Phoenix -- A Novel Technique for Performance-Aware Orchestration of Thread and Page Table Placement in NUMA Systems

TL;DR

Phoenix is an integrated CPU scheduler and memory manager that optimizes thread and page table placement in NUMA systems, significantly improving performance by reducing CPU and page-walk cycles through coordinated placement and replication strategies.

Contribution

It introduces a novel integrated approach combining CPU scheduling and memory management with on-demand page table replication for NUMA systems.

Findings

Reduces CPU cycles by 2.09x

Decreases page-walk cycles by 1.58x

Improves application performance on NUMA hardware

Abstract

The emergence of symmetric multi-processing (SMP) systems with non-uniform memory access (NUMA) has prompted extensive research on process and data placement to mitigate the performance impact of NUMA on applications. However, existing solutions often overlook the coordination between the CPU scheduler and memory manager, leading to inefficient thread and page table placement. Moreover, replication techniques employed to improve locality suffer from redundant replicas, scalability barriers, and performance degradation due to memory bandwidth and inter-socket interference. In this paper, we present Phoenix, a novel integrated CPU scheduler and memory manager with on-demand page table replication mechanism. Phoenix integrates the CPU scheduler and memory management subsystems, allowing for coordinated thread and page table placement. By differentiating between data and page table pages, Phoenix enables direct migration or replication of page tables based on application behavior. Additionally, Phoenix employs memory bandwidth management mechanism to maintain Quality of Service (QoS) while mitigating coherency maintenance overhead. We implemented Phoenix as a loadable kernel module for Linux, ensuring compatibility with legacy applications and ease of deployment. Our evaluation on real hardware demonstrates that Phoenix reduces CPU cycles by 2.09x and page-walk cycles by 1.58x compared to state-of-the-art solutions.