high performance computing on graphics processing units: hgpu.org

In a high performance multiple-input multiple-output (MIMO) system, a soft output MIMO detector combined with a channel decoder is often used at the receiver to maximize performance gain. Graphic processor unit (GPU) is a low-cost parallel programmable co-processor that can deliver extremely high computation throughput and is well suited for signal processing applications. We propose […]

Support Vector Machine (SVM) is one of the most popular tools for solving general classification and regression problems because of its high predicting accuracy. However, the training phase of nonlinear kernel based SVM algorithm is a computationally expensive task, especially for large datasets. In this paper, we propose an intelligent system to solve large classification […]

Quantitative SPECT and PET is not possible without accurate modeling of Compton scatter. The physics of this interaction is well-understood and Monte Carlo and analytical calculations are possible. However, such approaches require exorbitant computing times that limit their practical value in the clinical setting. We present a novel computational model that considerably reduces the computation […]

Adaptively Sampled Distance Fields (ADFs) are volumetric shape representations that support a broad range of applications in the areas of computer graphics, computer vision and physics. ADFs are especially beneficial for representing shapes with features at very diverse scales. In this paper, we propose a strategy to represent and reconstruct ADFs on modern graphics hardware […]

The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernel-based algorithms and image processing. General-purpose GPUs provide similar massive parallelism, but it can be difficult to design algorithms to make optimal use of the hardware. The presented research includes a GPU abstraction based on cellular neural networks. The abstraction […]

An algorithm for evolving neural network via the genetic algorithm based on GPU parallel architecture was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary) and was used on gender face recognition. By using the powerful ability of GPU parallel computing, CuParcone achieves a performance increase about 323 […]

Spatial-multiplex autostereoscopic systems display images interleaved from multi-view to provide adequate viewing zone sections for multiple users. The traditional methods render multiple views in multiple passes. In this paper, we present a GPU-based multi-view render (GBMVR) for spatial-multiplex autostereoscopic displays. It generates multiple views as textures in only one pass through the geometry shader and […]

Researchers built the EcoG GPU-based cluster to show that a system can be designed around GPU computing and still be power efficient.

The simulation of diffusion by Monte Carlo methods is often essential to describing NMR measurements of diffusion in porous media. However, simulation timescales must often span hundreds of milliseconds, with large numbers of trajectories required to ensure statistical convergence. Here we demonstrate that by parallelising code to run on graphics processing units (GPUs), these calculations […]

Massive convolution is the basic operation in multichannel acoustic signal processing. This field has experienced a major development in recent years. One reason for this has been the increase in the number of sound sources used in playback applications available to users. Another reason is the growing need to incorporate new effects and to improve […]

The MD4-family algorithms have been widely applied in cryptographic field. Nowadays, it is discovered that MD4-family algorithms are also suitable for random number generators. Since the MD4-family algorithms are computing intensive, they can be accelerated on Graphics Processing Units (GPUs) to generate massive high-quality random numbers. This paper presents acceleration of MD4-family algorithms based on […]

Volume reconstruction by backprojection is the computational bottleneck in many interventional clinical computed tomography (CT) applications. Today vendors in this field replace special purpose hardware accelerators by standard hardware like multicore chips and GPGPUs. This paper presents low-level optimizations for the backprojection algorithm, guided by a thorough performance analysis on four generations of Intel multicore […]