By Guang R. Gao
This monograph developed from my Ph. D dissertation accomplished on the Laboratory of laptop technological know-how, MIT, in the course of 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 learn effects built over the last 3 years due to the fact I joined McGill University-results dependent upon the foundations in my dissertation. The terminology dataflow smooth ware pipelining has been continually used because 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 circulation graph version as an operational version for concurrent computation. we glance at timing issues for application graph execution on an amazing static dataflow desktop, research the suggestion of pipe lining, and signify its functionality. We speak about balancing suggestions used to rework sure graphs into totally pipelined info move graphs. particularly, we exhibit how optimum balancing of an acyclic information move graph might be formulated as a linear programming challenge for which an optimum resolution exists. As a big end result, we convey the optimum balancing challenge of acyclic info movement graphs is reduceable to a category of linear programming challenge, the web paintings move challenge, for which recognized effective algorithms exist. This outcome disproves the conjecture that such difficulties are computationally hard.
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Additional info for A Code Mapping Scheme for Dataflow Software Pipelining
If a program involves many random array access operations, the overhead of transmitting array values could be high. Furthermore, if random update functions are involved, the storage management may become expensive . Criticism of tree-like array storage structures in dataflow computers is also well-known . 5 A Pipelined Code Mapping Scheme Massive fine-grain parallelism which can be exploited by a dataflow architecture poses challenges, as well as opportunities, for compilers of parallel machines.
The principle of dataflow software pipelining can be further extended. Il1 which defines array X from arrays A, B, C, and A Code Mapping Scheme for Dataflow Software Pipelining 14 • A[n] • • • • A  • (a) A[I] • • A[n] .... A[I] B[n] .... B[l] • • ern] .... C D[n] .... 3: Pipelining of Array Operations D. The core portion of each of several benchmarks of scientific computa- tion that we have studied consists of multiple code blocks, for example, in the order of 10-100 code blocks. 4. In this example there are three input arrays, A B, and C, and an output array Y.
Furthermore, if random update functions are involved, the storage management may become expensive . Criticism of tree-like array storage structures in dataflow computers is also well-known . 5 A Pipelined Code Mapping Scheme Massive fine-grain parallelism which can be exploited by a dataflow architecture poses challenges, as well as opportunities, for compilers of parallel machines. The functionality of data flow graphs relieves the compiler of the burden of solving the low-level scheduling problems which severely restrict conventional vectorizing compilers.
A Code Mapping Scheme for Dataflow Software Pipelining by Guang R. Gao