Author
J.T. Buck
Other affiliations: University of California
Bio: J.T. Buck is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Dataflow & Scheduling (computing). The author has an hindex of 7, co-authored 8 publications receiving 1727 citations. Previous affiliations of J.T. Buck include University of California.
Papers
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TL;DR: Ptolemy as discussed by the authors is an environment for simulation and prototyping of heterogeneous systems, which uses object-oriented software technology to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole.
Abstract: Ptolemy is an environment for simulation and prototyping of heterogeneous systems. It uses modern object-oriented software technology (C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. Ptolemy encompasses practically all aspects of designing signal processing and communications systems, ranging from algorithms and communication strategies, simulation, hardware and software design, parallel computing, and generating real-time prototypes. To accommodate this breadth, Ptoloemy must support a plethora of widely differing design styles. The core of Ptolemy is a set of object-oriented class definitions that makes a few assumptions about the system to be modeled; rather, standard interfaces are provided for generic objects and more specialized, application-specific objects are derived from these. A basic abstraction in Ptolemy is the Domain, which realizes a computational model appropriate for a particular type of subsystem. Current examples of domains include synchronous and dynamic dataflow, discrete-event, and others appropriate for control software and embedded microcontrollers. Domains can be mixed as appropriate to realize and overall system simulation. Some current applications of Ptolemy include networking and transport, call-processing and signaling software, embedded microcontrollers, signal processing (including implementation in real-time), scheduling of parallel digital signal processors, board-level hardware timing simulation, and combinations of these.
980 citations
27 Apr 1993
TL;DR: The authors build upon research by E. A. Lee (1991) concerning the token flow model by analyzing the properties of cycles of the schedule: sequences of actor executions that return the graph to its initial state.
Abstract: The authors build upon research by E. A. Lee (1991) concerning the token flow model, an analytical model for the behavior of dataflow graphs with data-dependent control flow, by analyzing the properties of cycles of the schedule: sequences of actor executions that return the graph to its initial state. Necessary and sufficient conditions are given for the existence of a bounded cyclic schedule as well as sufficient conditions for execution of the graph in bounded memory. The techniques presented apply to a more general class of dataflow graphs than previous methods. >
521 citations
11 Jan 1995
TL;DR: The software synthesis aspects of the Ptolemy system are introduced, which allows the user to choose among various single- or multiple-processor schedulers.
Abstract: Ptolemy is an environment for simulation, prototyping, and software synthesis for heterogeneous systems. It uses modern object-oriented software technology (in C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. The objectives of Ptolemy encompass practically all aspects of designing signal processing and communications systems, ranging from algorithms and communication strategies, through simulation, hardware and software design, parallel computing, and generation of real-time prototypes. In this paper we will introduce the software synthesis aspects of the Ptolemy system. The environment presented here is both modular and extensible. Ptolemy allows the user to choose among various single- or multiple-processor schedulers.
145 citations
TL;DR: It is shown how other scheduling objectives, such as minimizing data buffering requirements or increasing the number of data transfers that occur in registers, can be incorporated in a manner that does not conflict with the goal of code compactness.
Abstract: Synchronous dataflow (SDF) semantics are well-suited to representing and compiling multirate signal processing algorithms. A key to this match is the ability to cleanly express iteration without overspecifying the execution order of computations, thereby allowing efficient schedules to be constructed. Due to limited program memory, it is often desirable to translate the iteration in an SDF graph into groups of repetitive firing patterns so that loops can be constructed in the target code. This paper establishes fundamental topological relationships between iteration and looping in SDF graphs, and presents a scheduling framework that provably synthesizes the most compact looping structures for a large class of practical SDF graphs. By modularizing different components of the scheduling framework, and establishing their independence, we show how other scheduling objectives, such as minimizing data buffering requirements or increasing the number of data transfers that occur in registers, can be incorporated in a manner that does not conflict with the goal of code compactness. >
37 citations
14 Apr 1991
TL;DR: The use of two models of computation, synchronous dataflow (SDF) and dynamic data flow (DDF), to design and implement signal processing applications with multiple sample rates is discussed.
Abstract: The use of two models of computation, synchronous dataflow (SDF) and dynamic dataflow (DDF), to design and implement signal processing applications with multiple sample rates is discussed. The SDF model is used for synchronous applications. SDF is amenable to compile-time scheduling, and hence is much more efficient at runtime. The design environment, Ptolemy, can simultaneously support multiple models of computation, so SDF and DDF can be combined in a single application. Hence, the implementation will incur the run-time cost of DDF only for those asynchronous portions that absolutely must incur such cost. As an illustration, the authors detail a synchronous application, sample-rate conversion using polyphase filters, and an asynchronous application, timing recovery for an amplitude-shift-keyed signal. >
34 citations
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Book•
27 Dec 1999
TL;DR: The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
Abstract: Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.
2,062 citations
29 Jan 2003
TL;DR: It is argued that modeling systems in this manner leads to unexpected and hard-to-analyze interactions between the communication mechanisms and proposes a more structured approach to heterogeneity, called hierarchical heterogeneity, to solve this problem.
Abstract: Modern embedded computing systems tend to be heterogeneous in the sense of being composed of subsystems with very different characteristics, which communicate and interact in a variety of ways-synchronous or asynchronous, buffered or unbuffered, etc. Obviously, when designing such systems, a modeling language needs to reflect this heterogeneity. Today's modeling environments usually offer a variant of what we call amorphous heterogeneity to address this problem. This paper argues that modeling systems in this manner leads to unexpected and hard-to-analyze interactions between the communication mechanisms and proposes a more structured approach to heterogeneity, called hierarchical heterogeneity, to solve this problem. It proposes a model structure and semantic framework that support this form of heterogeneity, and discusses the issues arising from heterogeneous component interaction and the desire for component reuse. It introduces the notion of domain polymorphism as a way to address these issues.
1,146 citations
Book•
23 Jan 2013
TL;DR: This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study.
Abstract: The most visible use of computers and software is processing information for human consumption. The vast majority of computers in use, however, are much less visible. They run the engine, brakes, seatbelts, airbag, and audio system in your car. They digitally encode your voice and construct a radio signal to send it from your cell phone to a base station. They command robots on a factory floor, power generation in a power plant, processes in a chemical plant, and traffic lights in a city. These less visible computers are called embedded systems, and the software they run is called embedded software. The principal challenges in designing and analyzing embedded systems stem from their interaction with physical processes. This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study. The focus is on modeling, design, and analysis of cyber-physical systems, which integrate computation, networking, and physical processes. The second edition offers two new chapters, several new exercises, and other improvements. The book can be used as a textbook at the advanced undergraduate or introductory graduate level and as a professional reference for practicing engineers and computer scientists. Readers should have some familiarity with machine structures, computer programming, basic discrete mathematics and algorithms, and signals and systems.
1,017 citations
TL;DR: Ptolemy as discussed by the authors is an environment for simulation and prototyping of heterogeneous systems, which uses object-oriented software technology to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole.
Abstract: Ptolemy is an environment for simulation and prototyping of heterogeneous systems. It uses modern object-oriented software technology (C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. Ptolemy encompasses practically all aspects of designing signal processing and communications systems, ranging from algorithms and communication strategies, simulation, hardware and software design, parallel computing, and generating real-time prototypes. To accommodate this breadth, Ptoloemy must support a plethora of widely differing design styles. The core of Ptolemy is a set of object-oriented class definitions that makes a few assumptions about the system to be modeled; rather, standard interfaces are provided for generic objects and more specialized, application-specific objects are derived from these. A basic abstraction in Ptolemy is the Domain, which realizes a computational model appropriate for a particular type of subsystem. Current examples of domains include synchronous and dynamic dataflow, discrete-event, and others appropriate for control software and embedded microcontrollers. Domains can be mixed as appropriate to realize and overall system simulation. Some current applications of Ptolemy include networking and transport, call-processing and signaling software, embedded microcontrollers, signal processing (including implementation in real-time), scheduling of parallel digital signal processors, board-level hardware timing simulation, and combinations of these.
980 citations
01 May 1995
TL;DR: Dataflow process networks are shown to be a special case of Kahn process networks, a model of computation where a number of concurrent processes communicate through unidirectional FIFO channels, where writes to the channel are nonblocking, and reads are blocking.
Abstract: We review a model of computation used in industrial practice in signal processing software environments and experimentally and other contexts. We give this model the name "dataflow process networks," and study its formal properties as well as its utility as a basis for programming language design. Variants of this model are used in commercial visual programming systems such as SPW from the Alta Group of Cadence (formerly Comdisco Systems), COSSAP from Synopsys (formerly Cadis), the DSP Station from Mentor Graphics, and Hypersignal from Hyperception. They are also used in research software such as Khoros from the University of New Mexico and Ptolemy from the University of California at Berkeley, among many others. Dataflow process networks are shown to be a special case of Kahn process networks, a model of computation where a number of concurrent processes communicate through unidirectional FIFO channels, where writes to the channel are nonblocking, and reads are blocking. In dataflow process networks, each process consists of repeated "firings" of a dataflow "actor." An actor defines a (often functional) quantum of computation. By dividing processes into actor firings, the considerable overhead of context switching incurred in most implementations of Kahn process networks is avoided. We relate dataflow process networks to other dataflow models, including those used in dataflow machines, such as static dataflow and the tagged-token model. We also relate dataflow process networks to functional languages such as Haskell, and show that modern language concepts such as higher-order functions and polymorphism can be used effectively in dataflow process networks. A number of programming examples using a visual syntax are given. >
976 citations