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A Framework for Comparing Models of Computation
 IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems
, 1998
"... Abstract—We give a denotational framework (a “meta model”) within which certain properties of models of computation can be compared. It describes concurrent processes in general terms as sets of possible behaviors. A process is determinate if, given the constraints imposed by the inputs, there are e ..."
Abstract

Cited by 250 (55 self)
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Abstract—We give a denotational framework (a “meta model”) within which certain properties of models of computation can be compared. It describes concurrent processes in general terms as sets of possible behaviors. A process is determinate if, given the constraints imposed by the inputs, there are exactly one or exactly zero behaviors. Compositions of processes are processes with behaviors in the intersection of the behaviors of the component processes. The interaction between processes is through signals, which are collections of events. Each event is a valuetag pair, where the tags can come from a partially ordered or totally ordered set. Timed models are where the set of tags is totally ordered. Synchronous events share the same tag, and synchronous signals contain events with the same set of tags. Synchronous processes have only synchronous signals as behaviors. Strict causality (in timed tag systems) and continuity (in untimed tag systems) ensure determinacy under certain technical conditions. The framework is used to compare certain essential features of various models of computation, including Kahn process networks, dataflow, sequential processes, concurrent sequential processes with rendezvous, Petri nets, and discreteevent systems. I.
Heterogenous Simulation  mixing discreteevent model with dataflow
, 1996
"... This paper relates to systemlevel design of signal processing systems, which are often heterogeneous in implementation technologies and design styles. The heterogeneous approach, by combining small, specialized models of computation, achieves generality and also lends itself to automatic synthesis ..."
Abstract

Cited by 18 (4 self)
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This paper relates to systemlevel design of signal processing systems, which are often heterogeneous in implementation technologies and design styles. The heterogeneous approach, by combining small, specialized models of computation, achieves generality and also lends itself to automatic synthesis and formal verification. Key to the heterogeneous approach is to define interaction semantics that resolve the ambiguities when different models of computation are brought together. For this purpose, we introduce a tagged signal model as a formal framework within which the models of computation can be precisely described and unambiguously differentiated, and their interactions can be understood. In this paper, we will focus on the interaction between dataflow models, which have partially ordered events, and discreteevent models, with their notion of time that usually defines a total order of events. A variety of interaction semantics, mainly in handling the different notions of time in the two models, are explored to illustrate the subtleties involved. An implementation based on the Ptolemy system from U.C. Berkeley is described and critiqued.
The tagged signal model  a preliminary version of a denotational framework for comparing models of computation
 University of California, Berkeley, CA
, 1996
"... We give a denotational framework that describes concurrent processes in very general terms as sets of possible behaviors. Compositions of processes are given as intersections of their behaviors. The interaction between processes is through signals, which are collections of events. A system is determ ..."
Abstract

Cited by 6 (3 self)
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We give a denotational framework that describes concurrent processes in very general terms as sets of possible behaviors. Compositions of processes are given as intersections of their behaviors. The interaction between processes is through signals, which are collections of events. A system is determinate if given the constraints imposed by the inputs there are exactly one or exactly zero behaviors. Each event is a valuetag pair, where the tags can come from a partially ordered or totally ordered set. Timed models are where the set of tags is totally ordered. Synchronous events share the same tag, and synchronous signals contain events with the same set of tags. Synchronous systems contain synchronous signals. Strict causality (in timed systems) and continuity (in untimed systems) ensure determinacy under certain technical conditions. The framework is used to compare certain essential features of various models of computation, including Kahn process networks, dataflow, sequential processes, concurrent sequential processes with rendezvous, and discreteevent systems. 1.
HETEROGENOUS SIMULATION — MIXING DISCRETEEVENT MODELS WITH DATAFLOW
, 1996
"... This paper relates to systemlevel design of signal processing systems, which are often heterogeneous in implementation technologies and design styles. The heterogeneous approach, by combining small, specialized models of computation, achieves generality and also lends itself to automatic synthesis ..."
Abstract
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This paper relates to systemlevel design of signal processing systems, which are often heterogeneous in implementation technologies and design styles. The heterogeneous approach, by combining small, specialized models of computation, achieves generality and also lends itself to automatic synthesis and formal verification. Key to the heterogeneous approach is to define interaction semantics that resolve the ambiguities when different models of computation are brought together. For this purpose, we introduce a tagged signal model as a formal framework within which the models of computation can be precisely described and unambiguously differentiated, and their interactions can be understood. In this paper, we will focus on the interaction between dataflow models, which have partially ordered events, and discreteevent models, with their notion of time that usually defines a total order of events. A variety of interaction semantics, mainly in handling the different notions of time in the two models, are explored to illustrate the subtleties involved. An implementation based on the Ptolemy system from U.C. Berkeley is described and critiqued. 1.
MODELING CONCURRENT REALTIME PROCESSES USING DISCRETE EVENTS
, 1997
"... We give a formal framework for studying realtime discreteevent systems. It describes concurrent processes as sets of possible behaviors. Compositions of processes are processes with behaviors in the intersection of the behaviors of the component processes. The interaction between processes is thro ..."
Abstract
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We give a formal framework for studying realtime discreteevent systems. It describes concurrent processes as sets of possible behaviors. Compositions of processes are processes with behaviors in the intersection of the behaviors of the component processes. The interaction between processes is through signals, which are collections of events. Each event is a valuetag pair, where the tags denote time. Zeno conditions are defined and methods are given for avoiding them. Strict causality ensures determinacy under certain technical conditions, and deltacausality ensures the absence of Zeno conditions.
A DENOTATIONAL FRAMEWORK FOR COMPARING MODELS OF COMPUTATION
, 1997
"... We give a denotational framework (a “meta model”) within which certain properties of models of computation can be understood and compared. It describes concurrent processes in general terms as sets of possible behaviors. A process is determinate if given the constraints imposed by the inputs there a ..."
Abstract
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We give a denotational framework (a “meta model”) within which certain properties of models of computation can be understood and compared. It describes concurrent processes in general terms as sets of possible behaviors. A process is determinate if given the constraints imposed by the inputs there are exactly one or exactly zero behaviors. Compositions of processes are processes with behaviors in the intersection of the behaviors of the component processes. The interaction between processes is through signals, which are collections of events. Each event is a valuetag pair, where the tags can come from a partially ordered or totally ordered set. Timed models are where the set of tags is totally ordered. Synchronous events share the same tag, and synchronous signals contain events with the same set of tags. Synchronous processes have only synchronous signals as behaviors. Strict causality (in timed tag systems) and continuity (in untimed tag systems) ensure determinacy under certain technical conditions. The framework is used to compare certain essential features of various models of computation, including Kahn process networks, dataflow, sequential processes, concurrent sequential processes with rendezvous, Petri nets, and discreteevent systems. 1.