high performance computing on graphics processing units: hgpu.org

In this paper, we examine the challenges of implementing priority-based task management with respect to userdefined preferential attributes on a graphics processing unit (GPU). Previous approaches usually rely on constant synchronization with the CPU to determine the proper chronological sequence for execution. We transfer the responsibility for evaluating and arranging planned tasks to the GPU […]

Data Mining algorithms such as classification and clustering are the future of computation, though multidimensional data-processing is required. People are using multicore processors with GPU’s. Most of the programming languages doesn’t provide multiprocessing facilities and hence wastage of processing resources. Clustering and classification algorithms are more resource consuming. In this paper we have shown strategies […]

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. […]

With rapidly improving computational power numerical models are being developed for ever more complex problems that cannot be solved analytically, making them more and more computationally intensive. Parallel computing has emerged as an important paradigm to speed up the processing of such models. In recent years graphics processing units (GPU) are among the massively parallel […]

Parallel Computational Fluid Dynamics (ParCFD) Conference 2012 is the 24th of series of annual international meetings since 1989 dedicated to the discussion of most recent developments and applications of parallel computing in the field of CFD and related disciplines. ParCFD conferences are truly multi-cultural and international attracting many researchers across the globe with diverse technical […]

General-Purpose computing on GPUs (GPGPU) provides the opportunity to utilize the tremendous computational power of graphics accelerators for a wider set of problems. These devices leverage massive parallelism to achieve high performance, however, creating highly parallelized code which is optimized for the characteristics of GPUs is no simple task. The polyhedron model is used successfully […]

We consider the long-time behaviour of a free-molecular gas in a time-dependent vessel with absorbing boundary, in any space dimension. We first show, at the theoretical level, that the convergence towards equilibrium heavily depends on the initial data and on the time evolution law of the vessel. Subsequently, we describe a numerical strategy to simulate […]

Today every personal computer and almost every work-related computer has a GPU powerful enough to be used as a supplementary computational device. One framework which enables utilization of this is called OpenCL. We asked the question how one writes efficient algorithms on these GPGPU devices. We found that there are two major ways to run […]

Developing complex technical systems requires a systematic exploration of the given design space in order to identify optimal system configurations. However, studying the effects and interactions of even a small number of system parameters often requires an extensive number of simulation runs. This in turn results in excessive runtime demands which severely hamper thorough design […]

The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization […]

In this paper, we consider the automatic performance tuning of dense vector and matrix-vector operations on GPUs. Such operations form the backbone of level 1 and level 2 routines in the Basic Linear Algebra Subroutines (BLAS) library and are therefore of great importance in many scientific applications. As examples, we develop single-precision CUDA kernels for […]

In this paper, we analyze the trade-offs encountered when minimizing the total execution time using the rake-based applications on GPUs. We use clustering data streams as a case study, and present a rake-based implementation for it, making it more efficient in terms of memory usage. In order to maximize performance for different problem sizes and […]