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GPGPU COMPUTING

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Author(s): BOGDAN OANCEA | TUDOREL ANDREI | RALUCA MARIANA DRAGOESCU

Journal: Challenges of the Knowledge Society
ISSN 2068-7796

Volume: 2;
Issue: -;
Start page: 2026;
Date: 2012;
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Keywords: CUDA | Stream | OpenCL | GPU computing | parallel computing

ABSTRACT
Since the first idea of using GPU to general purpose computing, things have evolved over the years and now there are several approaches to GPU programming. GPU computing practically began with the introduction of CUDA (Compute Unified Device Architecture) by NVIDIA and Stream by AMD. These are APIs designed by the GPU vendors to be used together with the hardware that they provide. A new emerging standard, OpenCL (Open Computing Language) tries to unify different GPU general computing API implementations and provides a framework for writing programs executed across heterogeneous platforms consisting of both CPUs and GPUs. OpenCL provides parallel computing using task-based and data-based parallelism. In this paper we will focus on the CUDA parallel computing architecture and programming model introduced by NVIDIA. We will present the benefits of the CUDA programming model. We will also compare the two main approaches, CUDA and AMD APP (STREAM) and the new framwork, OpenCL that tries to unify the GPGPU computing models.

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