The Manchester prototype dataflow computer
Communications of the ACM - Special section on computer architecture
Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Performance evaluation of a simulated data-flow computer with low-resolution act
Journal of Parallel and Distributed Computing
Metamodeling: a study of approximations in queueing models
Metamodeling: a study of approximations in queueing models
Processor allocation in a multi-ring dataflow machine
Journal of Parallel and Distributed Computing
Analytical modeling and architectural modifications of a dataflow computer
ISCA '87 Proceedings of the 14th annual international symposium on Computer architecture
Performance Analysis of Parallel Processing Systems
IEEE Transactions on Software Engineering
Speedup Versus Efficiency in Parallel Systems
IEEE Transactions on Computers
Data-Driven and Demand-Driven Computer Architecture
ACM Computing Surveys (CSUR)
A multiple processor data flow machine that supports generalized procedures
ISCA '81 Proceedings of the 8th annual symposium on Computer Architecture
Activity routing in a distributed supply chain: Performance evaluation with two inputs
Journal of Network and Computer Applications
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This paper presents analytical results of computation-communication issues in dynamic dataflow architectures. The study is based on a generalized architecture which encompasses all the features of the proposed dynamic dataflow architectures. Based on the idea of characterizing dataflow graphs by their average parallelism, a queueing network model of the architecture is developed. Since the queueing network violates properties required for product from solution, a few approximations have been used. These approximations yield a multi-chain closed queueing network in which the population of each chain is related to the average parallelism of the dataflow graph executed in the architecture. Based on the model, we are able to study the effect on the performance of the system due to factors such as scalability, coarse grain vs. fine grain parallelism, degree of decentralized scheduling of dataflow instructions, and locality.