Examines recent advances in design, modeling, and implementation of dataflow and multithreaded computers. The text contains reports concerning many of the world's leading projects engaged in the continuing evolution and application of dataflow concepts. It covers the broad range of dataflow principles in program representation -- from language design to processor architecture -- and compiler optimization techniques.
The first section of the book delves into massively parallel distributed memory and multithreaded architecture design, synchronization and pipelined design, and superpipelined data-driven VLSI processors. The next section, on language and programming issues, discusses stream data types, the development of well-structured software, and coarse-grain dataflow programming.
Other parts of the text study parallelization of dataflow programs, an analytical model for the behavior of dataflow graphs, compare a centralized work distribution scheme with a distributed scheme, and present a comprehensive approach to understanding workload management schemes. Altogether, the book introduces the reader to dataflow concepts that show how functional programming ideas can be harnessed to exploit the power of parallel computing.
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Guang R. Gao is a computer scientist and a Professor of Electrical and Computer Engineering at the University of Delaware. Gao is a founder and Chief Scientist of ETI. Lubomir Bic is the author of Advanced Topics in Dataflow Computing and Multithreading, published by Wiley.
Examines recent advances in design, modeling, and implementation of dataflow and multithreaded computers. The text contains reports concerning many of the world's leading projects engaged in the continuing evolution and application of dataflow concepts. It covers the broad range of dataflow principles in program representation -- from language design to processor architecture -- and compiler optimization techniques.
The book includes papers on massively parallel distributed memory and multithreaded architecture design, synchronization and pipelined design, and superpipelined data-driven VLSI processors. Other sections discuss stream data types, the development of well-structured software, and parallelization of dataflow programs It also details and analytical model for the behavior of dataflow graphs, compare a centralized work distribution scheme with a distributed scheme, and present a comprehensive approach to understanding workload management schemes. Altogether, the text introduces the reader to dataflow concepts that show how functional programming ideas can be harnessed to exploit the power of parallel computing.
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