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    Dual Streaming for Hardware-Accelerated Ray Tracing

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    Date
    2017
    Author
    Shkurko, Konstantin
    Grant, Tim
    Kopta, Daniel
    Mallett, Ian
    Yuksel, Cem ORCID
    Brunvand, Erik
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    Abstract
    Hardware acceleration for ray tracing has been a topic of great interest in computer graphics. However, even with proposed custom hardware, the inherent irregularity in the memory access pattern of ray tracing has limited its performance, compared with rasterization on commercial GPUs. We provide a different approach to hardware-accelerated ray tracing, beginning with modifying the order of rendering operations, inspired by the streaming character of rasterization. Our dual streaming approach organizes the memory access of ray tracing into two predictable data streams. The predictability of these streams allows perfect prefetching and makes the memory access pattern an excellent match for the behavior of DRAM memory systems. By reformulating ray tracing as fully predictable streams of rays and of geometry we alleviate many long-standing problems of high-performance ray tracing and expose new opportunities for future research. Therefore, we also include extensive discussions of potential avenues for future research aimed at improving the performance of hardware-accelerated ray tracing using dual streaming.
    BibTeX
    @inproceedings {10.1145:3105762.3105771,
    booktitle = {Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},
    editor = {Vlastimil Havran and Karthik Vaiyanathan},
    title = {{Dual Streaming for Hardware-Accelerated Ray Tracing}},
    author = {Shkurko, Konstantin and Grant, Tim and Kopta, Daniel and Mallett, Ian and Yuksel, Cem and Brunvand, Erik},
    year = {2017},
    publisher = {ACM},
    ISSN = {2079-8679},
    ISBN = {978-1-4503-5101-0},
    DOI = {10.1145/3105762.3105771}
    }
    URI
    http://dx.doi.org/10.1145/3105762.3105771
    https://diglib.eg.org:443/handle/10.1145/3105762-3105771
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA