high performance computing on graphics processing units: hgpu.org

GPUs offer several times the floating point performance and memory bandwidth of current standard two socket CPU servers, e.g. NVIDIA C2070 vs. Intel Xeon Westmere X5650. The lattice Boltzmann method has been established as a flow solver in recent years and was one of the first flow solvers to be successfully ported and that performs […]

More and more 3D terrain information is converted and stored in a digital format, and they have been used as a primary data source for a variety of spatial analyses in various application domains. One unsolved issue is that 3D spatial analyses (e.g., 3D ray tracing) often require huge computing cost in terms of processing […]

The general purpose graphics processing units (GPGPU) are increasingly deployed for scientific computing due to their performance advantages over CPUs. What followed is the fact that fault tolerance has become a more serious concern compared to the period when GPGPUs were used exclusively for graphics applications. Using GPUs and CPUs together in a hybrid computing […]

A simple parallelization approach using General Purpose computation on Graphics Processing Unit was applied for solving the MHD equations using the CIP-MOCCT method. We investigated the efficiency of this parallelization approach and found that the computational speed of the modified code is significantly improved despite the simple modification.

We describe an accurate yet very fast implementation of a visual word generation method by using general purpose graphical processors (GPUs). Visual words have recently proved to be a key tool in image classification. Best performing Pascal VOC and ImageCLEF systems use Gaussian mixtures or k-means clustering to define visual words based on the content-based […]

Global remote sensing and large-scale environmental modeling have generated huge amounts of raster geospatial data. While the inherent data parallelism of large-scale raster geospatial data allows straightforward coarse-grained parallelization at the chunk level on CPUs, it is largely unclear how to effectively exploit such data parallelism on massively parallel General Purpose Graphics Processing Units (GPGPUs) […]

Graphics processing units (GPUs) are becoming increasingly important in today’s platforms as their increased generality allows for them to be used as powerful co-processors. However, unlike standard CPUs, GPUs are treated as I/O devices and require the use of interrupts to facilitate communication with the CPU. Interrupts cause delays in the execution of real-time tasks, […]

A modern personal computer can be now considered as a one-node heterogeneous cluster that simultaneously processes several applications’ tasks. It can be composed by asymmetric Processing Units (PUs), like the multi-core Central Processing Unit (CPU), the many-core Graphics Processing Units (GPUs) – which have become one of the main co-processors that contributed towards high performance […]

Advances in sequencing technologies have revolutionized the field of genomics by providing cost effective and high throughput solutions. In this paper, we develop a parallel sequence assembler implemented on general purpose graphic processor units (GPUs). Our work was largely motivated by a growing need in the genomic community for sequence assemblers and increasing use of […]

The scalability of a query depends on the amount of data that needs to be accessed when computing the answer. This implies three immediate general strategies for improving query performance: decrease the amount of data (including intermediate results) to be accessed by accessing it smarter; decrease the amount by simply reducing the data quantity in […]

In this work, we develop a new parallel implementation of the k-means unsupervised clustering algorithm for commodity graphic processing units (GPUs), and further evaluate the performance of this newly developed algorithm in the task of classifying (in unsupervised fashion) satellite imagery available from Google Maps engine. With the ultimate goal of evaluating the classification precision […]

We propose GROPHECY, a GPU performance projection framework that can estimate the performance benefit of GPU acceleration without actual GPU programming or hardware. Users need only to skeletonize pieces of CPU code that are targets for GPU acceleration. Code skeletons are automatically transformed in various ways to mimic tuned GPU codes with characteristics resembling real […]