Huff, Rafael ; Neves, Tiago ; Gierlinger, Thomas ; Kuijper, Arjan ; Stork, André ; Fellner, Dieter W. (2010)
OpenCL vs. CUDA for Ray Tracing.
XII Symposium on Virtual and Augmented Reality.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
For many years the Graphics Processing Unit (GPU) of common desktops was just used to accelerate certain parts of the graphics pipeline. After developers had access to the native instruction set and memory of the massive parallel computational elements of GPUs a lot has changed. GPUs became powerful and programmable. Nowadays two SDKs are most used for GPU programming: CUDA and OpenCL. CUDA is the most adopted general purpose parallel computing architecture for GPUs but is restricted to Nvidia graphic cards only. In contrast, OpenCL is a new royalityfree framework for parallel programming intended to be portable across different hardware manufacturers or even different platforms. In this paper, we evaluate both solutions considering a typical parallel algorithm: Ray Tracing. We show our performance results and experiences on developing both implementations that could be easily adapted to solve other problems.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2010 |
| Autor(en): | Huff, Rafael ; Neves, Tiago ; Gierlinger, Thomas ; Kuijper, Arjan ; Stork, André ; Fellner, Dieter W. |
| Art des Eintrags: | Bibliographie |
| Titel: | OpenCL vs. CUDA for Ray Tracing |
| Sprache: | Englisch |
| Publikationsjahr: | 2010 |
| Verlag: | Everton Cavalcante, Brazil |
| Veranstaltungstitel: | XII Symposium on Virtual and Augmented Reality |
| Kurzbeschreibung (Abstract): | For many years the Graphics Processing Unit (GPU) of common desktops was just used to accelerate certain parts of the graphics pipeline. After developers had access to the native instruction set and memory of the massive parallel computational elements of GPUs a lot has changed. GPUs became powerful and programmable. Nowadays two SDKs are most used for GPU programming: CUDA and OpenCL. CUDA is the most adopted general purpose parallel computing architecture for GPUs but is restricted to Nvidia graphic cards only. In contrast, OpenCL is a new royalityfree framework for parallel programming intended to be portable across different hardware manufacturers or even different platforms. In this paper, we evaluate both solutions considering a typical parallel algorithm: Ray Tracing. We show our performance results and experiences on developing both implementations that could be easily adapted to solve other problems. |
| Freie Schlagworte: | Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Ray tracing, Parallel programming, Programmable graphics hardware, Compute Unified Device Architecture (CUDA) |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 04 Feb 2022 12:41 |
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