high performance computing on graphics processing units: hgpu.org

In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent […]

Parallel and heterogeneous computing are growing in audience thanks to the increased performance brought by ubiquitous manycores and GPUs. However, available programming models, like OPENCL or CUDA, are far from being straightforward to use. As a consequence, several automated or semi-automated approaches have been proposed to automatically generate hardware-level codes from high-level sequential sources. Polyhedral […]

Mining the increasing amount of genomic data requires having very efficient tools. Increasing the efficiency can be obtained with better algorithms, but one could also take advantage of the hardware itself to reduce the application runtimes. Since a few years, issues with heat dissipation prevent the processors from having higher frequencies. One of the answers […]

We present a framework for representing image processing kernels based on decoupled access/execute metadata, which allow the programmer to specify both execution constraints and memory access pattern of a kernel. The framework performs source-to-source translation of kernels expressed in highlevel framework-specific C++ classes into low-level CUDA or OpenCL code with effective device-dependent optimizations such as […]

GPU-based computing has become one of the popular high performance computing fields. The field is called GPGPU. This paper is focused on design and implementation of a uniform GPGPU application that is optimized for both the legacy and the recent GPU architectures. As a typical example of such the GPGPU application, this paper will discuss […]

Automatic parallelization is becoming more important as parallelism becomes ubiquitous. The first step for achieving automation is to develop a theoretical foundation, for example, the polyhedron model. The second step is to implement the algorithms studied in the theoretical framework and getting them to work in a compiler that can be used to parallelize real […]

Large heterogeneous data centers of today lack methods to appraise the best fitting solutions regarding, among others, hardware acquisition cost, development time, and performance. Especially resource intensive applications benefit from increased data center utilization to leverage heterogeneous resources and accelerators. In this paper, we implement various methods to accelerate a seismic modeling application, which is […]

Over recent years, using Graphics Processing Units (GPUs) has become as an effective method for increasing the performance of many applications. However, these performance benefits from GPUs come at a price. Firstly extensive programming expertise and intimate knowledge of the underlying hardware are essential for gaining good speedups. Secondly, the expressibility of GPU-based programs are […]

This project was done with the Mobile Microprocessor Group at Intel Corporation as a part of a six month internship. The primay objective of this project was to study the performance of GPGPUs (General purpose computation on Graphics Processing Units) for various benchmark applications. GPGPUs have gained wide spread importance in recent years because of […]

Using hardware acceleration via General Programming on stock GPUs (GPGPU), I’ve sped up my algorithms by more than tenfold. This article shows how you can achieve these results too! Programmers have been interested in leveraging the highly parallel processing power of video cards to speed up applications that are not graphic in nature for a […]

Bioinspired methods usually need a high amount of computational resources. For this reason, parallelization is an interesting alternative in order to decrease the execution time and to provide accurate results. In this sense, recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also refered as GPU computation. Advances […]

General Purpose Computation on Graphics Processing Units (GPGPU) makes it possible to use the massive computing power of modern graphics cards for generic high-performance computing. However, the new virtualization technologies will typically not support high-performance graphics cards and as a consequence GPGPU resources can not be used in typical virtualization setups. In this paper we […]