high performance computing on graphics processing units: hgpu.org

This paper presents efficient implementation of RLS-based adaptive filters with a large number of taps on nVIDIA GeForce graphics processing unit (GPU) and CUDA software development environment. Modification of the order and the combination of calculations reduces the number of accesses to slow off-chip memory. Assigning tasks into multiple threads also takes memory access order […]

Time-of-Flight (ToF) cameras gained a lot of scientific attention and became a vivid field of research in the last years. A still remaining problem of ToF cameras are motion artifacts in dynamic scenes. This paper presents a new preprocessing method for a fast motion artifact compensation. We introduce a ow like algorithm that supports motion […]

Vortex detection and visualization is an important technique for computational fluid dynamics (CFD) modelers and analysts. Since vortices are often not just local phenomena, algorithms for detecting the vortex core can be expanded by the use of streamline placement and termination methodologies to appropriately visualize the vortex. We are enhancing an existing VCDetect software tool […]

Heterogeneous computing on CPUs and GPUs has traditionally used fixed roles for each device: the GPU handles data parallel work by taking advantage of its massive number of cores while the CPU handles non data-parallel work, such as the sequential code or data transfer management. Unfortunately, this work distribution can be a poor solution as […]

This document describes an implementation for GPU and CPU of Young and Van Vliet’s recursive Gaussian smoothing as an external module for the Insight Toolkit ITK, version 4.* www.itk.org. In the absence of an OpenCL-capable platform, the code will run the CPU implementation as an alternative to the existing Deriche recursive Gaussian smoothing filter in […]

The Halton sequence is one of the most popular low-discrepancy sequences. In order to satisfy some practical requirements, the original sequence is usually modified in some way. The scrambling algorithm, proposed by Owen, has several theoretical advantages, but on the other hand is difficult to implement in practice due to the trade-off between high memory […]

We present a new freely available open-source C++ software for numerical solution of the electromagnetic waves absorption and scattering problems within the Discrete Dipole Approximation paradigm. The code is based upon the famous and free Fortan-90 code DDSCAT by B. Draine and P. Flatau. Started as a teaching project, the presented code DDscat.C++ differs from […]

This is a parallelized algorithm performing a decomposition of a noisy time series into a number of frequency components. The algorithm analyses all suspicious periodicities that can be revealed, including the ones that look like an alias or noise at a glance, but later may prove to be a real variation. After selection of the […]

With the ease-of-programming, flexibility and yet efficiency, MapReduce has become one of the most popular frameworks for building big-data applications. MapReduce was originally designed for distributed-computing, and has been extended to various architectures, e,g, multi-core CPUs, GPUs and FPGAs. In this work, we focus on optimizing the MapReduce framework on Xeon Phi, which is the […]

In this paper we discuss ways to reduce the execution time of a software Global Navigation Satellite System (GNSS) receiver that is meant for offline operation in a cloud environment. Client devices record satellite signals they receive, and send them to the cloud, to be processed by this software. The goal of this project is […]

Efficient filtering remains an important challenge in computer graphics, particularly when filters are spatially-varying, have large extent, and/or exhibit complex anisotropic profiles. We present an efficient filtering approach for these difficult cases based on anisotropic filter decomposition (IFD). By decomposing complex filters into linear combinations of simpler, displaced isotropic kernels, and precomputing a compact prefiltered […]

Accelerated and in-core implementations of Big Data applications typically require large amounts of host and accelerator memory as well as efficient mechanisms for transferring data to and from accelerators in heterogeneous clusters. Scheduling for heterogeneous CPU and GPU clusters has been investigated in depth in the high-performance computing (HPC) and cloud computing arenas, but there […]