high performance computing on graphics processing units: hgpu.org

The scaling of serial algorithms cannot rely on the improvement of CPUs anymore. The performance of classical Support Vector Machine (SVM) implementations has reached its limit and the arrival of the multi core era requires these algorithms to adapt to a new parallel scenario. Graphics Processing Units (GPU) have arisen as high performance platforms to […]

Electrostatic potentials (ESPs) are frequently used in structural biology for the characterization of biomolecules. Here we study the potential employment of hardware accelerators like the graphics processing unit or the application-specific integrated circuit MD-GRAPE-3 for the purpose of efficient computation of ESPs. An algorithm closely coupled to the general description of molecular surfaces is ported […]

Recent advances in graphics processing units (GPUs) technology open a new era in high performance computing. Applications of GPUs to scientific computations are attracting a lot of attention due to their low cost in conjunction with their inherently remarkable performance features and the recently enhanced computational precision and improved programming tools. Domain decomposition methods (DDM) […]

This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the networkpsilas underlying connection pathways. This […]

A novel technique that uses the Moving Least Squares (MLS) method to interpolate sparse constraints over mesh surfaces is introduced in this paper. Given a set of constraints, the proposed technique constructs, directly on the surface, a smooth scalar field that interpolates or approximates the constraints. Three types of constraints: point-value, point-gradient and iso-contour, are […]

We present an extension to the CUDA programming language which extends parallelism to multi-GPU systems and GPU-cluster environments. Following the existing model, which exposes the internal parallelism of GPUs, our extended programming language provides a consistent development interface for additional, higher levels of parallel abstraction from the bus and network interconnects. The newly introduced layers […]

Regularization is a common technique used to improve image quality in inverse problems such as MR image reconstruction. In this work, we extend our previous Graphics Processing Unit (GPU) implementation of MR image reconstruction with compensation for susceptibility-induced field inhomogeneity effects by incorporating an additional quadratic regularization term. Regularization techniques commonly impose the prior information […]

Reduction is a common component of many applications, but can often be the limiting factor for parallelization. Previous reduction work has focused on detecting reduction idioms and parallelizing the reduction operation by minimizing data communications or exploiting more data locality. While these techniques can be useful, they are mostly limited to simple code structures. In […]

Many advanced MRI image acquisition and reconstruction methods see limited application due to high computational cost in MRI. For instance, iterative reconstruction algorithms (e.g. non-Cartesian k-space trajectory, or magnetic field inhomogeneity compensation) can improve image quality but suffer from low reconstruction speed. General-purpose computing on graphics processing units (GPU) have demonstrated significant performance speedups and […]

We propose a fast implementation for iterative MR image reconstruction using Graphics Processing Units (GPU). In MRI, iterative reconstruction with conjugate gradient algorithms allows for accurate modeling the physics of the imaging system. Specifically, methods have been reported to compensate for the magnetic field inhomogeneity induced by the susceptibility differences near the air/tissue interface in […]

We present data layout transformation as an effective performance optimization for memory-bound structuredgrid applications for GPUs. Structured grid applications are a class of applications that compute grid cell values on a regular 2D, 3D or higher dimensional regular grid. Each output point is computed as a function of itself and its nearest neighbors. Stencil code […]

Program optimization for highly parallel systems has historically been considered an art, with experts doing much of the performance tuning by hand. With the introduction of inexpensive, single-chip, massively parallel platforms, more developers will be creating highly data-parallel applications for these platforms while lacking the substantial experience and knowledge needed to maximize application performance. In […]