high performance computing on graphics processing units: hgpu.org

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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 […]

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 […]

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 […]

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. […]

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 […]

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 […]

While level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. This paper presents a tool for […]

BACKGROUND:The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for […]

Protein sequences with unknown functionality are often compared to a set of known sequences to detect functional similarities. Efficient dynamic programming algorithms exist for this problem, however current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these […]

This paper discusses the potential of graphics processing units (GPUs) in high-dimensional optimization problems. A single GPU card with hundreds of arithmetic cores can be inserted in a personal computer and dramatically accelerates many statistical algorithms. To exploit these devices fully, optimization algorithms should reduce to multiple parallel tasks, each accessing a limited amount of […]

GPUs have moved away from the traditional fixed-function 3D graphics pipeline toward a flexible general-purpose computational engine. Today, GPUs can implement many parallel algorithms directly using graphics hardware. Well-suited algorithms that leverage all the underlying computational horsepower often achieve tremendous speedups. Truly, the GPU is the first widely deployed commodity desktop parallel computer