Towards Fully-fledged GPU Multitasking via Proactive Memory Scheduling

TL;DR

This paper introduces MSched, an OS-level GPU scheduler that predicts and proactively manages memory working sets, significantly reducing page fault overhead and enabling efficient multitasking on GPUs with limited memory capacity.

Contribution

The paper presents a novel proactive memory scheduling approach that predicts GPU working sets with high accuracy and coalesces page faults, improving GPU multitasking performance.

Findings

MSched outperforms demand paging by up to 11.05x on scientific and deep learning workloads.

Achieves up to 57.88x speedup for LLM workloads under memory oversubscription.

Proactive scheduling reduces page fault handling overhead significantly.

Abstract

The limited HBM capacity has become the primary bottleneck for hosting an increasing number of larger-scale GPU tasks. While demand paging extends capacity via host DRAM, it incurs up to 78x slowdown due to the massive working sets and poor locality of GPU workloads. We observe, however, that GPU memory access patterns are inherently predictable via kernel launch arguments and their asynchronous execution nature. Leveraging this, we propose MSched, an OS-level scheduler that extends GPU context switching to include proactive working set preparation, thereby coalescing fragmented, eventual, and expensive page faults into a single efficient migration. MSched employs a template-based approach to predict working sets with near-perfect accuracy and proposes a co-design between task scheduler and memory manager to enforce a globally optimal page placement policy. Evaluation demonstrates that MSched outperforms demand paging by up to 11.05x for scientific and deep learning workloads, and 57.88x for LLM under memory oversubscription.