Benchmarking and learning garbage collection delays for resource‐restricted graphical user interfaces

Tablets, smartphones, and wearables have limited resources. Applications on these devices employ a graphical user interface (GUI) for interaction with users. Language runtimes for GUIs employ dynamic memory management using garbage collection (GC). However, GC policies and algorithms are designed fo...

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Bibliographic Details
Published in:Software, practice & experience practice & experience, 2022-01, Vol.52 (1), p.172-193
Main Authors: McCarthy, Harry, Koay, Abigail, Dawson, Michael, Kent, Kenneth B., Patros, Panos
Format: Article
Language:English
Subjects:
Algorithms
benchmarking
Cloud computing
Data centers
Electronic devices
Experiments
Garbage collection
Graphical user interface
GUI GC
JVM
machine learning
Memory management
performance engineering
Policies
Run time (computers)
Sanitation services
Smartphones
Tablet computers
User interface
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Summary:Tablets, smartphones, and wearables have limited resources. Applications on these devices employ a graphical user interface (GUI) for interaction with users. Language runtimes for GUIs employ dynamic memory management using garbage collection (GC). However, GC policies and algorithms are designed for data centers and cloud computing, but they are not necessarily ideal for resource‐constrained embedded devices. In this article, we present GUI GC, a JavaFX GUI benchmark, which we use to compare the performance of the four GC policies of the Eclipse OpenJ9 Java runtime on a resource‐constrained environment. Overall, our experiments suggest that the default policy Gencon registered significantly lower execution times than its counterparts. The region‐based policy, Balanced, did not fully utilize blocking times; thus, using GUI GC, we conducted experiments with explicit GC invocations that measured significant improvements of up to 13.22% when multiple CPUs were available. Furthermore, we created a second version of GUI GC that expands on the number of controllable load‐stressing dimensions; we conducted a large number of randomly configured experiments to quantify the performance effect that each knob has. Finally, we analyzed our dataset to derive suitable knob configurations for desired runtime, GC, and hardware stress levels.
ISSN:0038-0644
1097-024X
DOI:10.1002/spe.3005