WCET-aware Software Based Cache Partitioning for Multi-Task Real-Time Systems

Caches are a source of unpredictability since it is very difficult to predict if a memory access results in a cache hit or miss. In systems running multiple tasks steered by a preempting scheduler, it is even impossible to determine the cache behavior since interrupt-driven schedulers lead to unknown points of time for context switches. Partitioned caches are already used in multi-task environments to increase the cache hit ratio by avoiding mutual eviction of tasks from the cache. For real-time systems, the upper bound of the execution time is one of the most important metrics, called the Worst-Case Execution Time (WCET). In this paper, we use partitioning of instruction caches as a technique to achieve tighter WCET estimations since tasks can not be evicted from their partition by other tasks. We propose a novel WCET-aware cache partitioning algorithm, which determines the optimal partition size for each task with focus on decreasing the system\u27s WCET for a given set of possible partition sizes. Employing this algorithm, we are able to decrease the WCET depending on the number of tasks in a set by up to 34%. On average, reductions between 12% and 19% can be achieved

WCET-driven Branch Prediction aware Code Positioning ∗

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%