Download Algorithm-Structured Computer Arrays and Networks. by Leonard Uhr, Werner Rheinboldt PDF
By Leonard Uhr, Werner Rheinboldt
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6 A Petri net. Fig. 7 Equivalent McCulloch—Pitts net. Figure 7 shows a similar McCulloch —Pitts net to indicate their close relation. The Petri net's basic construct may be more appropriate for an information-flow net, since a token (which represents a job, or a packet of information) should remain until it is processed. Concurrent processes can be represented conveniently, since tokens can be placed Graphs, Automata, Petri Nets 27 so several nodes fire in parallel. Data-flow computers (Dennis et ai, 1980) and data-flow languages (McGraw, 1980, Arvind et ai, 1978) are designed with this type of data-flow network in mind (although the actual hardware topologies proposed to date to execute data-flow programs do not preserve that data-flow net topology).
But systems with three, four, or more CPUs have given relatively smaller further increases (especially when judged with respect to their added costs). A good deal of parallel hardware has always been built into conventional computers, to have them handle a whole word of information in parallel, and to use the most appropriate special-purpose processor to execute each instruction as quickly as possible. The most powerful supercomputers (and also the most expensive) have also made use of pipelines of assembly-line-like processors and other types of parallelism to further increase performance.
Clearly, to keep the processor busy we must make sure that whatever it needs will be in one of the η memories. Therefore we add hardware to make η as large as possible, and to effect parallel input of new information into that memory. In sharp contrast, consider a special-purpose network of logic gates designed to execute a particular algorithm as efficiently as possible. Examples are Kung's (1980a,b) systolic arrays and also the fast adders and 42 2 Designing, Organizing Multicomputers other carefully designed special-purpose hardware that make up a CPU.