high performance computing on graphics processing units: hgpu.org

Cancer is one of the severe diseases causing one in eight deaths worldwide. It can be cured if detected at the very first stage where the cancer cells stay fixed in their area. In stage two it will start to spread. When it spread to muscles enters in third stage. It may cause organ failure. […]

This paper presents a technique to fully automatically generate efficient and readable code for parallel processors. We base our approach on skeleton-based compilation and "algorithmic species", an algorithm classification of program code. We use a tool to automatically annotate C code with species information where possible. The annotated program code is subsequently fed into the […]

General Purpose computing on Graphics Processor Units (GPGPU) brings massively parallel computing (hundreds of compute cores) to the desktop at a reasonable cost, but requires that algorithms be carefully designed to take advantage of this power. The present work explores the possibilities of CUDA (NVIDIA Compute Unified Device Architecture) using GPGPU approach for 2D Triangulation […]

In medical ultrasound, synthetic aperture (SA) imaging is well-considered as a novel image formation technique for achieving superior resolution than that offered by existing scanners. However, its intensive processing load is known to be a challenging factor. To address such a computational demand, this paper proposes a new parallel approach based on the design of […]

Light sheet fluorescence microscopy is able to image large specimen with high resolution by imaging the samples from multiple angles. Multi-view deconvolution can significantly improve the resolution and contrast of the images, but its application has been limited due to the large size of the datasets. Here we present a derivation of multi-view Bayesian deconvolution […]

We consider sequential algorithms for hypergraph partitioning and GPU (i.e., fine-grained shared-memory parallel) algorithms for graph partitioning and clustering. Our investigation into sequential hypergraph partitioning is concerned with the efficient construction of high-quality matchings for hypergraph coarsening and optimisation with respect to general hypergraph partitioning quality metrics. We introduce the l*(l-1)-metric which exactly measures the […]

Graphical Processing Units (GPUs) are known to be excellent computation accelerators. However, their approach to data processing is very different from regular CPUs. This makes it harder for a regular developer to program these devices. In the past few years, several frameworks were introduced to simplify the programming of GPU devices. Accelerate and Obsidian are […]

We record and digitally preserve public monuments by describing them using a generative language. Such generative models allow visualisations of the monuments to be computed from descriptions that are suitable for transmission to personal mobile devices. The intent of using a personal mobile device is to make the cultural heritage experience more personal to the […]

The Curvelet transform is among one of the most powerful time-frequency representations of an image. However, since it is not a fast algorithm it cannot be employed in most real-time and/or large scale applications. This paper proposes a novel algorithm to speed up the Curvelet transform by both optimizing it for repetitive Curvelet usage and […]

Network Intrusion Detection Systems (NIDS) which should perform time-consuming evaluation of every packet received from network have faced throughput challenge as a result of the increase in the speed of network communications and the high volume of Internet threats. In an NIDS, the most important and time-consuming processes are pattern matching and deep inspection of […]

In order to provide efficient software tools to deal with large membrane systems, high-throughput simulators are required. Parallel computing platforms are good candidates, since they are capable of partially implementing the inherently parallel nature of the model. In this concern, today GPUs (Graphics Processing Unit) are considered as highly parallel processors, and they are being […]

We present the Hadoop Online Ray Tracer (HORT), a scalable ray tracing framework for general, pay-as-you-go, cloud computing services. Using MapReduce, HORT partitions the computational workload and scene data differently than other distributed memory ray tracing frameworks. We show that this unique partitioning significantly bounds the data replication costs and inter-process communication. Consequently HORT is […]