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Sun 23 Jun 2019 11:45 - 12:10 at 106A - Exotica

Generational garbage collectors are one of the most common types of automatic memory management. We can minimize the costs they incur by carefully choosing the points in a program's execution at which they run. However, this decision is generally based on simple, crude heuristics. Instead, we train random forest classifiers to decide when to collect based on features gathered from a running program. This reduces the total cost of collection in both time and space. We demonstrate useful generalization of learned policies to unseen traces of the same program, showing this approach may be fruitful for further investigation.

Sun 23 Jun

ismm-2019-papers
11:20 - 12:35: ISMM 2019 - Exotica at 106A
ismm-2019-papers11:20 - 11:45
Talk
Md Muhib KhanFlorida State University, Muhammad Ahad Ul AlamFlorida State University, USA, Amit Kumar NathFlorida State University, USA, Weikuan YuFlorida State University, USA
ismm-2019-papers11:45 - 12:10
Talk
Nicholas JacekUMass Amherst, Eliot MossUniversity of Massachusetts Amherst
ismm-2019-papers12:10 - 12:35
Talk
Pengcheng LiGoogle, Inc, Hao LuoUniversity of Rochester, Chen DingUniversity of Rochester