## Posts

Oct, 30

### Optimizing data intensive GPGPU computations for DNA sequence alignment

MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13× faster than the serial CPU version of […]

Oct, 30

### Accelerating molecular modeling applications with graphics processors

Molecular mechanics simulations offer a computational approach to study the behavior of biomolecules at atomic detail, but such simulations are limited in size and timescale by the available computing resources. State-of-the-art graphics processing units (GPUs) can perform over 500 billion arithmetic operations per second, a tremendous computational resource that can now be utilized for general […]

Oct, 30

### Mars: a MapReduce framework on graphics processors

We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of development of web search applications on a large number of commodity CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth, but are […]

Oct, 30

### Exploiting graphics processing units for computational biology and bioinformatics

Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs), the standard workhorses of scientific computing. With the recent release of generalpurpose GPUs and NVIDIA’s GPU programming language, CUDA, graphics engines are being adopted […]

Oct, 30

### High performance direct gravitational N-body simulations on graphics processing units II: An implementation in CUDA

We present the results of gravitational direct N-body simulations using the graphics processing unit (GPU) on a commercial NVIDIA GeForce 8800GTX designed for gaming computers. The force evaluation of the N -body problem is implemented in “Compute Unified Device Architecture” (CUDA) using the GPU to speedup the calculations. We tested the implementation on three different […]

Oct, 30

### Accelerating molecular dynamic simulation on graphics processing units

We describe a complete implementation of all-atom protein molecular dynamics running entirely on a graphics processing unit (GPU), including all standard force field terms, integration, constraints, and implicit solvent. We discuss the design of our algorithms and important optimizations needed to fully take advantage of a GPU. We evaluate its performance, and show that it […]

Oct, 30

### A performance study of general-purpose applications on graphics processors using CUDA

Graphics processors (GPUs) provide a vast number of simple, data-parallel, deeply multithreaded cores and high memory bandwidths. GPU architectures are becoming increasingly programmable, offering the potential for dramatic speedups for a variety of general-purpose applications compared to contemporary general-purpose processors (CPUs). This paper uses NVIDIA’s C-like CUDA language and an engineering sample of their recently […]

Oct, 30

### Rise of the Graphics Processor

The modern graphics processing unit (GPU) is the result of 40 years of evolution of hardware to accelerate graphics processing operations. It represents the convergence of support for multiple market segments: computer-aided design, medical imaging, digital content creation, document and presentation applications, and entertainment applications. The exceptional performance characteristics of the GPU make it an […]

Oct, 30

### Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study

This paper describes designing a parallel GA with GPU computation to solve the quadratic assignment problem (QAP) which is one of the hardest optimization problems in permutation domains. For the parallel method, a multiple-population, coarse-grained GA model was used. Each subpopulation is evolved by a multiprocessor in a GPU (NVIDIA GeForce GTX285). At predetermined intervals […]

Oct, 29

### Parallel Processing of Matrix Multiplication in a CPU and GPU Heterogeneous Environment

GPUs for numerical computations are becoming an attractive alternative in research. In this paper, we propose a new parallel processing environment for matrix multiplications by using both CPUs and GPUs. The execution time of matrix multiplications can be decreased to 40.1% by our method, compared with using the fastest of either CPU only case or […]

Oct, 29

### Ultrasound goes GPU: real-time simulation using CUDA

Despite the increasing adoption of other imaging modalities, ultrasound guidance is widely used for surgical procedures and clinical imaging due to its low cost, non-invasiveness, and real-time visual feedback. Many ultrasound-guided procedures require extensive training and where possible training on simulations should be preferred over patients. Computational resources for existing approaches to ultrasound simulation are […]

Oct, 29

### High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

We implement a high-order finite-element application, which performs the numerical simulation of seismic wave propagation resulting for instance from earthquakes at the scale of a continent or from active seismic acquisition experiments in the oil industry, on a large cluster of NVIDIA Tesla graphics cards using the CUDA programming environment and non-blocking message passing based […]