A Preliminary Study on Simultaneous Coscheduling for Discrete GPU vs. Fused GPU

2026-08-10

AI Summary

This work systematically investigates the trade-offs between performance and programmability in CPU-GPU cooperative scheduling across discrete and unified memory architectures, with a focus on sparse conjugate gradient computations. Evaluations are conducted on both the NVIDIA GH200 Superchip—a platform featuring a unified memory architecture—and the discrete H100 PCIe system, comparing three memory management paradigms: explicit data copies, managed memory, and mapped memory. The study reveals that the GH200’s fused architecture substantially enhances the practicality of managed memory, enabling diverse hybrid task-partitioning strategies to achieve both high performance and programming simplicity. These findings underscore the significant impact of underlying memory architecture on the efficacy of cooperative scheduling approaches.

Abstract

CPU-GPU coscheduling enables simultaneous execution of an application across both processing units, but its efficiency depends on workload partitioning and memory architecture. This preliminary study evaluates coscheduling on the NVIDIA GH200 Superchip compared to a discrete H100 PCIe platform. Using sparse conjugate gradient (CG) as a case study, we assess various work divisions across three memory-management paradigms: explicit copy, managed memory, and mapped memory. Our evaluation highlights the run time and programmability tradeoffs of reducing manual CPU-GPU data movement. The results show that compared with the H100 PCIe platform, GH200 makes several hybrid CPU-GPU work divisions competitive and makes managed memory practical for several matrices. These results suggest that integrated CPU-GPU platforms such as GH200 can improve both performance and programmability for coscheduled workloads.

Problem

• coscheduling
• CPU-GPU
• memory management
• workload partitioning
• integrated GPU

Innovation

• coscheduling
• fused GPU
• managed memory
• sparse conjugate gradient
• CPU-GPU integration