Abstract

Predicting the execution time of code segments in real-time systems is challenging. Most recently designed machines contain pipelines and caches. Pipeline hazards may result in multicycle delays. Instruction or data memory references may not be found in cache and these misses typically require several cycles to resolve. Whether an instruction will stall due to apipeline hazard oracache miss depends on the dynamic sequence of previous instructions executed and memory references performed. Furthermore, these penalties are not independent since delays due to pipeline stalls and cache miss penalties may overlap. This paper describes an approach for bounding the worst and best-case performance of large code segments on machines that exploit both pipelining and instruction caching. First, a method is used to analyze a program’s control flow to statically categorize the caching behavior of each instruction. Next, these categorizations are used in the pipeline analysis of sequences of instructions representing paths within the program. A timing analyzer uses the pipeline path analysis to estimate the worst and best-case execution performance of each loop and function in the program. Finally, agraphical user interface is invoked that allows a user to request timing predictions on portions of the program. The results indicate that the timing analyzer efficiently produces tight predictions of worst and best-case performance for pipelining and instruction caching. Index terms: real-time systems, worst-case execution time, best-case execution time, timing analysis, instruction cache, pipelining

Citations