DocumentCode :
2788172
Title :
Task-pushing: a Scalable Parallel GC Marking Algorithm without Synchronization Operations
Author :
Wu, Ming ; Li, Xiao-Feng
Author_Institution :
lnst. of Comput. Technol., Chinese Acad. of Sci., Beijing
fYear :
2007
fDate :
26-30 March 2007
Firstpage :
1
Lastpage :
10
Abstract :
This paper describes a scalable parallel marking technique for garbage collection that does not employ any synchronization operation. To achieve good scalability, two major design issues have to be resolved in parallel marking algorithm, i.e., the overhead of synchronization operations and load balance. This paper presents task-pushing, a novel parallel marking algorithm where each thread proactively gives up its spare tasks to other threads. Enlightened by the idea of communicating sequential process (CSP), task-pushing arranges the computation into a process network, eliminating synchronization operations in the whole marking process. Load balance is achieved by dripping tasks from thread local mark-stack for other threads to execute. To the best of our knowledge, this is the first parallel marking algorithm that completely avoids the synchronization primitives. We evaluated task-pushing in aspects of queuing efficiency, load balancing strategy, synchronization overhead, and overall scalability. The results on a 16-way Intel Xeon machine showed that task-pushing has better scalability than work-stealing technique with pseudojbb and GCOld server-kind Java benchmarks.
Keywords :
Java; communicating sequential processes; parallel algorithms; resource allocation; storage management; Intel Xeon machine; Java; communicating sequential process; load balance; parallel garbage collection marking algorithm; parallel marking technique; synchronization operation; task-pushing step; Algorithm design and analysis; Compaction; Computer networks; Concurrent computing; Load management; Middleware; Partitioning algorithms; Scalability; Software algorithms; Yarn;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Processing Symposium, 2007. IPDPS 2007. IEEE International
Conference_Location :
Long Beach, CA
Print_ISBN :
1-4244-0910-1
Electronic_ISBN :
1-4244-0910-1
Type :
conf
DOI :
10.1109/IPDPS.2007.370317
Filename :
4228045
Link To Document :
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