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Posts

Jun, 5

Simulating anomalous diffusion on graphics processing units

The computational power of modern graphics processing units (GPUs) has become an interesting alternative in high performance computing. The specialized hardware of GPUs delivers a high degree of parallelism and performance. Various applications in scientific computing have been implemented such that computationally intensive parts are executed on GPUs. In this article, we present a GPU […]
Jun, 5

Implementation of association rule mining using CUDA

The purpose of this paper is to implement association rule mining algorithm using Nvidia CUDA framework for general purpose computing on GPU. The major objective is to perform performance comparison of association rule mining algorithm using C based implementation on Intel Quad Core/Core2 Duo CPU with CUDA based implementation on Nvidia G80 and GTX 200 […]
Jun, 5

A Study on Parallel Imaging Algorithm of 3D Geological Data

This paper discusses the three-dimension visualization of the geological data using the ray-casting algorithm which can display the internal structure of geological body in details. An improved scheme transforming lots of matrix multiplications to a few of vector additions is developed to accelerate the ray-casting algorithm for the imaging of huge amount of three-dimensional geological […]
Jun, 5

Efficient Embarrassingly Parallel on Graphics Processor Unit

The Embarrassingly Parallel (EP) is one kernel benchmark of NAS Parallel Benchmarks (NPB) which are a set of programs designed to help evaluate the performance of parallel supercomputers. In the EP benchmark, two-dimensional statistics are accumulated from a large number of Gaussian pseudo-random numbers, which produced by Linear Congruential Generator (LCG). In this paper, we […]
Jun, 5

Accelerating Unstructured Mesh Computational Fluid Dynamics on the NVidia Tesla GPU Architecture

This report presents steps towards accelerating Fluidity, a general-purpose computational fluid dynamics package. One portion of the code, an iterative solver, is targeted for optimisation by using Graphics Processing Units (GPUs) to perform computations. A literature survey which examines the performance issues of iterative solvers and optimisations which may overcome these issues on classical and […]
Jun, 5

Performance Analysis of the OP2 Framework on Many-core Architectures

We present a performance analysis and benchmarking study of the OP2 "active" library, which provides an abstraction framework for the solution of parallel unstructured mesh applications. OP2 aims to decouple the scientific specification of the application from its parallel implementation, achieving code longevity and near-optimal performance through re-targeting the back-end to different hardware. Runtime performance […]
Jun, 5

A framework for parallel unstructured grid applications on GPUs

PDEs are important in a whole variety of applications. Want a suitable level of abstraction to separate the user’s specification of the app from the details of the parallel implementation. Aim to achieve code longevity and near-optimal performance through re-targeting the back-end to different hardware.
Jun, 5

QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

One of the major trends in the design of exascale architectures is the use of multicore nodes enhanced with GPU accelerators. Exploiting all resources of a hybrid accelerators- based node at their maximum potential is thus a fundamental step towards exascale computing. In this article, we present the design of a highly efficient QR factorization […]
Jun, 4

Accelerating GPU kernels for dense linear algebra

Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major building block of dense linear algebra (DLA) libraries, and therefore have to be highly optimized. We present some techniques and implementations that significantly accelerate the corresponding routines from currently available libraries for GPUs. In particular, Pointer Redirecting – a set of GPU specific optimization […]
Jun, 4

A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators

We present a Cholesky factorization for multicore with GPU accelerators systems. The challenges in developing scalable high performance algorithms for these emerging systems stem from their heterogeneity, massive parallelism, and the huge gap between the GPUs’ compute power vs the CPU-GPU communication speed. We show an approach that is largely based on software infrastructures that […]
Jun, 4

Numerical simulation of 3D particulate flows based on GPU technology

This thesis deals with a particular problem out of the research field of computational fluid dynamics, the numerical simulation of fluids containing soluted rigid particles. Such problems arise within a variety of applied sciences, such as medicine, ecology and engineering and need to be studied in detail in three-dimensions. So far most scientific publications on […]
Jun, 4

Monte Carlo Radiative Transport on the GPU

This paper presents a fast parallel Monte Carlo method to solve the radiative transport equation in inhomogeneous participating media. The implementation is based on CUDA and runs on the GPU. In order to meet the requirements of the parallel GPU architecture and to reuse shooting paths, we follow a photon mapping approach where during gathering […]

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