Heterogeneous Mobile System-on-Chips (SoCs) containing CPU and GPU cores are becoming prevalent in embedded computing, and they need to execute applications concurrently. However, existing run-time management approaches do not perform adaptive mapping and thread-partitioning of applications while exploiting both CPU and GPU cores at the same time. In this paper, we propose an adaptive mapping and thread-partitioning approach for energy-efficient execution of concurrent OpenCL applications on both CPU and GPU cores while satisfying performance requirements. To start execution of concurrent applications, the approach makes mapping (number of cores and operating frequencies) and partitioning (distribution of threads between CPU and GPU) decisions to satisfy performance requirements for each application. The mapping and partitioning decisions are made by having a collaboration between the CPU and GPU cores’ processing capabilities such that balanced execution can be performed. During execution, adaptation is triggered when new application(s) arrive, or an executing one finishes, that frees cores. The adaptation process identifies a new mapping and thread-partitioning in a similar collaborative manner for remaining applications provided it leads to an improvement in energy efficiency. The proposed approach is experimentally validated on the Odroid-XU3 hardware platform with varying set of applications. Results show an average energy saving of 37%, compared to existing approaches while satisfying the performance requirements.

中文翻译：

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节能异构移动 SoC 的协同适配

包含 CPU 和 GPU 内核的异构移动系统级芯片 (SoC) 在嵌入式计算中变得越来越普遍，它们需要同时执行应用程序。然而，现有的运行时管理方法不能在同时利用 CPU 和 GPU 内核的同时执行应用程序的自适应映射和线程分区。在本文中，我们提出了一种自适应映射和线程分区方法，用于在 CPU 和 GPU 内核上节能执行并发 OpenCL 应用程序，同时满足性能要求。为了开始执行并发应用程序，该方法做出映射（内核数量和操作频率）和分区（CPU 和 GPU 之间的线程分布）决策以满足每个应用程序的性能要求。映射和分区决策是通过在 CPU 和 GPU 内核的处理能力之间进行协作来做出的，以便可以执行平衡的执行。在执行过程中，当新应用程序到达或正在执行的应用程序完成时触发适配，从而释放内核。适应过程以类似的协作方式为剩余的应用程序识别新的映射和线程分区，前提是它可以提高能效。所提出的方法在具有不同应用程序集的 Odroid-XU3 硬件平台上进行了实验验证。结果表明，在满足性能要求的同时，与现有方法相比，平均节能 37%。