The design of the memory hierarchy is crucial to the performance of high performance computer systems. The incorporation of multiple levels of caches into the memory hierarchy is known to increase the performance of high end machines, but the development of architectural prototypes of various memory hierarchy designs is costly and time consuming. In this paper, we will describe a single pass method used in combination with trace sampling techniques to produce a fast and accurate approach for simulating multiple sizes of caches simultaneously.

There is a wealth of technological alternatives that can be incorporated into a processor design. These include reservation station designs, functional unit duplication, and processor branch handling strategies. The performance of a given design is measured through the execution of application programs and other workloads. Presently, trace driven simulation is the most popular method of processor performance analysis in the development stage of system design. Current techniques of trace driven simulation, however, are extremely slow and expensive. A fast and accurate method for statistical trace sampling of superscalar processors is proposed.

Profiling is a technique of gathering program statistics in order to aid program optimization. In particular, it is an essential component of compiler optimization for the extraction of instruction-level parallelism. Code instrumentation has been the most popular method of profiling. However real-time, interactive, and transaction processing applications suffer from the high execution-time overhead imposed by software instrumentation. This paper suggests the use of hardware dedicated to the task of profiling. The hardware proposed consists of a set of counters, the profile buffer. A profile collection method that combines the use of hardware, the compiler and operating system support is described. Three methods for profile buffer indexing, address-mapping, selective indexing, and compiler indexing are presented that allow this approach to produce accurate profiling information with very little execution slowdown. The profile information obtained is applied to a prominent compiler optimization, namely superblock scheduling. The resulting instruction-level parallelism approaches that obtained through the use of perfect profile information.