Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from costly data movement between memory units and processing units, which consumes large amounts of energy and execution cycles. Memory-centric computing systems, i.e., with processing-in-memory (PIM) capabilities, can alleviate this data movement bottleneck. Our goal is to understand the potential of modern general-purpose PIM architectures to accelerate ML training. To do so, we (1) implement several representative classic ML algorithms (namely, linear regression, logistic regression, decision tree, K-Means clustering) on a real-world general-purpose PIM architecture, (2) rigorously evaluate and characterize them in terms of accuracy, performance and scaling, and (3) compare to their counterpart implementations on CPU and GPU. Our evaluation on a real memory-centric computing system with more than 2500 PIM cores shows that general-purpose PIM architectures can greatly accelerate memory-bound ML workloads, when the necessary operations and datatypes are natively supported by PIM hardware. For example, our PIM implementation of decision tree is $27\times$ faster than a state-of-the-art CPU version on an 8-core Intel Xeon, and $1.34\times$ faster than a state-of-the-art GPU version on an NVIDIA A100. Our K-Means clustering on PIM is $2.8\times$ and $3.2\times$ than state-of-the-art CPU and GPU versions, respectively. To our knowledge, our work is the first one to evaluate ML training on a real-world PIM architecture. We conclude with key observations, takeaways, and recommendations that can inspire users of ML workloads, programmers of PIM architectures, and hardware designers & architects of future memory-centric computing systems.

翻译：训练机器学习算法是一项计算密集型过程，由于频繁访问大规模训练数据集，该过程常受限于内存带宽。因此，以处理器为中心的系统（如CPU、GPU）因内存单元与处理单元间高昂的数据移动成本而面临窘境，这种移动消耗大量能量与执行周期。具备处理-内存能力的以内存为中心的计算系统可缓解这一数据移动瓶颈。本研究旨在探索现代通用型PIM架构加速机器学习训练的潜力。为此，我们：（1）在真实通用型PIM架构上实现多种经典机器学习算法（线性回归、逻辑回归、决策树、K均值聚类）；（2）从精度、性能与可扩展性角度严格评估并刻画其特性；（3）与CPU和GPU上的对应实现进行对比。在具有2500余个PIM核心的真实以内存为中心计算系统上的评估表明：当PIM硬件原生支持必要操作与数据类型时，通用型PIM架构可显著加速内存受限的机器学习工作负载。例如，我们的决策树PIM实现性能相较于8核Intel Xeon上最先进的CPU版本提升27倍，较NVIDIA A100上最先进的GPU版本提升1.34倍；K均值聚类的PIM实现性能分别达到最先进CPU与GPU版本的2.8倍与3.2倍。据我们所知，本研究首次在真实PIM架构上评估机器学习训练。我们总结了关键发现、经验与建议，可为机器学习工作负载用户、PIM架构程序员以及未来以内存为中心计算系统的硬件设计者与架构师提供启示。