Whirlpool: Improving Dynamic Cache Management with Static Data Classification

Cache hierarchies are increasingly non-uniform and difficult to manage. Several techniques, such as scratchpads or reuse hints, use static information about how programs access data to manage the memory hierarchy. Static techniques are effective on regular programs, but because they set fixed policies, they are vulnerable to changes in program behavior or available cache space. Instead, most systems rely on dynamic caching policies that adapt to observed program behavior. Unfortunately, dynamic policies spend significant resources trying to learn how programs use memory, and yet they often perform worse than a static policy. We present Whirlpool, a novel approach that combines static information with dynamic policies to reap the benefits of each. Whirlpool statically classifies data into pools based on how the program uses memory. Whirlpool then uses dynamic policies to tune the cache to each pool. Hence, rather than setting policies statically, Whirlpool uses static analysis to guide dynamic policies. We present both an API that lets programmers specify pools manually and a profiling tool that discovers pools automatically in unmodified binaries. We evaluate Whirlpool on a state-of-the-art NUCA cache. Whirlpool significantly outperforms prior approaches: on sequential programs, Whirlpool improves performance by up to 38% and reduces data movement energy by up to 53%; on parallel programs, Whirlpool improves performance by up to 67% and reduces data movement energy by up to 2.6x.National Science Foundation (U.S.) (grant CCF-1318384)National Science Foundation (U.S.) (CAREER-1452994)Samsung (Firm) (GRO award

Bridging Theory and Practice in Cache Replacement

Much prior work has studied processor cache replacement policies, but a large gap remains between theory and practice. The optimal policy (MIN) requires unobtainable knowledge of the future, and prior theoretically-grounded policies use reference models that do not match real programs. Meanwhile, practical policies are designed empirically. Lacking a strong theoretical foundation, they do not make the best use of the information available to them. This paper bridges theory and practice. We propose that practical policies should replace lines based on their economic value added (EVA), the difference of their expected hits from the average. We use Markov decision processes to show that EVA is optimal under some reasonable simplifications. We present an inexpensive, practical implementation of EVA and evaluate it exhaustively over many cache sizes. EVA outperforms prior practical policies and saves area at iso-performance. These results show that formalizing cache replacement yields practical benefits