A micromagnetic simulator running on graphics processing unit (GPU) is presented. It achieves significant performance boost as compared to previous central processing unit (CPU) simulators, up to two orders of magnitude for large input problems. Different from GPU implementations of other research groups, this simulator is developed with C++ Accelerated Massive Parallelism (C++ AMP) and is hard...

This paper presents the parallel implementation of NeuroJet, a neural network simulation program, using GPUs. To help researchers study different correlation among applications, programming models, and underlying hardware architectures, NeuroJet is examined as a case study to provide insights and findings specific to GPU programming. We demonstrate the importance of programmer effort through ou...

The Gillespie τ-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap τ is quite expensive. In this paper, we explore the acceleration of the τ-Leaping Method using Graphics Processing Unit (GPUs) for ultra-large networks (>0.5e(6) reaction chann...

Monte Carlo methods are common practice in financial engineering for a wide variety of problems, one being option pricing. Large clusters of computers are used to run these calculations. Growing volumes and complexity of work that needs to be performed as well as strict requirements for fast responses makes for a pressing demand for high performance computing. We present a prototype implementat...

With recent technical advances in graphic processing units (GPUs), GPUs have outperformed CPUs in terms of compute capability and memory bandwidth. Many successful GPU applications to high performance computing have been reported. JPEG-LS is an ISO/IEC standard for lossless image compression which utilizes adaptive context modeling and run-length coding to improve compression ratio. However, ad...

We have developed a high-throughput graphics processing unit (GPU) code that can characterize a large database of crystalline porous materials. In our algorithm, the GPU is utilized to accelerate energy grid calculations, where the grid values represent interactions (i.e., Lennard-Jones + Coulomb potentials) between gas molecules (i.e., CH4 and CO2) and materials' framework atoms. Using a paral...

A new paradigm for computing fluid flows is the use of Graphics Processing Units (GPU), which have recently become very powerful and convenient to use. In the past three years, we have implemented five different fluid flow algorithms on GPUs and have obtained significant speed-ups over a single CPU. Typically, it is possible to achieve a factor of 50-100 over a single CPU. In this review paper,...

This work considers the deployment of pseudo-random number generators (PRNGs) on graphics processing units (GPUs), developing an approach based on the xorgens generator to rapidly produce pseudo-random numbers of high statistical quality. The chosen algorithm has configurable state size and period, making it ideal for tuning to the GPU architecture. We present a comparison of both speed and sta...

Finite volume numerical methods have been widely studied, implemented and parallelized on multiprocessor systems or on clusters. Modern graphics processing units (GPU) provide architectures and new programing models that enable to harness their large processing power and to design computational fluid dynamics simulations at both high performance and low cost. We report on solving the 2D compres...

Architectures for parallel computing are becoming all the more essential with the increasing demands of multimedia, scientific, and engineering applications. These applications require architectures which can scale to meet their real-time constraints, with all current technology limitations in mind such as the memory gap, and power wall. While current many-core CPU architectures show an increas...