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The worst-case execution time (WCET) of a task denotes the largest possible execution time for all possible inputs and thus, hardware states. For non-preemptive multitask scheduling, techniques for the static estimation of safe upper bounds have been subject to industrial practice for years. For preemptive scheduling however, the isolated analysis of tasks becomes imprecise as interferences among tasks cannot be considered with sufficient precision. For such scenarios, the cache-related preemption delay (CRPD) denotes a key metric as it reflects the effects of preemptions on the execution behavior of a single task. Until recently, proposals for CRPD analyses were often limited to direct mapped caches or comparably imprecise for k-way set-associative caches.In this paper, we propose how the current best techniques for CRPD analysis, which have only been proposed separately and for different aspects of the analysis can be brought together to construct an efficient CRPD analysis with unique properties. Moreover, along the construction, we propose several different enhancements to the methods employed. We also exploit that in a complete approach, analysis steps are synergetic and can be combined into a single analysis pass solving all formerly separate steps at once. In addition, we argue that it is often sufficient to carry out the combined analysis on basic block bounds, which further lowers the overall complexity. The result is a proposal for a fast CRPD analysis of very high accuracy.

In the past decades, embedded system designers moved from simple, predictable system designs towards complex systems equipped with caches, branch prediction units and speculative execution. This step was necessary in order to fulfill increasing requirements on computational power. Static analysis techniques considering such speculative units had to be developed to allow the estimation of an upper bound of the execution time of a program. This bound is called worst-case execution time (WCET). Its knowledge is crucial to verify whether hard real-time systems satisfy their timing constraints, and the WCET is a key parameter for the design of embedded systems.In this paper, we propose a WCET-driven branch prediction aware optimization which reorders basic blocks of a function in order to reduce the amount of jump instructions and mispredicted branches. We employed a genetic algorithm which rearranges basic blocks in order to decrease the WCET of a program. This enables a first estimation of the possible optimization potential at the cost of high optimization runtimes. To avoid time consuming repetitive WCET analyses, we developed a new algorithm employing integer-linear programming (ILP). The ILP models the worst-case execution path (WCEP) of a program and takes branch prediction effects into account. This algorithm enables short optimization runtimes at slightly decreased optimization results. In a case study, the genetic algorithm is able to reduce the benchmarks' WCET by up to 24.7% whereas our ILP-based approach is able to decrease the WCET by up to 20.0%.

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