To overcome of the high cost of developing IoT (Internet of Things) services by vertically integrating devices and services, Open IoT has been developed to enable various IoT services to be developed by integrating horizontally separated devices and services. For Open IoT, we have proposed Tacit Computing technology to discover the devices that can provide the data users need on demand and use them dynamically. We have also proposed an automatic GPU (graphics processing unit) offloading method as an elementary technology of Tacit Computing. However, our GPU offloading method can improve only a limited number of applications because it only optimizes the extraction of parallelizable loop statements. Therefore, in this paper, to improve performances of more applications automatically, we propose an improved GPU offloading method with fewer data transfers between the CPU and GPU that can improve performance of many IoT applications. We evaluate our proposed GPU offloading method by applying it to Darknet and Fourier Transform, which are general large applications for CPU, and find that it can process them 3 times and 5 times as quickly as only using CPUs within 10-hour tuning time.

中文翻译：

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物联网应用GPU自动卸载的并行处理区域提取和数据传输数量减少研究

为了克服通过垂直集成设备和服务开发物联网（IoT）服务的高成本，开发了开放物联网，通过集成水平分离的设备和服务来开发各种物联网服务。对于开放物联网，我们提出了隐性计算技术来发现可以按需提供用户所需数据的设备并动态使用它们。我们还提出了一种自动 GPU（图形处理单元）卸载方法作为隐性计算的基本技术。然而，我们的 GPU 卸载方法只能改进有限数量的应用程序，因为它只优化了可并行循环语句的提取。因此，在本文中，为了自动提高更多应用程序的性能，我们提出了一种改进的 GPU 卸载方法，在 CPU 和 GPU 之间传输更少的数据，可以提高许多物联网应用程序的性能。我们通过将我们提出的 GPU 卸载方法应用于暗网和傅立叶变换（CPU 的一般大型应用程序）来评估我们提出的 GPU 卸载方法，并发现在 10 小时的调整时间内，它的处理速度是仅使用 CPU 的 3 倍和 5 倍。