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个计算密集型过程，由于需要反复访问大规模训练数据集，这一过程常常受限于内存瓶颈。因此，以处理器为中心的计算系统（如CPU、GPU）会因内存单元与处理单元之间的高代价数据移动而消耗大量能量和执行周期。具有处理中内存（PIM）能力的内存计算系统能够缓解这一数据移动瓶颈。我们的目标是理解现代通用PIM架构在加速机器学习训练方面的潜力。为此，我们（1）在真实的通用PIM架构上实现了多种代表性经典机器学习算法（即线性回归、逻辑回归、决策树、K均值聚类），（2）从准确性、性能和扩展性方面对这些算法进行了严格评估和特征分析，（3）并与它们在CPU和GPU上的对应实现进行了对比。我们在拥有超过2500个PIM核心的真实内存计算系统上进行的评估表明：当PIM硬件原生支持必要的操作和数据类型时，通用PIM架构能够显著加速内存密集型机器学习工作负载。例如，我们的PIM决策树实现比8核Intel Xeon上的最新CPU版本快27倍，比NVIDIA A100上的最新GPU版本快1.34倍；我们的PIM K均值聚类实现分别比最新CPU和GPU版本快2.8倍和3.2倍。据我们所知，我们的工作是首个在真实PIM架构上评估机器学习训练的研究。最后，我们提出了关键观察、结论和建议，可为机器学习工作负载用户、PIM架构程序员以及未来内存计算系统的硬件设计者与架构师提供启发。