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cuda compute unified device architecture RESEARCH PAPER


CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs).The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA compute platform extends from the 1000s of general purpose compute processors featured in our GPU's compute architecture, parallel computing extensions to many popular languages, powerful drop-in accelerated libraries to turn key applications and cloud based compute appliances.

Image convolution with CUDA
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Abstract Convolution filtering is a technique that can be used for a wide array of image processing tasks, some of which may include smoothing and edge detection. In this document we show how a separable convolution filter can be implemented in NVIDIA CUDA

Optimizing matrix transpose in CUDA
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The reader should be familiar with basic CUDA programming concepts such as kernels, threads, and blocks, as well as a basic understanding of the different memory spaces accessible by CUDA threads. A good introduction to CUDA programming is given in the

Optimizing cuda
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Page 1. S05: High Performance Computing with CUDA Optimizing CUDA Mark Harris NVIDIA Developer Technology Page 2. 2 S05: High Performance Computing with CUDA CUDA is fast and efficient CUDA enables efficient use of the massive parallelism of NVIDIA GPUs Direct

Parallel prefix sum (scan) with CUDA
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Abstract Parallel prefix sum, also known as parallel Scan, is a useful building block for many parallel algorithms including sorting and building data structures. In this document we introduce Scan and describe step-by-step how it can be implemented efficiently in NVIDIA

Efficient sparse matrix-vector multiplication on CUDA
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Abstract The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations

NVIDIA CUDA software and GPU parallel computing architecture
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NVIDIA CUDA Software and GPU Parallel Computing Architecture David B. Kirk, Chief Scientist Page 2. NVIDIA Corporation Outline Applications of GPU Computing CUDA Programming Model Overview Programming in CUDA The Basics How to Get Started!

Fast n-body simulation with cuda
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An N-body simulation numerically approximates the evolution of a system of bodies in which each body continuously interacts with every other body. A familiar example is an astrophysical simulation in which each body represents a galaxy or an individual star, and

Particle simulation using cuda
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Particle Simulation using CUDA Page 2. July 2012 Page ii of 12 Document Change History Version Date Responsible Reason for Change 1.0 Sept 19 2007 Simon Green Initial draft 1.1 Nov 3 2007 Simon Green Fixed some

Efficient histogram algorithms for NVIDIA CUDA compatible devices
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Abstract We present two efficient histogram algorithms designed for NVIDIAs compute unified device architecture ( CUDA ) compatible graphics processor units (GPUs). Our algorithm can be used for parallel computation of histograms on large data-sets and for

Automated dynamic analysis of CUDA programs
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ABSTRACT Recent increases in the programmability and performance of GPUs have led to a surge of interest in utilizing them for general-purpose computations. Tools such as NVIDIAs Cuda allow programmers to use a C-like language to code algorithms for

Introducing currennt: The munich open-source cuda recurrent neural network toolkit
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Abstract In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIAs Computed Unified Device Architecture ( CUDA ). CURRENNT supports uni-

Enabling task parallelism in the cuda scheduler
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Abstract General purpose computing on graphics processing units (GPUs) introduces the challenge of scheduling independent tasks on devices designed for data parallel or SPMD applications. This paper proposes an issue queue that merges workloads that would

Efficient random number generation and application using CUDA
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Page 1. Chapter 37 Efficient Random Number Generation and Application Using CUDA Lee Howes Imperial College London David Thomas Imperial College London Monte Carlo methods provide approximate numerical solutions to problems that would be difficult or impossible

Cuda particles
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match code in CUDA 2.0 release Particle systems are a commonly used technique for simulating physical

On implementing graph cuts on cuda
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Abstract The Compute Unified Device Architecture ( CUDA ) has enabled graphics processors to be explicitly programmed as general-purpose shared-memory multi-core processors with a high level of parallelism. In this paper, we present our preliminary results

Accelerating matlab with cuda
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is a powerful tool for prototyping and analysis. MATLAB could be easily extended via MEX files to take advantage of the computational power offered by the latest NVIDIA graphics processor unit (GPU). The graphic processor can be considered as a

Distributed genetic programming on GPUs using CUDA
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Abstract Using of a cluster of Graphics Processing Unit (GPU) equipped computers, it is possible to accelerate the evaluation of individuals in Genetic Programming. Program compilation, fitness case data and fitness execution are spread over the cluster of

Data Parallel Three-Dimensional Cahn-Hilliard Field Equation Simulation on GPUs with CUDA .
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Computational scientific simulations have long used parallel computers to increase their performance. Recently graphics cards have been utilised to provide this functionality. GPGPU APIs such as NVIDIAs CUDA can be used to harness the power of GPUs for

Introducing CURRENNT-the Munich open-source CUDA recurrent neural network toolkit
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Abstract In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIAs Computed Unified Device Architecture ( CUDA ). CURRENNT supports uni-

Discrete cosine transform for 8x8 blocks with CUDA
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Abstract In this whitepaper the Discrete Cosine Transform (DCT) is discussed. The two- dimensional variation of the transform that operates on 8x8 blocks (DCT8x8) is widely used in image and video coding because it exhibits high signal decorrelation rates and can be

Compute unified device architecture ( CUDA ) based finite-difference time-domain (FDTD) implementation
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Abstract Recent developments in the design of graphics processing units (GPUs) have made it possible to use these devices as alternatives to central processor units (CPUs) and perform high performance scientific computing on them. Though several implementations of

Geometric algorithms on CUDA
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Abstract: The recent launch of the NVIDIA CUDA technology has opened a new era in the young field of GPGPU (General Purpose computation on GPUs). This technology allows the design and implementation of parallel algorithms in a much simpler way than previous

CUSVM: A CUDA implementation of support vector classification and regression
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Abstract. This paper presents cuSVM, a software package for high-speed Support Vector Machine (SVM) training and prediction that exploits the massively parallel processing power of Graphics Processors (GPUs). cuSVM is written in NVIDIAs CUDA C-language GPU

Imaging earths subsurface using CUDA
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The main goal of earth exploration is to provide the oil and gas industry with knowledge of the earths subsurface structure to detect where oil can be found and recovered. To do so, large-scale seismic surveys of the earth are performed, and the data recorded undergoes

Hierarchical clustering with cuda /gpu.
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Abstract Graphics processing units (GPUs) are powerful computational devices tailored towards the needs of the 3-D gaming industry for high-performance, real-time graphics engines. Nvidia Corporation provides a programming language called CUDA for general-

General-purpose sparse matrix building blocks using the NVIDIA CUDA technology platform
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Abstract We report on our experience with integrating and using graphics processing units (GPUs) as fast parallel floatingpoint co-processors to accelerate two fundamental computational scientific kernels on the GPU: sparse direct factorization and nonlinear

cuHMM: a CUDA implementation of hidden Markov model training and classification
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Hidden Markov model (HMM) as a sequential classifier has important applications in speech and language processing [Rab89][JM08] and biological sequence analysis [Kro98]. In this project, we analysis the parallelism in the three algorithms for HMM training and

Accelerating braided b+ tree searches on a gpu with cuda
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Abstract. Previous work has shown that using the GPU as a brute force method for SELECT statements on a SQLite database table yields significant speedups. However, this requires that the entire table be selected and transformed from the B-Tree to row-column format. This

High performance computing with CUDA
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Page 1. High Performance Computing with CUDA Massimiliano Fatica NVIDIA Corporation Page 2. GPU Performance History GPUs are massively multithreaded many-core chips Hundreds of cores, thousands of concurrent threads Huge economies of scale Still on aggressive

Implementation of a simple genetic algorithm within the cuda architecture
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The increasing interest of researchers in using low cost GPUs for applications requiring intensive parallel computing is due to the ability of these devices to solve parallelizable problems much faster than traditional sequential processors. The first applications of

CUDA /OpenGL fluid simulation
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Abstract This document describes an NVIDIA CUDA implementation of a simple fluids solver for the Navier-Stokes equations for incompressible flow. The CUDA algorithms are based on Jos Stams FFT-based Stable Fluids system , and we refer the reader to this paper for

GPU acceleration of the long-wave rapid radiative transfer model in WRF using CUDA Fortran
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Abstract. This paper presents the approach and results of porting the Long-Wave Rapid Radiative Transfer Model (RRTM) component of the Weather Research and Forecast (WRF) code to the GPU using CUDA Fortran. After a brief description of the RTTM code,

Stereo imaging with CUDA
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Abstract Stereo Imaging is a powerful yet seldom utilized technique for determining the distance to objects using a pair of camera spaced apart. This is fundamentally the same visual system used by humans and most other animals. The extremely high computational

Realtime Dense Stereo Matching with Dynamic Programming in CUDA .
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Abstract Real-time depth extraction from stereo images is an important process in computer vision. This paper proposes a new implementation of the dynamic programming algorithm to calculate dense depth maps using the CUDA architecture achieving real-time performance

Gpu acceleration of object classification algorithms using nvidia cuda
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Abstract The field of computer vision has become an important part of todays society, supporting crucial applications in the medical, manufacturing, military intelligence and surveillance domains. Many computer vision tasks can be divided into fundamental steps:

Numerical simulation of the complex Ginzburg-Landau equation on GPUs with CUDA
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ABSTRACT The Time Dependent Ginzburg Landau (TDGL) equation models a complex scalar field and is used to study a variety of different physical systems and exhibits phase transitional behaviours that necessitate study using numerical simulation methods. We

Interactive ray tracing with cuda
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Page 1. Interactive Ray Tracing with CUDA David Luebke and Steven Parker NVIDIA Research Page 2. Ray Tracing Rasterization Rasterization For each triangle: Find the pixels it covers For each pixel: compare to closest triangle so far Ray tracing For each pixel: Find the triangles that

Particle swarm optimization within the CUDA architecture
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The increasing interest of researchers in using low cost GPUs for applications requiring intensive parallel computing is due to the ability of these devices to solve parallelizable problems much faster than traditional sequential processors. The first applications of

Accelerating kernel density estimation on the GPU using the CUDA framework
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Abstract The main problem of the kernel density estimation methods is the huge computational requirements, especially for large data sets. One way for accelerating these methods is to use the parallel processing. Recent advances in parallel processing have

cudaBayesreg: Bayesian Computation in CUDA .
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Abstract Graphical processing units are rapidly gaining maturity as powerful general parallel computing devices. The package cudaBayesreg uses GPU oriented procedures to improve the performance of Bayesian computations. The paper motivates the need for devising

What is CUDA
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CUDA Compute Unified Device Architecture General purpose computation on comodity graphics hardware (GPUs) Available for free download from the Nvidia website (drivers and SDK). Availble on Nvidia Geforce 8 and Quadro FX 4600/5600 series of GPUs Nvidia promises

Performance tuning for CUDAaccelerated neighborhood denoising filters
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Abstract Neighborhood denoising filters are powerful techniques in image processing and can effectively enhance the image quality in CT reconstructions. In this study, by taking the bilateral filter and the non-local mean filter as two examples, we discuss their

Benchmarking the NVIDIA 8800GTX with the CUDA Development Platform
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Page 1. Benchmarking the NVIDIA 8800GTX with the CUDA Development Platform Michael McGraw-Herdeg, MIT Douglas , The Aerospace Corporation B. Scott Michel, The Aerospace Corporation 2007 The Aerospace Corporation Page 2. Outline Introduction

Multi-view range image registration using CUDA
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Abstract: In this paper, we propose a real-time and on-line 3D registration system which acquires and registers multiview range images simultaneously. The proposed system implements a 3D registration technique using GPU programming techniques. To register

An introduction to gpu computing and cuda architecture
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Page 1. NVIDIA Corporation 2011 An Introduction to GPU Computing and CUDA Architecture Sarah Tariq, NVIDIA Corporation Page 2. NVIDIA Corporation 2011 GPU Computing GPU: Graphics Processing Unit Traditionally used for real-time rendering High computational density

Cuda supercomputing for the masses: Part 1
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Many people (myself included) have achieved this level of performance and scalability on non-trivial problems by using CUDA (short for Compute Unified Device Architecture) from NVIDIA to program inexpensive multi-threaded GPUs. I purposefully stress programming

High quality dxt compression using cuda
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Abstract DXT is a fixed ratio compression format designed for real-time hardware decompression of textures. While its also possible to encode DXT textures in real-time, the quality of the resulting images is far from the optimal. In this white paper we will overview a

JPEG compression algorithm using CUDA
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Abstract The goal of this project was to explore the potential performance improvements that could be gained through the use GPU processing techniques within the CUDA architecture for JPEG compression algorithm. The choice of compression algorithms as the focus was

Implementing genetic algorithms to CUDA environment using data parallelization
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Computation methods of parallel problem solving using graphic processing units (GPUs) have attracted much research interests in recent years. Parallel computation can be applied to genetic algorithms (GAs) in terms of the evaluation process of individuals in a population.

CUDA Fortran for scientists and engineers
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This document in intended for scientists and engineers who develop or maintain computer simulations and applications in Fortran, and who would like to harness parallel processing power of graphics processing units (GPUs) to accelerate their code. The goal here is to

Implementing fast MRI gridding on GPUs via CUDA
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Abstract Modern graphics processing units (GPUs) have made high-performance SIMD designs available to consumers at commodity prices. This has made them an attractive platform for parallel applications, however developing efficient general-purpose code for

A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment
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ABSTRACT Monte Carlo (MC) method is able to accurately calculate eigenvalues in reactor analysis. Its lengthy computation time can be reduced by general-purpose computing on Graphics Processing Units (GPU), one of the latest parallel computing techniques under

Image and video processing on CUDA : state of the art and future directions
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Abstract:-In the last few years a myriad of computer graphic applications have been developed using standard programming techniques, which are mainly based on multicore general-purpose processors (CPUs) architectures. Due to the rapid turning towards high

A general relativistic evolution code on CUDA architectures
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Abstract I describe the implementation of a finite-differencing code for solving Einsteins field equations on a GPU, and measure speed-ups compared to a serial code on a CPU for different parallelization and caching schemes. Using the most efficient scheme, the (single

Parallelization of the cuckoo search using cuda architecture
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Abstract: Cuckoo Search is one of the recent swarm itelligence metaheuritics. It has been succesfuly applied to a number of optimization problems, but is stil not very well researched. In this paper we present a parallelized version of the Cuckoo Search algorithm. The

Implementation of symmetric dynamic programming stereo matching algorithm using cuda
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Abstract Stereo correspondence is a computationally intensive procedure, real-time depth map generation for high resolution video is beyond the capability of mainstream CPUs available today. Similar to many other vision algorithms, there is a high degree of parallelism

CUDAlevel Performance with Python-level Productivity for Gaussian Mixture Model Applications.
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Abstract Typically, scientists with computational needs prefer to use high-level languages such as Python or MATLAB; however, large computationally-intensive problems must eventually be recoded in a low level language such as C or Fortran by expert programmers

Sparse-matrix-CG-solver in CUDA
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D Michels Proceedings of the 15th Central European 2011 faculty.kfupm.edu.sa Abstract This paper describes the implementation of a parallelized conjugate gradient solver for linear equation systems using CUDAC. Given a real, symmetric and positive definite coefficient matrix and a right-hand side, the parallized cg-solver is able to find a solution for

Online approximate string matching with CUDA
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Abstract Approximate string matching is an important problem in various fields such as natural text searching or when working with large sets of DNA data. We study the bit-parallel approximate string matching algorithms of Baeza-Yates, Navarro and of Hyyr . We

Unified memory in cuda 6.0. a brief overview of related data access and transfer issues
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Abstract This document highlights aspects related to the support and use of unified, or managed, memory in CUDA 6. The discussion provides an opportunity to revisit two other CUDA memory transaction topics: zero-copy memory and unified virtual addressing. The
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