By Guang R. Gao
This monograph advanced from my Ph. D dissertation accomplished on the Laboratory of machine technological know-how, MIT, throughout the summer season of 1986. In my dissertation I proposed a pipelined code mapping scheme for array operations on static dataflow architectures. the most addition to this paintings is located in bankruptcy 12, reflecting new study effects constructed over the last 3 years due to the fact that I joined McGill University-results dependent upon the foundations in my dissertation. The terminology dataflow smooth ware pipelining has been continuously used for the reason that e-book of our 1988 paper at the argument-fetching dataflow structure version at McGill college . within the first a part of this e-book we describe the static facts move graph version as an operational version for concurrent computation. we glance at timing issues for software graph execution on an amazing static dataflow machine, research the idea of pipe lining, and symbolize its functionality. We speak about balancing concepts used to remodel sure graphs into totally pipelined information circulation graphs. particularly, we convey how optimum balancing of an acyclic facts stream graph should be formulated as a linear programming challenge for which an optimum resolution exists. As a huge end result, we convey the optimum balancing challenge of acyclic info stream graphs is reduceable to a category of linear programming challenge, the web paintings move challenge, for which recognized effective algorithms exist. This consequence disproves the conjecture that such difficulties are computationally hard.
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Additional info for A Code Mapping Scheme for Dataflow Software Pipelining
This class can be reduced to a class of known network flow programming problems which have practical algorithmic solutions. Hence, the construction of a program to perform such optimization is computationally tractable. 5 we discuss an extension of the balancing techniques to a broader class of data flow graphs. 1 Weighted Data Flow Graphs In the discussion of balancing problems using graph-theoretic terms, it is often convenient to use what is known as a weighted data flow graph. 2: A Weighted Data Flow Graph node u and v (see the definition of a chain in Chapter 2).
2 Timing Considerations Program execution on a dataflow computer is asynchronous-there is no centralized control mechanism to schedule the firing of the instructions. 3From now on the terms data flow program and data flow graph are used interchangeably when no confusion may occur. 9: An Example of Pipelining To study performance, however, it is convenient to associate timing parameters with the static data flow graph model. In this section we introduce two major assumptions about the idealized static dataflow machine described earlier.
2(b) two sets of tokens are pipelined through the graph, and the actors in stages 1 and 3 are enabled and can be executed concurrently. Thus, the two forms of parallelism are fully exploited. The power of fine-grain parallelism in a dataflow computer derives from machine-level programs having large pipelines in which thousands of actors in hundreds of stages are executed concurrently. Each actor in the pipe is activated in a totally data-driven manner, and no explicit sequential control is needed.
A Code Mapping Scheme for Dataflow Software Pipelining by Guang R. Gao