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A compilation flow for parametric dataflow: Programming model, scheduling, and application to heterogeneous MPSoC

机译:参数数据流的编译流:编程模型,调度和对异构MPSoC的应用

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Efficient programming of signal processing applications on embedded systems is a complex problem. High level models such as Synchronous dataflow (SDF) have been privileged candidates for dealing with this complexity. These models permit to express inherent application parallelism, as well as analysis for both verification and optimization. Parametric dataflow models aim at providing sufficient dynamicity to model new applications, while at the same time maintaining the high level of analyzability needed for efficient real life implementations. This paper presents a new compilation flow that targets parametric dataflows. Built on the LLVM compiler infrastructure, it offers an actor based C++ programming model to describe parametric graphs, a compilation front-end providing graph analysis features, and a retargetable back-end to map the application on real hardware. This paper gives an overview of this flow, with a specific focus on scheduling. The crucial gap between dataflow models and real hardware on which actor firing is not atomic, as well as the consequences on FIFOs sizing and execution pipelining are taken into account. The experimental results illustrate our compilation flow applied to compilation of 3GPP LTE-Advanced demodulation on a heterogeneous MPSoC with distributed scheduling features. This achieves performances similar to time-consuming hand made optimizations.
机译:嵌入式系统上的信号处理应用的高效编程是一个复杂的问题。诸如同步数据流(SDF)之类的高级模型已经是处理这种复杂性的特权候选者。这些模型允许表达固有的应用程序并行性,以及验证和优化的分析。参数数据流模型旨在提供足够的动态性以模拟新应用,同时保持高效现实生活实现所需的高水平的分析性。本文介绍了针对参数数据流的新编译流。它构建在LLVM编译器基础架构上,它提供了基于演员的C ++编程模型来描述参数图,汇编前端提供图形分析功能,以及将应用程序映射到实际硬件上的应用程序。本文概述了这一流程,具体关注调度。参与者射击不是原子的数据流模型与实际硬件之间的关键差距以及对FIFOS尺寸和执行流水线的后果。实验结果说明了我们的编译流程,应用于使用分布式调度特征的异构MPSOC对3GPP LTE-Advanced解调的编译。这实现了类似于耗时的手工优化的性能。

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