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Technical Perspective: Back to the Edge

"You may fire when you are ready, Gridley," is the famous command from Commodore Dewey in the Battle of Manila Bay, 1898. He may not have realized it, but he was articulating the basic principle of dataflow computing, where an instruction can be executed as soon as its inputs are available. Dataflow has long fascinated computer architects as perhaps a more "natural" way for computation circuits to best exploit parallelism for performance.

A visiting alien may be forgiven for experiencing whiplash when shown how we treat parallelism in programs. Mathematical algorithms have abundant parallelism; the only limit is data dependency (an operator can be evaluated when its inputs are available). We code it in a mainstream programming language (C/C++, Python, among others), which has completely sequential semantics (zero parallelism) to make sense of reads and writes to memory. As illustrated in Figure 1, compilers sweat mightily to rediscover some of the lost parallelism in their internal CDFGs (control and data flow graphs), and then produce machine code that, again, is completely sequential. When we execute this on a modern von Neumann CPU, wide-issue, out-of-order circuits once again sweat mightily (burning power) to rediscover parallelism.


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