@article{LuetkeDreimann:2026,
abstract = {With the growing demand for artificial intelligence and other data-intensive applications, the demand for graphics processing units ({GPUs}) has also increased. Even though there are many approaches on multiplexing {GPUs}, none of the approaches known to us enable the operating system to coherently integrate {GPU} resources alongside {CPU} resources into a holistic resource management. Due to the history of {GPUs}, {GPU} drivers are still a large, isolated part within the driver stack of operating systems. This paper aims to conduct a case study on how a multiplexing solution for {GPGPUs} could look like, where the {OS} is able to define scheduling policies for {GPGPU} tasks and manage {GPU} memory. {OS}-controlled {GPU} memory management can especially be helpful for efficient and safe communication between {GPGPU} applications. We will discuss and evaluate the architecture of {MxGPU}, which offers software-based multiplexing of integrated Intel {GPUs}. {MxGPU} has a tiny code base, which is a precondition for formal verification approaches and usage in safety-critical environments. Experiments with our prototype show that {MxGPU} can grant the operating system control over {GPU} resources while allowing more {GPU} sessions. Furthermore, {MxGPU} allows for execution of {GPGPU} tasks with less latency compared to Linux and enables efficient and safe communication between {GPU} applications.},
author = {Lütke Dreimann, Marcel and Spinczyk, Olaf},
date = {2026-03-01},
doi = {10.1016/j.sysarc.2025.103669},
eid = {103669},
entrysubtype = {Journal},
issn = {1383-7621},
journaltitle = {Journal of Systems Architecture},
keywords = {{GPGPU}, Multiplexing, Resource management, Operating system},
shortjournal = {Journal of Systems Architecture},
shorttitle = {{MxGPU}},
title = {{MxGPU}: Efficient and safe communication between {GPGPU} applications in an {OS}-controlled {GPGPU} multiplexing environment},
volume = {172},
}
