Exploration of OpenCL Heterogeneous Programming for Porting Solidification Modeling to CPU-GPU Platforms

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.