The high efficiency of domain-specific hardware has sparked substantial interest in adopting accelerators in data analytics systems. Among many choices, GPUs and FPGAs thrived as two popular solutions due to their prevalent deployments in cloud data centers. This paper investigates hardware acceleration solutions for aggregation, a critical data analytics operation. Specifically, we implement aggregation with a unified hardware acceleration framework, which trades efficiency for ease of programming and portability, and then further develop hardware-specific optimizations. We evaluate these solutions on three recent computing hardware platforms: a CPU, a GPU, and an FPGA, with metrics that cover both the performance and energy consumption of on-device and end-to-end processing.

翻译：领域专用硬件的高效率引发了在数据分析系统中采用加速器的浓厚兴趣。在众多选择中，GPU和FPGA凭借其在云数据中心中的广泛部署，成为两种流行的解决方案。本文研究了聚合操作（一种关键的数据分析运算）的硬件加速方案。具体而言，我们通过统一硬件加速框架实现聚合，该框架以效率换取编程便捷性和可移植性，并进一步开发了硬件特定的优化。我们在三种近期计算硬件平台（CPU、GPU和FPGA）上评估这些方案，使用的指标涵盖设备内处理和端到端处理的性能及能耗。