GPU implementation of a resource-constrained virtual machine

📅 2026-08-17
📈 Citations: 0
Influential: 0
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🤖 AI Summary
This study addresses hardware obsolescence caused by software bloat and GPU performance bottlenecks in resource-constrained virtual machines. We propose deploying the Uxn virtual machine on integrated graphics and introduce an OpenMP-style parallel API based on the Uxntal language. By exploiting data parallelism, this approach enables the efficient execution of frugal computing workloads on general-purpose GPUs. Experimental evaluations demonstrate a 19× speedup on stencil benchmarks and a 7× increase in Bunnymark frame rates. These results effectively validate the feasibility and significant advantages of leveraging GPU acceleration for frugal computing within resource-limited environments, offering a viable solution to extend hardware lifecycle while maintaining computational efficiency.
📝 Abstract
One of the main reasons compute hardware becomes obsolete is software bloat: resource requirements increase for every iteration of a software product. Resource constrained VMs are one way to combat software bloat as they post a hard limit on the resources and so force the programmer to be frugal. In this paper we explore the deployment of one such resource constrained VM, Uxn, on GPU. We show that for competitive performance it is essential to make use of the GPU data parallelism. We present an OpenMP-style parallelism API for Uxntal, the stack-based assembly-style language for the Uxn platform. We demonstrate that exemplar code using our API can run at comparable performance even on an integrated GPU. Specifically, our evaluation results show that using this approach improves performance on the compute-intensive Stencil benchmark with 19x and frame rate on the graphics-intensive Bunnymark benchmark with 7x. In practice, all laptops and desktops and even mobile devices have a GPU and our work shows that they can be used to execute frugal workloads effectively.
Problem

Research questions and friction points this paper is trying to address.

Resource-constrained VM
GPU implementation
Software bloat
Uxn
Innovation

Methods, ideas, or system contributions that make the work stand out.

Resource-constrained VM
GPU parallelism
Uxn
OpenMP-style API
Uxntal
S
Simone Li
University of Glasgow, Glasgow, UK
V
Vladislav Brusokas
University of Glasgow, Glasgow, UK
A
Andrei Ghita
University of Glasgow, Glasgow, UK
S
Shuxuan Li
University of Glasgow, Glasgow, UK
Wim Vanderbauwhede
Wim Vanderbauwhede
Professor in Computing Science, University of Glasgow
Low Carbon and Sustainable ComputingFPGAGPUHPCcompilers