论文标题
在存在级别资源的情况下使用执行决策图计算执行时间
Computing Execution Times with eXecution Decision Diagrams in the Presence of Out-Of-Order Resources
论文作者
论文摘要
最差的案例执行时间(WCET)是验证关键实时应用程序的关键组件。然而,即使是最简单的微处理器也可以使用同时获得的资源来实现管道,例如通过获取和内存阶段共享的内存总线。尽管从本质上讲,尽管它们的固定管道非常确定性,但总线可能会导致对内存的顺序访问,因此,正时异常:可以具有全局效应的局部时序效应,但不能轻易构成以估算全局WCET。为了应对这种情况,WCET分析必须产生重要的过度估计,以保留计算时间的安全性或必须明确跟踪所有可能的执行。在后一种情况下,逐步行为的存在导致对有效状态抽象难以设计的管道状态数量的组合爆炸。本文使用执行决策图(XDD)[1]提出了管道中计时的紧凑而精确的表示。我们通过利用XDD的代数属性来展示如何使用XDD沿执行路径对管道状态进行建模。该计算模型允许在控制流程图级别上计算确切的时间行为,并且可以在存在层外总线访问的情况下有效,准确地支持WCET计算。最终在Tacle Benchmark Suite上进行了实验,我们观察到良好的性能,使这种方法适合工业应用。
Worst-Case Execution Time (WCET) is a key component for the verification of critical real-time applications. Yet, even the simplest microprocessors implement pipelines with concurrently-accessed resources, such as the memory bus shared by fetch and memory stages. Although their in-order pipelines are, by nature, very deterministic, the bus can cause out-of-order accesses to the memory and, therefore, timing anomalies: local timing effects that can have global effects but that cannot be easily composed to estimate the global WCET. To cope with this situation, WCET analyses have to generate important over-estimations in order to preserve safety of the computed times or have to explicitly track all possible executions. In the latter case, the presence of out-of-order behavior leads to a combinatorial blowup of the number of pipeline states for which efficient state abstractions are difficult to design. This paper proposes instead a compact and exact representation of the timings in the pipeline, using eXecution Decision Diagram (XDD) [1]. We show how XDD can be used to model pipeline states all along the execution paths by leveraging the algebraic properties of XDD. This computational model allows to compute the exact temporal behavior at control flow graph level and is amenable to efficiently and precisely support WCET calculation in presence of out-of-order bus accesses. This model is finally experimented on the TACLe benchmark suite and we observe good performance making this approach appropriate for industrial applications.