Automated memoization: Automatically identifying memoization units in simulation parameter studies

Abstract

Simulations, and in particular large scale parameter studies, typically exhibit a considerable amount of redundancies. These redundancies can be avoided by memoization, a technique that stores and re-uses intermediate results. This requires a Memoization Unit (MU) to be identified first and then transformed. We have recently enabled the automation of the second step to also be applicable to impure computations, allowing it to become a valuable tool for the modeling and simulation domain. However, the first step still needs to be performed manually. Hence, the user needs to understand the model and the concept of memoization well enough to specify which computations to annotate for memoization. In this paper, we describe our approach to automatically identify memoization-worth computations. Input to this algorithm is an unmodified parameter study. After identifying the most promising memoization opportunities, we use the existing automated memoization tool to create a memoized parameter study, which can then be executed quickly. Our evaluation shows that our automated approach is able to identify those MUs that previously had to be annotated manually. This identification takes less than 2 minutes for a case study that without memoization takes several hours.