使用FSM-SADF建模资源共享

J. Bastos, S. Stuijk, J. Voeten, R. Schiffelers, Johan Jacobs, H. Corporaal
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引用次数: 7

摘要

本文提出了一种捕获平台上应用程序映射的建模方法。该方法基于场景感知数据流(SADF)模型。与相关工作相比,我们用一个正式的SADF模型来表达完整的设计空间。这使我们能够拥有一个紧凑的、可探索的状态空间,它与一个可执行的模型相关联,能够象征性地分析不同映射的定时行为。我们可以为应用程序任务建模不同的绑定,为绑定在共享资源中的任务建模不同的静态顺序调度,以及使用SADF语义自然地捕获资源声明/取消声明。此外,通过使用数据流图的固有属性和有限状态机的动态行为,我们可以对不同级别的流水线进行建模,例如完整的应用程序流水线和应用程序连续执行的交错流水线。模型的大小与应用程序的执行次数无关。由于我们能够在单个SADF模型中捕获所有这些行为,我们可以使用可用的数据流分析,例如最坏情况和最佳情况吞吐量以及死锁自由检查。此外,由于模型独立于分析技术捕获设计空间,因此可以使用不同的探索方法来分析不同的需求集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling resource sharing using FSM-SADF
This paper proposes a modeling approach to capture the mapping of an application on a platform. The approach is based on Scenario-Aware Dataflow (SADF) models. In contrast to the related work, we express the complete design-space in a single formal SADF model. This allows us to have a compact and explorable state-space linked with an executable model capable of symbolically analyzing different mappings for their timing behavior. We can model different bindings for application tasks, different static-orders schedules for tasks bound in shared resources, as well as naturally capturing resource claiming/unclaiming using SADF semantics. Moreover, by using the inherent properties of dataflow graphs and the dynamic behavior of a Finite-State Machine, we can model different levels of pipelining, such as full application pipelining and interleaved pipelining of consecutive executions of the application. The size of the model is independent of the number of executions of the application. Since we are able to capture all this behavior in a single SADF model we can use available dataflow analysis, such as worst-case and best-case throughput and deadlock-freedom checking. Furthermore, since the model captures the design-space independently of the analysis technique, one can use different exploration approaches to analyze different sets of requirements.
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