Analysis Of Cache Behavior Research Articles

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

AbstractThe memory hierarchy plays an essential role in the performance of current computers, so good analysis tools that help in predicting and understanding its behavior are required. Analytical modeling is the ideal base for such tools if its traditional limitations in accuracy and scope of application can be 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 in analyzing them. Nevertheless, many important applications exhibit this kind of pattern, 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 information requirements of the PME model are defined and its integration in the XARK compiler, a research compiler oriented to automatic kernel recognition in scientific codes, is described. We show how to exploit the powerful information‐gathering capabilities provided by this compiler to allow the automated modeling of loop‐oriented scientific codes. Experimental results that validate the correctness of the automated PME model are also presented. Copyright © 2007 John Wiley & Sons, Ltd.

Exact analysis of the cache behavior of nested loops

We develop from first principles an exact model of the behavior of loop nests executing in a memory hicrarchy, by using a nontraditional classification of misses that has the key property of composability. We use Presburger formulas to express various kinds of misses as well as the state of the cache at the end of the loop nest. We use existing tools to simplify these formulas and to count cache misses. The model is powerful enough to handle imperfect loop nests and various flavors of non-linear array layouts based on bit interleaving of array indices. We also indicate how to handle modest levels of associativity, and how to perform limited symbolic analysis of cache behavior. The complexity of the formulas relates to the static structure of the loop nest rather than to its dynamic trip count, allowing our model to gain efficiency in counting cache misses by exploiting repetitive patterns of cache behavior. Validation against cache simulation confirms the exactness of our formulation. Our method can serve as the basis for a static performance predictor to guide program and data transformations to improve performance.