PIMCloud: QoS-Aware Resource Management of Latency-Critical Applications in Clouds with Processing-in-Memory

The slowdown of Moore’s Law, combined with advances in 3D stacking of logic and memory, have pushed architects to revisit the concept of processing-in-memory (PIM) to overcome the memory wall bottleneck. This PIM renaissance finds itself in a very different computing landscape from the one twenty years ago, as more and more computation shifts to the cloud. Most PIM architecture papers still focus on best-effort applications, while PIM’s impact on latency-critical cloud applications is not well understood.This paper explores how datacenters can exploit PIM architectures in the context of latency-critical applications. We adopt a general-purpose cloud server with HBM-based, 3D-stacked logic+memory modules, and study the impact of PIM on six diverse interactive cloud applications. We reveal the previously neglected opportunity that PIM presents to these services, and show the importance of properly managing PIM-related resources to meet the QoS targets of interactive services and maximize resource efficiency. Then, we present PIMCloud, a QoS-aware resource manager designed for cloud systems with PIM allowing colocation of multiple latency-critical and best-effort applications. We show that PIMCloud efficiently manages PIM resources: it (1) improves effective machine utilization by up to 70% and 85% (average 24% and 33%) under 2-app and 3-app mixes, compared to the best state-of-the-art manager; (2) helps latency-critical applications meet QoS; and (3) adapts to varying load patterns.