high performance computing on graphics processing units: hgpu.org

The openIP open source image processing library is a set of c++ libraries providing tools for education, research and industrial purposes. The aim of the development is to fill in the gap between the academic and commercial utilization of image processing. The openIP libraries are interoperable, open source and easy to install. To provide fast […]

In recent years, GPUs have emerged as an extremely cost-effective means for achieving high performance. Many application developers, including those with no prior parallel programming experience, are now trying to scale their applications using GPUs. While languages like CUDA and OpenCL have eased GPU programming for non-graphical applications, they are still explicitly parallel languages. All […]

The STI Cell Broadband Engine (BE) is a highly capable heterogeneous multicore processor with large bandwidth and computing power perfectly suited for numerical simulation. However, all performance benefits come at the price of productivity since more responsibility is put to the programmer. In particular, programming with the IBM Cell SDK is hampered by not only […]

Computer systems are moving towards a heterogeneous architecture with a combination of one or more CPUs and one or more accelerator processors. Such heterogeneous systems pose a new challenge to the parallel programming community. Languages such as OpenCL and CUDA provide a program environment for such systems. However, they focus on data parallel programming where […]

A dense 3-d terrain model obtained using reconstruction methods from aerial images is represented in a probabilistic volumetric framework. The choice of probabilistic representation is to represent inherent ambiguity in reconstruction of surface from images. Such probabilistic representation handles the ambiguity very well but leads to expensive dense volumetric storage. The area coverage required for […]

Graphics Processing Units (GPU) have become the platform of choice for accelerating a large range of data parallel and task parallel applications. Both AMD and NVIDIA have developed GPU implementations targeted at the high performance computing market. The rapid adoption of GPU computing has been greatly aided by the introduction of high-level programming environments such […]

SkePU is a C++ template library that provides a simple and unified interface for specifying data-parallel computations with the help of skeletons on GPUs using CUDA and OpenCL. The interface is also general enough to support other architectures, and SkePU implements both a sequential CPU and a parallel OpenMP backend. It also supports multi-GPU systems. […]

The development of standard processors changed in the last years moving from bigger, more complex, and faster cores to putting several more simple cores onto one chip. This changed also the way programs are written in order to leverage the processing power of multiple cores of the same processor. In the beginning, programmers had to […]

We present SkePU, a C++ template library which provides a simple and unified interface for specifying data-parallel computations with the help of skeletons on GPUs using CUDA and OpenCL. The interface is also general enough to support other architectures, and SkePU implements both a sequential CPU and a parallel OpenMP backend. It also supports multi-GPU […]

With the advent of heterogeneous computing systems consisting of multi-core CPUs and many-core GPUs, robust methods are needed to facilitate fair benchmark comparisons between different systems. In this paper we present a benchmarking methodology for measuring a number of performance metrics for heterogeneous systems. Methods for comparing performance and energy efficiency are included. Consideration is […]

Unquestionably computer architectures have undergone a recent and noteworthy paradigm shift that now delivers multi- and many-core systems with tens to many thousands of concurrent hardware processing elements per workstation or supercomputer node. GPGPU (General Purpose Graphics Processor Unit) technology in particular has attracted significant attention as new software development capabilities, namely CUDA (Compute Unified […]

This work considers the acceleration of matrix-based computational electromagnetic (CEM) techniques using graphics processing units (GPUs). These massively parallel processors have gained much support since late 2006, with software tools such as CUDA and OpenCL greatly simplifying the process of harnessing the computational power of these devices. As with any advances in computation, the use […]