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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
Times are displayed in time zone: Tijuana, Baja California change

11:20 - 12:35
ExoticaISMM 2019 at 106A
11:20
25m
Talk
Exploration of Memory Hybridization for RDD Caching in Spark
ISMM 2019
Md Muhib KhanFlorida State University, Muhammad Ahad Ul AlamFlorida State University, USA, Amit Kumar NathFlorida State University, USA, Weikuan YuFlorida State University, USA
11:45
25m
Talk
Learning When to Garbage Collect with Random Forests
ISMM 2019
Nicholas JacekUMass Amherst, Eliot MossUniversity of Massachusetts Amherst
12:10
25m
Talk
Timescale Functions for Parallel Memory Allocation
ISMM 2019
Pengcheng LiGoogle, Inc, Hao LuoUniversity of Rochester, Chen DingUniversity of Rochester