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- Barringer, Rasmus, et al.
(författare)
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A(4): Asynchronous Adaptive Anti-Aliasing using Shared Memory
- 2013
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Ingår i: ACM Transactions on Graphics. - : Association for Computing Machinery (ACM). - 0730-0301 .- 1557-7368. ; 32:4, s. 100-100
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Tidskriftsartikel (refereegranskat)abstract
- Edge aliasing continues to be one of the most prominent problems in real-time graphics, e.g., in games. We present a novel algorithm that uses shared memory between the GPU and the CPU so that these two units can work in concert to solve the edge aliasing problem rapidly. Our system renders the scene as usual on the GPU with one sample per pixel. At the same time, our novel edge aliasing algorithm is executed asynchronously on the CPU. First, a sparse set of important pixels is created. This set may include pixels with geometric silhouette edges, discontinuities in the frame buffer, and pixels/polygons under user-guided artistic control. After that, the CPU runs our sparse rasterizer and fragment shader, which is parallel and SIMD:ified, and directly accesses shared resources (e.g., render targets created by the GPU). Our system can render a scene with shadow mapping with adaptive anti-aliasing with 1 6 samples per important pixel faster than the GPU with 8 samples per pixel using multi-sampling anti-aliasing. Since our system consists of an extensive code base, it will be released to the public for exploration and usage.
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| 2. |
- Barringer, Rasmus, et al.
(författare)
-
Dynamic Stackless Binary Tree Traversal
- 2013
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Ingår i: Journal of Computer Graphics Techniques. - 2331-7418. ; 2:1, s. 38-49
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Tidskriftsartikel (refereegranskat)abstract
- A fundamental part of many computer algorithms involves traversing a binary tree. One notable example is traversing a space-partitioning acceleration structure when computing ray-traced images. Traditionally, the traversal requires a stack to be temporarily stored for each ray, which results in both additional storage and memory-bandwidth usage. We present a novel algorithm for traversing a binary tree that does not require a stack and, unlike previous approaches, works with dynamic descent direction without restarting. Our algorithm will visit exactly the same sequence of nodes as a stack-based counterpart with extremely low computational overhead. No additional memory accesses are made for implicit binary trees. For sparse trees, parent links are used to backtrack the shortest path. We evaluate our algorithm using a ray tracer with a bounding volume hierarchy for which source code is supplied.
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