This article provides a comprehensive study of OpenCL heterogeneous programming for porting applications to CPU–GPU computing platforms, with a real‐life application for the solidification modeling. The aim is to achieve a flexible workload distribution between available CPU–GPU resources and optimize application performance. Considering the solidification application as a use case, we explore the necessary steps required for (i) adaptation of an application to CPU–GPU platforms, and (ii) mapping the application workload onto the OpenCL programming model. The adaptation is based on a reformulation of steps developed previously for CPU–MIC architectures. The mapping process allows us to utilize OpenCL for harnessing CPU and GPU cores using data parallelism, as well as for the management of available compute devices with task parallelism. The resulting OpenCL code's performance and energy efficiency is experimentally studied for two platforms with powerful GPUs of various generations (with Kepler and Volta architectures). The experiments confirm the performance advantage of using computing resources of both GPUs and CPUs. The achieved benefit depends on the relationship between the computing power of CPUs and GPUs. Moreover, this gain entails the growth of the average power that increases the energy consumed during the application execution.

中文翻译：

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将固化建模移植到 CPU-GPU 平台的 OpenCL 异构编程探索

本文全面研究了用于将应用程序移植到 CPU-GPU 计算平台的 OpenCL 异构编程，以及用于固化建模的实际应用程序。目的是在可用的 CPU-GPU 资源之间实现灵活的工作负载分配并优化应用程序性能。将固化应用程序视为一个用例，我们探讨了 (i) 将应用程序适配到 CPU-GPU 平台以及 (ii) 将应用程序工作负载映射到 OpenCL 编程模型所需的必要步骤。改编基于先前为 CPU-MIC 架构开发的步骤的重新制定。映射过程允许我们利用 OpenCL 来利用数据并行性来利用 CPU 和 GPU 内核，以及管理具有任务并行性的可用计算设备。结果 OpenCL 代码的性能和能源效率在两个平台上进行了实验研究，这些平台具有不同代的强大 GPU（使用 Kepler 和 Volta 架构）。实验证实了同时使用 GPU 和 CPU 的计算资源的性能优势。实现的收益取决于 CPU 和 GPU 的计算能力之间的关系。此外，这种增益会导致平均功率的增加，从而增加应用程序执行期间消耗的能量。实现的收益取决于 CPU 和 GPU 的计算能力之间的关系。此外，这种增益会导致平均功率的增加，从而增加应用程序执行期间消耗的能量。实现的收益取决于 CPU 和 GPU 的计算能力之间的关系。此外，这种增益会导致平均功率的增加，从而增加应用程序执行期间消耗的能量。