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OpenCL vs. CUDA for Ray Tracing

Huff, Rafael and Neves, Tiago and Gierlinger, Thomas and Kuijper, Arjan and Stork, André and Fellner, Dieter W. (2010):
OpenCL vs. CUDA for Ray Tracing.
p. 4, Everton Cavalcante, Brazil, XII Symposium on Virtual and Augmented Reality, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2010
Creators: Huff, Rafael and Neves, Tiago and Gierlinger, Thomas and Kuijper, Arjan and Stork, André and Fellner, Dieter W.
Title: OpenCL vs. CUDA for Ray Tracing
Language: English
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.

Publisher: Everton Cavalcante, Brazil
Uncontrolled Keywords: Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Ray tracing, Parallel programming, Programmable graphics hardware, Compute Unified Device Architecture (CUDA)
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: XII Symposium on Virtual and Augmented Reality
Date Deposited: 12 Nov 2018 11:16
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