Name
Abstract | ||
|---|---|---|
Recent studies have found that parallel garbage collection performs worse with more CPUs and more collector threads. As part of this work, we further investigate this enomenon and find that poor scalability is worst in highly scalable Java applications. Our investigation to find the causes clearly reveals that efficient multi-threading in an application can prolong the average object lifespan, which results in less effective garbage collection. We also find that prolonging lifespan is the direct result of Linuxu0027s Completely Fair Scheduler due to its round-robin like behavior that can increase the heap contention between the application threads. Instead, if we use pseudo first-in-first-out to schedule application threads in large multicore systems, the garbage collection scalability is significantly improved while the time spent in garbage collection is reduced by as much as 21%. The average execution time of the 24 Java applications used in our study is also reduced by 11%. Based on this observation, we propose two approaches to optimally select scheduling policies based on application scalability profile. Our first approach uses the profile information from one execution to tune the subsequent executions. Our second approach dynamically collects profile information and performs policy selection during execution. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2892242.2892248 | VEE |
Keywords | Field | DocType |
garbage collection, object lifespan, process scheduling | Garbage,Computer science,Manual memory management,Scheduling (computing),Parallel computing,Heap (data structure),Real-time computing,Thread (computing),Garbage collection,Java,Operating system,Scalability | Conference |
Volume | Issue | ISSN |
51 | 7 | 0362-1340 |
ISBN | Citations | PageRank |
978-1-4503-3947-6 | 1 | 0.35 |
References | Authors | |
16 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junjie Qian | 1 | 8 | 2.13 |
| Witawas Srisa-an | 2 | 294 | 28.67 |
| Sharad C. Seth | 3 | 671 | 93.61 |
| Hong Jiang | 4 | 2137 | 157.96 |
| Du Li | 5 | 398 | 24.03 |
| Pan Yi | 6 | 1 | 0.35 |