high performance computing on graphics processing units: hgpu.org

The aim of this report to elaborate TiViPE modules that make use of Open Computing Language (OpenCL) programming. OpenCL is available in TiViPE from version 2.1.0. The aim of TiViPE is to integrate different technologies in a seamless way using graphical icons [1]. Due to these icons the user does not need to have in depth knowledge of the underlying hardware architecture [2]. In this report the focus is on how to integrate the Open Computing Language (OpenCL) into TiViPE. OpenCL is an open standard for parallel programming of heterogeneous systems, it has been intitated by Apple and is coordinated by the Khronos group. The aim of OpenCL is to support parallel programming on many different computational devices, including Central Processing Units (CPUs) and Graphical Processing Units (GPUs). The terminology for CPU and GPU becomes somewhat outdated because GPUs can be used as generic processing units also known as GP-GPUs. It is the reason that AMD has redefined its processing units to Accelerated Processing Units (APU), since both CPU and GPU can be used in the same way when using the OpenCL SDK (Softwre Development Kit). Objectively spoken OpenCL mostly benefits if the hardware is massively parallel and for the moment this implies that the GPU processing units are most suitable. The NVIDIA CUDA (Compute Unified Device Architecture) SDK is a competitor of OpenCL. The only drawback of CUDA compared to OpenCL is that only NVIDIA (GPU) hardware is supported. OpenCL has a bunch of restrictions compared to CUDA and TiViPE has put an effort to alleviate the gap between CUDA and OpenCL. Our aim is to make OpenCL and CUDA programming very similar and make modular integration in TiViPE the same. In this document we make a step by step comparison between the 2 technologies using a simple example.