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Heliostat: Harnessing Ray Tracing Accelerators for Page Table Walks

Yuan Feng, Yuke Li, Jiwon Lee, Won Woo Ro, Hyeran Jeon

2025Year
8Citations
3Top-tier citations

Abstract

This paper introduces Heliostat, which enhances page translation bandwidth on GPUs by harnessing underutilized ray tracing accelerators (RTAs).While most existing studies focused on better utilizing the provided translation bandwidth, this paper introduces a new opportunity to fundamentally increase the translation bandwidth.Instead of overprovisioning the GPU memory management unit (GMMU), Heliostat repurposes the existing RTAs by leveraging the operational similarities between ray tracing and page table walks.Unlike earlier studies that utilized RTAs for certain workloads, Heliostat democratizes RTA for supporting any workloads by improving virtual memory performance.Heliostat+ optimizes Heliostat by handling predicted future address translations proactively.Heliostat outperforms baseline and two state-of-the-arts by 1.93×, 1.92×, and 1.66×.Heliostat+ further speeds up Heliostat by 1.23×.Compared to an overprovisioned comparable solution, Heliostat occupies only 1.53% of the area and consumes 5.8% of the power.

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