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Posts

Nov, 4

Parallel Computing Experiences with CUDA

The CUDA programming model provides a straightforward means of describing inherently parallel computations, and NVIDIA’s Tesla GPU architecture delivers high computational throughput on massively parallel problems. This article surveys experiences gained in applying CUDA to a diverse set of problems and the parallel speedups over sequential codes running on traditional CPU architectures attained by executing […]
Nov, 4

Issues and challenges in compiling for graphics processors

Graphics has been one of the best success stories of parallel processing. Using a unique combination of specialized hardware and aspecialized programming model, game developers routinely write high performance code using millions of threads. Each Generation of graphic processors (GPU’s) delivers higher performance and is more programmable then the last. Unlike CPU’s, these processors are […]
Nov, 4

GPUCV: A Framework for Image Processing Acceleration with Graphics Processors

This paper presents a state of the art report on using graphics hardware for image processing and computer vision. Then we describe GPUCV, an open library for easily developing GPU accelerated image processing and analysis operators and applications
Nov, 4

GAMER with out-of-core computation

GAMER is a GPU-accelerated Adaptive-MEsh-Refinement code for astrophysical simulations. In this work, two further extensions of the code are reported. First, we have implemented the MUSCL-Hancock method with the Roe’s Riemann solver for the hydrodynamic evolution, by which the accuracy, overall performance and the GPU versus CPU speed-up factor are improved. Second, we have implemented […]
Nov, 4

Relational joins on graphics processors

We present a novel design and implementation of relational join algorithms for new-generation graphics processing units (GPUs). The most recent GPU features include support for writing to random memory locations, efficient inter-processor communication, and a programming model for general-purpose computing. Taking advantage of these new features, we design a set of data-parallel primitives such as […]
Nov, 4

Approximate Dynamic Programming and Neural Networks on Game Hardware

Modern graphics processing units (GPU) and game consoles are used for much more than simply 3D graphics applications and video games. From machine vision to finite element analysis, GPU’s are being used in diverse applications, collectively called General Purpose computation onf graphics processor units (GPGPU). Additionally, game consoles are entering the market of high performance […]
Nov, 4

GPU processing of particle system animation

An approach to particle system processing on a GPU is discussed. Balancing of CPU and GPU loads is described in detail. Original approaches aimed at reducing the data flow from the system memory to the video memory are proposed. A comparison between the proposed GPU-based approach and the classical CPU-based particle system animation is given.
Nov, 4

Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1-39)

To date, the slowest-folding proteins folded ab initio by all-atom molecular dynamics simulations have had folding times in the range of nanoseconds to microseconds. We report simulations of several folding trajectories of NTL9(1-39), a protein which has a folding time of ~1.5 ms. Distributed molecular dynamics simulations in implicit solvent on GPU processors were used […]
Nov, 4

High-Level programming of graphics hardware to increase performance of electromagnetics simulation

Modern graphics processing units (GPU’s) utilize a programmable parallel pipeline architecture to render complex scenes onto a two-dimensional screen. Rendering these scenes requires rasterization, texturing operations, and multiple stages of lighting operations. These processes are computationally intensive and must be performed near real-time in today’s gaming and workstation applications. These industries have driven the performance […]
Nov, 4

Artificial neural network computation on graphic process unit

Artificial neural network (ANN) is widely used in pattern recognition related area. In some case, the computational load is very heavy, in other case, real time process is required. So there is a need to apply a parallel algorithm on it, and usually the computation for ANN is inherently parallel. In this paper, graphic hardware […]
Nov, 4

Implementing sparse matrix-vector multiplication on throughput-oriented processors

Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations encounter a broad spectrum of matrices ranging from the regular to the highly irregular. Harnessing the tremendous potential of throughput-oriented processors for sparse operations requires that we expose substantial fine-grained parallelism […]
Nov, 4

CuPP – A framework for easy CUDA integration

This paper reports on CuPP, our newly developed C++ framework designed to ease integration of NVIDIAs GPGPU system CUDA into existing C++ applications. CuPP provides interfaces to reoccurring tasks that are easier to use than the standard CUDA interfaces. In this paper we concentrate on memory management and related data structures. CuPP offers both a […]
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