A Survey of Neural Computation on Graphics Processing Hardware

Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. From machine vision to finite element analysis, CPU\u27s are being used in diverse applications, collectively called general purpose graphics processor utilization. This paper explores the capabilities and limitations of modern GPU\u27s and surveys the neural computation technologies that have been applied to these devices

EMPIRICAL TRANSITION PROBABILITY INDEXING GENOME SEQUENCE ALIGNMENT BASED ON CUDA

After Deoxyribonucleic Acid (DNA) was discovered, finding the similarities in proteins became a fundamental procedure. In recent years, there has been a rapid development in alignment technologies. Alignment is the basic operation used to compare biological sequences and to determine the similarities that eventually result for structural, functional, or biological process relationships. These new technologies produce data in the order of numerous gigabyte-pairs per day. With the use of a Graphics Processing Unit (GPU), these data can be solved. We can utilize a GPU in computation as a massive parallel processor because the GPU consists of multiple pips. This new hardware creates new opportunities to study and improve current algorithms that are used for research in DNA alignment. In this thesis, we proposed a new algorithm to tackle this problem. We matched blocks of reference and target sequences based on the similarities between their empirical transition probabilities matrixes. The computations were conducted on an NVIDIA GTX 760, equipped with 2GB RAM, running Microsoft Windows 8.1 Professional. Our experimental results show robustness in nucleotide sequence alignment, and the parallelized transition probability indexing on a GPU achieves faster results than a former study of a proposed sequential method on a CPU

DCT Implementation on GPU

There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform

Platform Independent Real-Time X3D Shaders and their Applications in Bioinformatics Visualization

Since the introduction of programmable Graphics Processing Units (GPUs) and procedural shaders, hardware vendors have each developed their own individual real-time shading language standard. None of these shading languages is fully platform independent. Although this real-time programmable shader technology could be developed into 3D application on a single system, this platform dependent limitation keeps the shader technology away from 3D Internet applications. The primary purpose of this dissertation is to design a framework for translating different shader formats to platform independent shaders and embed them into the eXtensible 3D (X3D) scene for 3D web applications. This framework includes a back-end core shader converter, which translates shaders among different shading languages with a middle XML layer. Also included is a shader library containing a basic set of shaders that developers can load and add shaders to. This framework will then be applied to some applications in Biomolecular Visualization

Accurate geometry reconstruction of vascular structures using implicit splines

3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy

Brook for GPUs: Stream Computing on Graphics Hardware

In this paper, we present Brook for GPUs, a system for general-purpose computation on programmable graphics hardware. Brook extends C to include simple data-parallel constructs, enabling the use of the GPU as a streaming coprocessor. We present a compiler and runtime system that abstracts and virtualizes many aspects of graphics hardware. In addition, we present an analysis of the effectiveness of the GPU as a compute engine compared to the CPU, to determine when the GPU can outperform the CPU for a particular algorithm. We evaluate our system with five applications, the SAXPY and SGEMV BLAS operators, image segmentation, FFT, and ray tracing. For these applications, we demonstrate that our Brook implementations perform comparably to hand-written GPU code and up to seven times faster than their CPU counterparts