论文标题
具有硬件加速的异质平台上实时应用的优化分区和优先分配
Optimized Partitioning and Priority Assignment of Real-Time Applications on Heterogeneous Platforms with Hardware Acceleration
论文作者
论文摘要
硬件加速器，例如基于GPU和FPGA的加速器，为有效地平行功能提供了绝佳的机会。最近，现代嵌入式平台开始配备这种加速器，从而为新兴的，高度计算的密集型工作负载提供了令人信服的选择，例如下一代自动驾驶系统所要求的。除了需要计算效率的需求之外，这些工作负载通常以实时要求为特征，需要满足以确保系统的安全和正确行为。为此，本文提出了一个整体框架，以帮助设计人员在具有硬件加速器的异构平台上的实时应用程序。提出的模型的灵感来自博世（Bosch）在Waters 2019挑战中提出的高级驾驶援助系统的现实设置，进一步概括了更广泛的异质体系结构。对所得的分析进行了线性化并用于编码一个联合（i）保证时间限制的优化问题，（ii）找到合适的任务对核心映射，（iii）对每个任务分配优先级，（iii）选择了哪些计算来加速加速，寻求较小的遇到次数的差距和同步仪，并寻求较小的遇到的次数划分。
Hardware accelerators, such as those based on GPUs and FPGAs, offer an excellent opportunity to efficiently parallelize functionalities. Recently, modern embedded platforms started being equipped with such accelerators, resulting in a compelling choice for emerging, highly computational intensive workloads, like those required by next-generation autonomous driving systems. Alongside the need for computational efficiency, such workloads are commonly characterized by real-time requirements, which need to be satisfied to guarantee the safe and correct behavior of the system. To this end, this paper proposes a holistic framework to help designers partition real-time applications on heterogeneous platforms with hardware accelerators. The proposed model is inspired by a realistic setup of an advanced driving assistance system presented in the WATERS 2019 Challenge by Bosch, further generalized to encompass a broader range of heterogeneous architectures. The resulting analysis is linearized and used to encode an optimization problem that jointly (i) guarantees timing constraints, (ii) finds a suitable task-to-core mapping, (iii) assigns a priority to each task, and (iv) selects which computations to accelerate, seeking for the most convenient trade-off between the smaller worst-case execution time provided by accelerators and synchronization and queuing delays.
