Generative communication in Linda
ACM Transactions on Programming Languages and Systems (TOPLAS)
ACM Transactions on Programming Languages and Systems (TOPLAS)
Parallel discrete event simulation
Communications of the ACM - Special issue on simulation
Parallel and distributed simulation
WSC '95 Proceedings of the 27th conference on Winter simulation
Garbage collection: algorithms for automatic dynamic memory management
Garbage collection: algorithms for automatic dynamic memory management
Space-time memory: a parallel programming abstraction for interactive multimedia applications
Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Garbage collection of timestamped data in Stampede
Proceedings of the nineteenth annual ACM symposium on Principles of distributed computing
Asynchronous distributed simulation via a sequence of parallel computations
Communications of the ACM - Special issue on simulation modeling and statistical computing
A computational model of everything
Communications of the ACM
Uniprocessor Garbage Collection Techniques
IWMM '92 Proceedings of the International Workshop on Memory Management
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Dead Timestamp Identification in Stampede
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
SIMULATION OF PACKET COMMUNICATION ARCHITECTURE COMPUTER SYSTEMS
SIMULATION OF PACKET COMMUNICATION ARCHITECTURE COMPUTER SYSTEMS
A comparative study of stampede garbage collection algorithms
LCPC'02 Proceedings of the 15th international conference on Languages and Compilers for Parallel Computing
Stampede: a cluster programming middleware for interactive stream-oriented applications
IEEE Transactions on Parallel and Distributed Systems
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There is an important class of interactive multimedia applications that deals with stream data from distributed sources. Indexing the data temporally facilitates ordering individual streams as well as correlating items from different streams. The Stampede programming system organizes stream data into channels that are distributed and synchronized data structures that contain timestamped items. A Stampede program is a data flow graph of threads and channels. Stampede semantics for channels allow concurrent access from multiple threads for input and output. While a channel holds timestamped items, the semantics do not place any restriction on either the production or consumption order of these items. Furthermore, timestamps of items in a channel need not be contiguous. These flexibilities are required due to the dynamic and parallel structure of stream-oriented applications targeted by the Stampede system. Under such circumstances, a key issue is the "garbage collection” (GC) of channel items. In this paper, we present and compare three different GC algorithms: 1) REF is a simple algorithm that keeps a reference count on individual items; 2) TGC is a distributed algorithm for computing a global low watermark for timestamp values of interest in the entire application; 3) DGC is another distributed algorithm that uses information about the dependencies between the producers and consumers of data streams to compute a low water mark local to each node of the data flow graph. DGC can simultaneously eliminate garbage from channels and unneeded computations from threads. In tests performed using an interactive application, DGC enjoys nearly 30 percent reduction in the application memory footprint compared to TGC and REF. DGC and REF are also shown to be more scalable compared to TGC.