By Guang R. Gao
This monograph advanced from my Ph. D dissertation accomplished on the Laboratory of computing device technology, 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 on the grounds that I joined McGill University-results dependent upon the rules in my dissertation. The terminology dataflow gentle ware pipelining has been constantly used because book of our 1988 paper at the argument-fetching dataflow structure version at McGill collage . within the first a part of this e-book we describe the static info move graph version as an operational version for concurrent computation. we glance at timing issues for software graph execution on a fantastic static dataflow laptop, learn the concept of pipe lining, and represent its functionality. We talk about balancing concepts used to rework yes graphs into totally pipelined information move graphs. particularly, we convey how optimum balancing of an acyclic information circulate graph will be formulated as a linear programming challenge for which an optimum answer exists. As an incredible outcome, we express the optimum balancing challenge of acyclic information circulate graphs is reduceable to a category of linear programming challenge, the internet paintings circulate challenge, for which famous 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
Under such conditions, the machine supports optimal performance of a balanced data flow graph running in a maximally pipelined fashion. The rest of this monograph assumes such a machine model is being used. What will be the effect if, in a real machine, we consider the variation of the execution time for different types of instructions, or even the same type of instructions due to a different communication delay? 1) to be considered as a bound on the firing time of all instructions. The bound should be chosen such that it can absorb not only the time difference in firing different types of instructions but also the time variations due to the machine computation and communication load.
Obviously, since the first run (initiated by co) is advanced at its maximum rate, it will not block the second run at all; thus, the second run can also be advanced at its maximum rate. Similarly, a new token C2 can be presented to the input arc at t4, etc. Therefore, the graph G can run in a maximally pipelined fashion. 0 38 A Code Mapping Scheme for Dataflow Software Pipelining As a remark, we note that the above proof is based on the partition presented earlier. This is the situation where being a balanced graphplays a key role.
Chapter 6 presents the code mapping scheme for the remaining expressions in PIPVAL, other than expressions built from the two previously mentioned array creation constructs. Chapters 7 and 8 are examine the pipelined code mapping of foraH expressions. Our analysis includes the generation of data flow graphs, as well as the optimization of array operations. Chapter 9 discusses the mapping of for-construct expressions. Chapter 10 is a survey of optimization techniques which can be combined with our basic code mapping schemes.