Understanding the Role of GPGPU-Accelerated SoC-Based ARM Clusters

The last few years saw the emergence of 64-bit ARM SoCs targeted for mobile systems and servers. Mobile-class SoCs rely on the heterogeneous integration of a mix of CPU cores, GPGPU cores, and accelerators, whereas server-class SoCs instead rely on integrating a larger number of CPU cores with no GPGPU support and a number of network accelerators. Previous works, such as the Mont-Blanc project, built their prototype ARM cluster out of mobile-class SoCs and compared their work against x86 solutions. These works mainly focused on the CPU performance. In this paper, we propose a novel ARM-based cluster organization that exploits faster network connectivity and GPGPU acceleration to improve the performance and energy efficiency of the cluster. Our custom cluster, based on Nvidia Jetson TX1 boards, is equipped with 10Gb network interface cards and enables us to study the characteristics, scalability challenges, and programming models of GPGPU-accelerated workloads. We also develop an extension to the Roofline model to establish a visually intuitive performance model for the proposed cluster organization. We compare the GPGPU performance of our cluster with discrete GPGPUs. We demonstrate that our cluster improves both the performance and energy efficiency of workloads that scale well and can leverage the better CPU-GPGPU balance of our cluster. We contrast the CPU performance of our cluster with ARM-based servers that use many CPU cores. Our results show the poor performance of the branch predictor and L2 cache are the bottleneck of server-class ARM SoCs. Furthermore, we elucidate the impact of using 10Gb connectivity with mobile systems instead of traditional, 1Gb connectivity.