Paper Info
Title
Analysis and Optimization of Task Granularity on the Java Virtual Machine.
Abstract
Task granularity, i.e., the amount of work performed by parallel tasks, is a key performance attribute of parallel applications. On the one hand, fine-grained tasks (i.e., small tasks carrying out few computations) may introduce considerable parallelization overheads. On the other hand, coarse-grained tasks (i.e., large tasks performing substantial computations) may not fully utilize the available CPU cores, leading to missed parallelization opportunities. In this article, we provide a better understanding of task granularity for task-parallel applications running on a single Java Virtual Machine in a shared-memory multicore. We present a new methodology to accurately and efficiently collect the granularity of each executed task, implemented in a novel profiler (available open-source) that collects carefully selected metrics from the whole system stack with low overhead, and helps developers locate performance and scalability problems. We analyze task granularity in the DaCapo, ScalaBench, and Spark Perf benchmark suites, revealing inefficiencies related to fine-grained and coarse-grained tasks in several applications. We demonstrate that the collected task-granularity profiles are actionable by optimizing task granularity in several applications, achieving speedups up to a factor of 5.90×. Our results highlight the importance of analyzing and optimizing task granularity on the Java Virtual Machine.
Year
DOI
Venue
2020
10.1145/3338497
ACM Transactions on Programming Languages and Systems
Keywords
DocType
Volume
Java virtual machine,Task granularity,actionable profiles,performance analysis and optimization,task parallelism,vertical profiling
Conference
41
Issue
ISSN
Citations 
3
0164-0925
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Andrea Rosà16312.04
Eduardo Rosales222.06
Walter Binder3107792.58