Posts
Sep, 1
Performance Analysis on Several GPU Architectures of an Algorithm for Noise Removal
In this paper, we present an efficient implementation of parallel algorithms to remove noise in digital images using different Graphics Processing Units (GPUs). The algorithm, based on the concept of peer group, uses a fuzzy metric for finding wrong pixels and the Arithmetic Mean Filter (AMF) to correct it. There are many factors to study […]
Sep, 1
GPU Acceleration of Genetic Algorithms for Subset Selection for Partial Fault Tolerance
As reconfigurable logic devices see increasing use in aerospace and terrestrial applications, fault tolerant techniques are being developed to counter rising susceptibility due to decreasing feature sizes. Applying fault-tolerance to an entire circuit induces unacceptable area and time penalties, thus some techniques trade area for fault tolerance. Area-Constrained Partial Fault Tolerance (ACPFT) is a methodology […]
Sep, 1
A Portable High-Productivity Approach to Program Heterogeneous Systems
The exploitation of heterogeneous resources is becoming increasingly important for general purpose computing. Unfortunately, heterogeneous systems require much more effort to be programmed than the traditional single or even multi-core computers most programmers are familiar with. Not only new concepts, but also new tools with different restrictions must be learned and applied. Additionally, many of […]
Sep, 1
Scalable Solution of Radiative Heat Transfer Problems by the Photon Monte Carlo Algorithm on Hybrid Computing Architectures
The simulation of Radiative Heat Transfer (RHT) effects by the Photon Monte Carlo (PMC) method is a computationally demanding problem. In this paper we present results and analysis of a new algorithm designed to solve this problem on a hybrid computing architecture. This architecture includes distributed memory, shared memory, and Graphics Processing Unit (GPU) accelerated […]
Sep, 1
Towards large-scale network analytics
In this thesis, we present a framework for efficient analysis of large-scale network datasets. There are four important components in our framework: a) a high performance computing platform with Graphics Processing Units (GPUs) and efficient implementations of mining algorithms on top of the GPU platform. b) an efficient summarization method to compress the storage space […]
Sep, 1
Parallel GPU-accelerated Recursion-based Generators of Pseudorandom Numbers
The aim of the paper is to show how to design fast parallel algorithms for linear congruential and lagged Fibonacci pseudorandom numbers generators. The new algorithms employ the divide-and-conquer approach for solving linear recurrence systems and can be easily implemented on GPU-accelerated hybrid systems using CUDA or OpenCL. Numerical experiments performed on a computer system […]
Sep, 1
A GPU Support for Large Scale Quantum Chemistry Applications
GPU/GPGPU computing has been used widely in scientific simulation to improve the performance on hybrid architectures. The quantum chemistry field has benefited greatly from using GPUs, including tasks such as visualization of molecular orbitals and computation of electronic structures. To gain significant success in using GPUs, a large amount of code rewriting and restructuring is […]
Sep, 1
GAROP: Genetic Algorithm framework for Running On Parallel environments
In this research, a Genetic Algorithms framework for Running On Parallel environments, which is named GAROP, is proposed. The GAROP provides the library for a parallel processing, so that users should only describe codes for genetic algorithms (GA) programs, utilizing the library implemented for the part requiring a parallel processing. In the GAROP framework, GA […]
Sep, 1
Binomial American Option Pricing on CPU-GPU Hetergenous System
We present a novel parallel binomial algorithm to compute prices of American options. The algorithm partitions a binomial tree into blocks of multiple levels of nodes, and assigns each such block to multiple processors. Each processor in parallel with the others computes the option’s values at nodes assigned to it. The computation consists of two […]
Sep, 1
A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures
Parallel computing environments are ubiquitous nowadays, including traditional CPU clusters and the emergence of GPU clusters and CPU-GPU clusters because of their performance, cost and energy efficiency. With this trend, an important research issue is to effectively utilize the massive computing power in these architectures to accelerate data-intensive applications arising from commercial and scientific domains. […]
Aug, 31
Multi-GPU Implementation of the Uniformization Method for Solving Markov Models
Markovian models can generate very large sparse matrices, which are difficult to store and solve. A useful method for finding transient probabilities in Markovian models is the uniformization. The aim of this paper is to show that the performance of the uniformization can be improved using multiGPU architecture. We propose partitioning scheme for HYB sparse […]
Aug, 31
CUDA-Accelerated Data-Mining for Putative Heteromeric Transcription Factors and Target Genes Using Microarray Gene Expression Profiles
Understanding protein-protein and protein-DNA interactions is key to understanding the dynamics of gene regulation [3,17]. We here review a previously presented method[1,15,20], based on a variation of microarray expression profile correlation analysis, that seeks to identify interactions between a putative heteropolymeric transcription factor(TF) complex and DNA as well as some experimental results that bolster the […]