DocumentCode :
2946307
Title :
Deadlock detection for distributed process networks
Author :
Olson, Alex G. ; Evans, Brian L.
Author_Institution :
Embedded Signal Process. Lab., Texas Univ., Austin, TX, USA
Volume :
5
fYear :
2005
fDate :
18-23 March 2005
Abstract :
The process network (PN) model, which consists of concurrent processes communicating over first-in first out unidirectional queues, is useful for modeling and exploiting functional parallelism in streaming data applications. The PN model maps easily onto multi-processor and/or multi-threaded targets. Since the PN model is Turing complete, memory requirements cannot be predicted statically. In general, any bounded-memory scheduling algorithm for this model requires run-time deadlock detection. The few PN implementations that perform deadlock detection detect only global deadlocks. Not all local deadlocks, however, will cause a PN system to reach global deadlock. In this paper, we present the first local deadlock detection algorithm for PN models. The proposed algorithm is based on the Mitchell and Merritt algorithm and is suitable for both parallel and distributed PN implementations.
Keywords :
concurrency control; multi-threading; multiprocessing systems; parallel processing; processor scheduling; Mitchell-Merritt algorithm; Turing complete model; bounded-memory scheduling algorithm; concurrent processes; distributed process networks; first-in first out unidirectional queues; local deadlock detection algorithm; memory requirement predictions; multiprocessor systems; multithreaded systems; parallel processing; process network model; run-time deadlock detection; streaming data functional parallelism; Concurrent computing; Detection algorithms; Laboratories; Parallel processing; Signal processing; Signal processing algorithms; Sonar; System recovery; Workstations; Yarn;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-8874-7
Type :
conf
DOI :
10.1109/ICASSP.2005.1416243
Filename :
1416243
Link To Document :
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