Collaborative execution of fluid flow simulation using non-uniform decomposition on heterogeneous architectures

The demand for computing power, along with the diversity of computational problems, culminated in a variety of heterogeneous architectures. Among them, hybrid architectures combine different specialized hardware into a single chip, comprising a System-on-Chip (SoC). Since these architectures usually have limited resources, efficiently splitting data and tasks between the different hardware is primal to improve performance. In this context, we explore the non-uniform decomposition of the data domain to improve fluid flow simulation performance on heterogeneous architectures. We evaluate two hybrid architectures: one comprised of a general-purpose x86 CPU and a graphics processing unit (GPU) integrated into a single chip (AMD Kaveri SoC), and another comprised by a general-purpose ARM CPU and a Field Programmable Gate Array (FPGA) integrated into the same chip (Intel Arria 10 SoC). We investigate the effects on performance and energy efficiency of data decomposition on each platform's devices on a collaborative execution. Our case study is the well-known Lattice Boltzmann Method (LBM), where we apply the technique and analyze the performance and energy efficiency of five kernels on both devices on each platform. Our experimental results show that non-uniform partitioning improves the performance of LBM kernels by up to 11.40% and 15.15% on AMD Kaveri and Intel Arria 10, respectively. While AMD's Kaveri platform's performance efficiency is of up to 10.809 MLUPS with an energy efficiency of 142.881 MLUPKJ, Intel's Arria 10 platform's is of up to 1.12 MLUPS and 82.272 MLUPKJ.