Name
Abstract | ||
|---|---|---|
The fork-join model for parallel computing has become very popular and is included in the Java class library since Java 7. While understanding and optimizing the performance of fork-join computations is of paramount importance, accurately profiling them on the Java Virtual Machine (JVM) is challenging due to the complexity of the API. In this paper, we present a novel model for analyzing fork-join computations on the JVM, addressing the peculiarities of the Java fork-join framework, including features such as task unforking and task reuse. We implement our model in a profiler that detects every spawned fork-join task, capturing all task dependencies and aiming at collecting cycle-accurate task-granularity data. We evaluate our profiler against a dedicated fork-join profiler for the JVM, showing that our tool achieves higher profile accuracy and introduces less overhead. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-12597-3_3 | Euro-Par 2022: Parallel Processing |
Keywords | DocType | Volume |
Fork-join Parallelism, Work Stealing, Accurate Profiling, Task Granularity, Task Dependencies, Java | Conference | 13440 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Basso Matteo | 1 | 0 | 0.34 |
| Rosales Eduardo | 2 | 0 | 0.34 |
| Schiavio Filippo | 3 | 0 | 0.34 |
| Rosà Andrea | 4 | 0 | 0.34 |
| Walter Binder | 5 | 1077 | 92.58 |