This paper formulates and shows how to solve the problem of selecting the cache size and depth of cache pipelining that maximizes the performance of a given instruction-set architecture. The solution combines trace-driven architectural simulations and the timing analysis of the physical implementation of the cache. Increasing cache size tends to improve performance but this improvement is limited because cache access time increases with its size. This trade-off results in an optimization problem we referred to as multilevel optimization, because it requires the simultaneous consideration of two levels of machine abstraction: the architectural level and the physical implementation level. The introduction of pipelining permits the use of larger caches without increasing their apparent access time, however, the bubbles caused by load and branch delays limit this technique. In this paper we also show how multilevel optimization can be applied to pipelined systems if software- and hardware-based strategies are considered for hiding the branch and load delays. The multilevel optimization technique is illustrated with the design of a pipelined cache for a high clock rate MIPS-based architecture. The results of this design exercise show that, because processors with pipelined caches can have shorter CPU cycle times and larger caches, a significant performance advantage is gained by using two or three pipeline stages to fetch data from the cache. Of course, the results are only optimal for the implementation technologies chosen for the design exercise; other choices could result in quite different optimal designs. The exercise is primarily to illustrate the steps in the design of pipelined caches using multilevel optimization; however, it does exemplify the importance of pipelined caches if high clock rate processors are to achieve high performance.
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