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PLDI 2020
Mon 15 - Fri 19 June 2020

We show how to infer deterministic cache replacement policies using off-the-shelf automata learning and program synthesis techniques. For this, we construct and chain two abstractions that expose the cache replacement policy of any set in the cache hierarchy as a membership oracle to the learning algorithm, based on timing measurements on a silicon CPU. Our experiments demonstrate an advantage in scope and scalability over prior art and uncover two previously undocumented cache replacement policies.

Thu 18 Jun
Times are displayed in time zone: Pacific Time (US & Canada) change

16:00 - 16:20
Multi-modal Synthesis of Regular Expressions
PLDI Research Papers
Qiaochu ChenUniversity of Texas at Austin, USA, Xinyu WangUniversity of Michigan at Ann Arbor, USA, Xi YeUniversity of Texas at Austin, USA, Greg DurrettUniversity of Texas at Austin, USA, Isil DilligUniversity of Texas at Austin, USA
16:20 - 16:40
Optimizing Homomorphic Evaluation Circuits by Program Synthesis and Term Rewriting
PLDI Research Papers
DongKwon LeeSeoul National University, South Korea, Woosuk LeeHanyang University, South Korea, Hakjoo OhKorea University, South Korea, Kwangkeun YiSeoul National University, South Korea
16:40 - 17:00
CacheQuery: Learning Replacement Policies from Hardware Caches
PLDI Research Papers
Pepe VilaIMDEA Software Institute, Spain, Pierre GantyIMDEA Software Institute, Spain, Marco GuarnieriIMDEA Software Institute, Spain, Boris KöpfMicrosoft Research, n.n.