algorithms for a self-tuning microprogrammed computer
Manual tuning techniques are widely applied but are generally slow, costly and require a great deal of expertise. This paper addresses the problem of automatically tuning the virtual architecture of a microprogrammed computer by microprogramming techniques. Two algorithms are presented to automate the tuning process. The algorithms are implemented on the same dynamic microprogrammed computer that executes the given application. After execution of the program the algorithms are invoked and a tuning iteration performed on the architecture and the program. Several tuning iterations are performed with different data sets over a period of time to select the optimized architecture. It is demonstrated that individual code segments experience a 2-8 speed improvement over their corresponding non-tuned versions, while the overall execution time of the program is reduced by 30-45\%. The computational requirements of the algorithms are shown to be very modest.
algorithm computation tuning requirements expertise paper automation virtualization architecture process dynamic applications iter data timing select code segmentation experience version