Heliostat: Harnessing Ray Tracing Accelerators for Page Table Walks
Yuan Feng, Yuke Li, Jiwon Lee, Won Woo Ro, Hyeran Jeon
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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Cited by top-tier papers3
- GRTX: Efficient Ray Tracing for 3D Gaussian-Based RenderingJunseo Lee, Sangyun Jeon, Jungi Lee, Junyong Park et al.HPCA 2026 · 2 citations
- Revelator: Rapid Data Fetching Via System-Software-Guided Hash-Based Speculative Address TranslationKonstantinos Kanellopoulos, Konstantinos Sgouras, Harsh Songara, Andreas Kosmas Kakolyris et al.ISCA 2026
- TTP: A Hardware-Efficient Design for Precise Prefetching in Ray TracingYavuz Selim Tozlu, Anshul Naithani, Huiyang ZhouISCA 2026
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