high performance computing on graphics processing units: hgpu.org

We implemented a large-scale cerebellar cortical model composed of more than 100,000 spiking neuron units on a Graphics Processing Unit (GPU). We carried out computer simulations of the model in realtime. We adopted the model to online learning of timing for a humanoid robot.

Achieving accurate interpolation is an important requirement for many signal-processing applications. While nearest-neighbor and linear interpolation methods are popular due to their native GPU support, they unfortunately result in severe undesirable artifacts. Better interpolation methods are known but lack a native GPU support. Yet, a particularly attractive one is prefiltered cubic-spline interpolation. The signal it […]

In this paper the data registration module being a component of semantic simulation engine is shown. An improved implementation of ICP (Iterative Closest Point) algorithm based on GPGPU (General-purpose computing on graphics processing units) is proposed. The main achievement is on-line aliment of two data sets composed of up to 262144 3D points, therefore it […]

Current processor trends of integrating more cores with wider SIMD units, along with a deeper and complex memory hierarchy, have made it increasingly more challenging to extract performance from applications. It is believed by some that traditional approaches to programming do not apply to these modern processors and hence radical new languages must be discovered. […]

Multiple exercise options generalize American-style options as they allow the holder multiple exercise rights and control over the exercise amounts. They arise in both real and financial option applications, such as tolling agreements and swing options which are primarily used in the energy industry. The Forest of Stochastic Meshes is a recently proposed simulation method […]

In advent of multicore era, plain C/C++ programming language can not fully reflect computer architectures any more. Source-to-source transformation helps tailor programs close to contemporary hardwares. We propose a template-based approach to perform transformation for programs with rich static information. The template metaprogramming techniques we present can conduct parallelization and memory hierarchical optimization for specific […]

Four-dimensional Cone Beam Computed Tomography (4D-CBCT) has been developed to provide respiratory phase resolved volumetric imaging in image guided radiation therapy (IGRT). Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. In this work, we propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an […]

We present a convex hull algorithm that is accelerated on commodity graphics hardware. We analyze and identify the hurdles of writing a recursive divide and conquer algorithm on the GPU and divise a framework for representing this class of problems. Our framework transforms the recursive splitting step into a permutation step that is well-suited for […]

This open-source textbook on parallel programming is aimed more on the practical end of things, in that: There is very little theoretical content, such as O() analysis, maximum theoretical speedup, acyclic graphs and so on; Real code is featured throughout; We use the main parallel platforms-OpenMP, CUDA and MPI-rather than languages that at this stage […]

Field Programmable Gate-Arrays (FPGAs) can efficiently implement application specific processors in non-conventional number systems, such as the decimal (Binary-Coded Decimal, or BCD) number system required for accounting accuracy in financial applications. The main purpose of this work is to show that applications requiring several decimal (BCD) operations can be accelerated by a processor implemented on […]

We present an architecture for real-time, online vision systems which enables development and use of complex vision pipelines integrating any number of algorithms. Individual algorithms are implemented using modular plugins, allowing integration of independently developed algorithms and rapid testing of new vision pipeline configurations. The architecture exploits the parallelization of graphics processing units (GPUs) and […]

Regularization of matrix-valued data is of importance in medical imaging, motion analysis and scene understanding. In this report we describe a novel method for efficient regularization of matrix group-valued images. Using the augmented Lagrangian framework we separate the total-variation regularization of matrix-valued images into a regularization and projection steps, both of which are fast and […]