in the performance of current computers, thus good analysis tools that help predict and understand its behavior are required. Analytical modeling is the ideal base for such tools if its traditional limitations in accuracy and scope of application are overcome. While there has been extensive research on the modeling of codes with regular access patterns, less attention has been paid to codes with irregular patterns due to the increased difficulty to analyze them. Nevertheless, many important applications exhibit this kind of patterns, and their lack of locality make them more cache-demanding, which makes their study more relevant. The focus of this paper is the automation of the Probabilistic Miss Equations (PME) model, an analytical model of the cache behavior that provides fast and accurate predictions for codes with irregular access patterns. The paper defines the information requirements of the PME model and describes its integration in the XARK compiler, a research compiler oriented to automatic kernel recognition in scientific codes. We show how to exploit the powerful information-gathering capabilities provided by this compiler to allow automated modeling of loop-oriented scientific codes. Experimental results that validate the correctness of the automated PME model are also presented.