Throughput driven transformations of Synchronous Data Flows for mapping to heterogeneous MPSoCs
Throughput driven transformations of Synchronous Data Flows for mapping to heterogeneous MPSoCs
复制标题
吞吐量驱动的同步数据流转换，用于映射到异构 MPSoC
DOI：
10.1109/samos.2012.6404168
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
G. Ascheid
中科院分区：
文献类型：
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作者：
Anastasia Stulova;R. Leupers;G. Ascheid
由于现代嵌入式系统的能源效率要求，芯片供应商倾向于具有不同类型的处理引擎和非均匀互连结构的多核架构。同时，多个应用程序旨在在具有这种异构体系结构的设备上并发运行。硬件及其用例复杂性的快速增长对软件开发工具提出了新的挑战。为了克服这种复杂性，基于计算模型的方法正变得越来越有前途。同步数据流（SDF）是一种流行的规范形式主义的流应用程序具有固有的并发性质。然而，在原始表示中表达的并行性通常不足以最大限度地利用多核平台的潜力。在本文中，我们提出了一个整体的方法来提高流应用程序的吞吐量，同时将它们映射到异构体系结构。该方法使用的转换，适应SDF中的并行性，根据可用的平台资源。我们使用遗传算法来探索SDF实例，其目标是在目标平台上最大化吞吐量。我们的模型支持架构异构性和多应用场景。实验表明，我们的方法优于其他技术，在大多数的测试情况下，利用一个单一的应用程序上的并行性，并使并发应用程序的优化。
Due to energy efficiency requirements of modern embedded systems, chip vendors are inclined towards multicore architectures with different types of processing engines and non-uniform interconnect fabrics. At the same time multiple applications are intended to run concurrently on the devices with such heterogeneous architectures. This rapid growth in the complexity of the hardware and its use cases imposes new challenges on the software development tools. To overcome this complexity, model of computation based approaches are becoming increasingly promising. Synchronous Data Flow (SDF) is a popular specification formalism for streaming applications with inherently concurrent nature. However, the parallelism expressed in the original representation is often not sufficient to maximally exploit the potential of multicore platforms. In this paper we present a holistic methodology for improving the throughput of streaming applications while mapping them onto heterogeneous architectures. The approach uses transformations that adapt the parallelism in SDF according to available platform resources. We use a genetic algorithm to explore SDF instances with the objective of maximizing throughput on a target platform. Our model supports architecture heterogeneity and multi-application scenarios. The experiments indicate that our approach outperforms other techniques for exploiting parallelism on a single application in most of the test cases and enables concurrent applications optimization.