A WCET Analysis Method for Pipelined Microprocessors with Cache Memories

When constructing real-time systems, safe and tight estimations of the worst case execution time (WCET) of programs are needed. To obtain tight estimations, a common approach is to do path and timing analyses. Path analysis is responsible for eliminating infeasible paths in the program and timing analysis is responsible for accurately modeling the timing behavior of programs. The focus of this thesis is on analysis of programs running on high-performance microprocessors employing pipelining and caching. This thesis presents a new method, referred to as cycle-level symbolic execution, that tightly integrates path and timing analysis. An implementation of the method has been used to estimate the WCET for a suite of programs running on a high-performance processor. The results show that by using an integrated analysis, the overestimation is significantly reduced compared to other methods. The method automatically eliminates infeasible paths and derives path information such as loop bounds, and performs accurate timing analysis for a multiple-issue processor with an instruction and data cache. The thesis also identifies timing anomalies in dynamically scheduled processors. These anomalies can lead to unbounded timing effects when estimating the WCET, which makes it unsafe to use previously presented timing analysis methods. To handle these unbounded timing effects, two methods are proposed. The first method is based on program modifications and the second method relies on using pessimistic timing models. Both methods make it possible to safely use all previously published timing analysis methods even for architectures where timing anomalies can occur. Finally, the use of data caching is examined. For data caching to be fruitful in real-time systems, data accesses must be predictable when estimating the WCET. Based on a notion of predictable and unpredictable data structures, it is shown how to classify program data structures according to their influence on data cache analysis. For both categories, several examples of frequently used types of data structures are provided. Furthermore, it is shown how to make an efficient data cache analysis even when data structures have an unknown placement in memory. This is important, for example, when analyzing single subroutines of a program.