1239

Posts

Nov, 1

A control-structure splitting optimization for GPGPU

Control statements in a GPU program such as loops and branches pose serious challenges for the efficient usage of GPU resources because those control statements will lead to the serialization of threads and consequently ruin the occupancy of GPU, that is, the number of threads running concurrently. Unlike traditional vector processing units that are inside […]
Nov, 1

GPU-assisted decoding of video samples represented in the YCoCg-R color space

Although pixel shaders were designed for the creation of programmable rendering effects, they can also be used as generic processing units for vector data. In this paper, attention is paid to an implementation of the YCoCg-R to RGB color space transform, as defined in the H.264/AVC Fidelity Range Extensions, by making use of pixel shaders. […]
Nov, 1

GPGPU: general purpose computation on graphics hardware

The graphics processor (GPU) on today’s commodity video cards has evolved into an extremely powerful and flexible processor. The latest graphics architectures provide tremendous memory bandwidth and computational horsepower, with fully programmable vertex and pixel processing units that support vector operations up to full IEEE floating point precision. High level languages have emerged for graphics […]
Nov, 1

A GPU accelerated storage system

Massively multicore processors, like, for example, Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign […]
Nov, 1

OpenVIDIA: parallel GPU computer vision

Graphics and vision are approximate inverses of each other: ordinarily Graphics Processing Units (GPUs) are used to convert "numbers into pictures" (i.e. computer graphics). In this paper, we propose using GPUs in approximately the reverse way: to assist in "converting pictures into numbers" (i.e. computer vision). The OpenVIDIA project uses single or multiple graphics cards […]
Nov, 1

GPU-ClustalW: Using Graphics Hardware to Accelerate Multiple Sequence Alignment

Molecular Biologists frequently compute multiple sequence alignments (MSAs) to identify similar regions in protein families. However, aligning hundreds of sequences by popular MSA tools such as ClustalW requires several hours on sequential computers. Due to the rapid growth of biological sequence databases biologists have to compute MSAs in a far shorter time. In this paper […]
Nov, 1

GPU Simulation and Rendering of Volumetric Effects for Computer Games and Virtual Environments

Abstract As simulation and rendering capabilities continue to increase, volumetric effects like smoke, fire or explosions will be frequently encountered in computer games and virtual environments. In this paper, we present techniques for the visual simulation and rendering of such effects that keep up with the demands for frame rates imposed by such environments. This […]
Nov, 1

Fast parallel Particle-To-Grid interpolation for plasma PIC simulations on the GPU

Particle-In-Cell (PIC) methods have been widely used for plasma physics simulations in the past three decades. To ensure an acceptable level of statistical accuracy relatively large numbers of particles are needed. State-of-the-art Graphics Processing Units (GPUs), with their high memory bandwidth, hundreds of SPMD processors, and half-a-teraflop performance potential, offer a viable alternative to distributed […]
Nov, 1

GPU Accelerated Smith-Waterman

We present a novel hardware implementation of the double affine Smith-Waterman (DASW) algorithm, which uses dynamic programming to compare and align genomic sequences such as DNA and proteins. We implement DASW on a commodity graphics card, taking advantage of the general purpose programmability of the graphics processing unit to leverage its cheap parallel processing power. […]
Nov, 1

CUDA-Lite: Reducing GPU programming complexity

Abstract. The computer industry has transitioned into multi-core and many-core parallel systems. The CUDA programming environment from NVIDIA is an attempt to make programming many-core GPUs more accessible to programmers. However, there are still many burdens placed upon the programmer to maximize performance when using CUDA. One such burden is dealing with the complex memory […]
Nov, 1

Program optimization space pruning for a multithreaded gpu

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-parallel applications for these platforms, who lack the substantial experience and knowledge needed to maximize their performance. This creates a […]

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