high performance computing on graphics processing units: hgpu.org

New computational architectures, such as multi-core processors and graphics processing units (GPUs), pose challenges to application developers. Although in the case of general-purpose GPU programming, environments and toolkits such as CUDA and OpenCL have simplified application development, different ways of thinking about memory access, storage, and program execution are required. This paper presents a strategy […]

Partial wave analysis is a core tool in hadron spectroscopy. With the high statistics data available at facilities such as the Beijing Spectrometer III, this procedure becomes computationally very expensive. We have successfully implemented a framework for performing partial wave analysis on graphics processors. We discuss the implementation, the parallel computing frameworks employed and the […]

MOTIVATION: Hundreds of genome-wide association studies have been performed over the last decade, but as single nucleotide polymorphism (SNP) chip density has increased so has the computational burden to search for epistasis [for n SNPs the computational time resource is O(n(n-1)/2)]. While the theoretical contribution of epistasis toward phenotypes of medical and economic importance is […]

Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization in cancer diagnosis. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper we present a pipeline that enables interactive visual exploration and semi-automatic segmentation and classification of CEUS data. For […]

Software Distributed Shared Memory (SDSM) systems offer a convenient way to run applications developed for shared memory systems on distributed systems with no changes to them. However, since SDSM systems add an extra layer of abstraction to the memory hierarchy, applications may suffer performance problems when running on top of them. Our main research interest […]

A frequently used method of clustering is a technique called k-means clustering. The k-means algorithm consists of two steps: A map step, which is simple to execute on a GPU, and a reduce step, which is more problematic. Previous researchers have used a hybrid approach in which the map step is computed on the GPU […]

Gaining knowledge out of vast datasets is a main challenge in data-driven applications nowadays. Sparse grids provide a numerical method for both classification and regression in data mining which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. Due to the recursive nature of sparse grid […]

Graphics processing units (GPUs) provide a low cost platform for accelerating high performance computations. The introduction of new programming languages, such as CUDA and OpenCL, makes GPU programming attractive to a wide variety of programmers. However, programming GPUs is still a cumbersome task for two primary reasons: tedious performance optimizations and lack of portability. First, […]

Over recent years, the use of Graphics Processing Units (GPUs) for general-purpose computing has become increasingly popular. The main reasons for this development are the attractive performance/price and performance/power ratios of these architectures. However, substantial performance gains from GPUs come at a price: they require extensive programming expertise and, typically, a substantial re-coding effort. Although […]

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 […]