## Posts

Oct, 30

### Mapping computational concepts to GPUs

Recently, graphics processors have emerged as a powerful computational platform. A variety of encouraging results, mostly from researchers using GPUs to accelerate scientific computing and visualization applications, have shown that significant speedups can be achieved by applying GPUs to data-parallel computational problems. However, attaining these speedups requires knowledge of GPU programming and architecture.The preceding chapters […]

Oct, 30

### Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation

Modern videogames place increasing demands on the computational and graphical hardware, leading to novel architectures that have great potential in the context of high performance computing and molecular simulation. We demonstrate that Graphical Processing Units (GPUs) can be used very efficiently to calculate two-electron repulsion integrals over Gaussian basis functionsthe first step in most quantum […]

Oct, 30

### A Memory Model for Scientific Algorithms on Graphics Processors

We present a memory model to analyze and improve the performance of scientific algorithms on graphics processing units (GPUs). Our memory model is based on texturing hardware, which uses a 2D block-based array representation to perform the underlying computations. We incorporate many characteristics of GPU architectures including smaller cache sizes, 2D block representations, and use […]

Oct, 30

### Accelerating Density Functional Calculations with Graphics Processing Unit

An algorithm is presented for graphics processing units (GPUs), which execute single-precision arithmetic much faster than commodity microprocessors (CPUs), to calculate the exchange-correlation term in ab initio density functional calculations. The algorithm was implemented and applied to two molecules, taxol and valinomycin. The errors in the total energies were about 10−5 a.u., which is accurate […]

Oct, 30

### A hardware redundancy and recovery mechanism for reliable scientific computation on graphics processors

General purpose computation on graphics processors (GPGPU) has rapidly evolved since the introduction of commodity programmable graphics hardware. With the appearance of GPGPU computation-oriented APIs such as AMD’s Close to the Metal (CTM) and NVIDIA’s Compute Unified Device Architecture (CUDA), we begin to see GPU vendors putting financial stakes into this non-graphics, one-time niche market. […]

Oct, 30

### Performance enhancement of MAGIC FDTD-PIC plasma-wave simulations using GPU processing

Summary form only given. Present day computers equipped with powerful graphics processing units (GPUs) show considerable promise of increased performance for the electromagnetic (EM) modeler. In order to determine the degree of performance gain achievable for electro-energetic physics computations, the MAGIC EM finite difference-time domain (FDTD) particle-in-cell (PIC) plasma code is undergoing testing for parallel […]

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

### 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 […]