Static Worst-Case Execution Time (WCET) analysis is a technique to derive upper bounds for the execution times of programs. Such bounds are crucial when designing and verifying real-time systems. A key component for statically deriving safe and tight WCET bounds is information on the possible program flow through the program. Such flow information can be provided manually by user annotations, or automatically by a flow analysis. To make WCET analysis as simple and safe as possible, it should preferably be automatically derived, with no or very limited user interaction.In this paper we present a method for deriving such flow information called abstract execution. This method can automatically calculate loop bounds, bounds for including nested loops, as well as many types of infeasible paths. Our evaluations show that it can calculate WCET estimates automatically, without any user annotations, for a range of benchmark programs, and that our techniques for nested loops and infeasible paths sometimes can give substantially better WCET estimates than using loop bounds analysis only.
In reactive systems, execution is driven by external events to which the system should respond with appropriate actions. Such events can be simple, but systems are often supposed to react to sophisticated situations involving a number of simpler events occurring in accordance with some pattern. A systematic approach to handle this type of systems is to separate the mechanism for detecting composite events from the rest of the application logic.In this paper, we present an event algebra for composite event detection. We show a number of algebraic laws that facilitate formal reasoning, and justify the algebra semantics by showing to what extent the operators comply with intuition. Finally, we present an implementation of the algebra, and identify a large subset of expressions for which detection can be performed with bounded resources.
Caches have become increasingly important with the widening gap between main memory and processor speeds. Small and fast cache memories are designed to bridge this discrepancy. However, they are only effective when programs exhibit sufficient data locality. In addition, caches are a source of unpredictability, resulting in programs sometimes behaving in a different way than expected.Detailed information about the number of cache misses and their causes allows us to predict cache behavior and to detect bottlenecks. Small modifications in the source code may change memory patterns, thereby altering the cache behavior. Code transformations which take the cache behavior into account might result in a high cache performance improvement. However, cache memory behavior is very hard to predict, thus making the task of optimizing and timing cache behavior very difficult.This article proposes and evaluates a new compiler framework that times cache behavior for multitasking systems. Our method explores the use of cache partitioning and dynamic cache locking to provide worst-case performance estimates in a safe and tight way for multitasking systems. We use cache partitioning, which divides the cache among tasks to eliminate inter-task cache interferences. We combine static cache analysis and cache locking mechanisms to ensure that all intra-task conflicts, and consequently, memory access times, are exactly predictable.The results of our experiments demonstrate the capability of our framework to describe cache behavior at compile time. We compare our timing approach with a system equipped with a nonpartitioned but statically locked data cache. Our method outperforms static cache locking for all analyzed task sets under various cache architectures, demonstrating that our fully predictable scheme does not compromise the performance of the transformed programs.
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