PROJECT TITLE :

GPU-Accelerated High-Throughput Online Stream Data Processing - 2018

ABSTRACT:

The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs) makes them perfect for parallel processing of massive knowledge. During this Project, we tend to gift the look, implementation and analysis of G-Storm, a GPU-enabled parallel system based on Storm, that harnesses the massively parallel computing power of GPUs for top-throughput on-line stream data processing. G-Storm has the following desirable features: one) G-Storm is intended to be a general data processing platform as Storm, that can handle various applications and knowledge sorts. 2) G-Storm exposes GPUs to Storm applications whereas preserving its easy-to-use programming model. 3) G-Storm achieves high-throughput and low-overhead information processing with GPUs. 4) G-Storm accelerates data processing additional by enabling Direct Information Transfer (DDT), between two executors that process knowledge at a typical GPU. We tend to implemented G-Storm based mostly on Storm zero.nine.2 and tested it using three different applications, including continuous query, matrix multiplication and image resizing. Extensive experimental results show that 1) Compared to Storm, G-Storm achieves over 7? improvement on throughput for continuous question, while maintaining affordable average tuple processing time. It also leads to a pair of.three? and one.three? throughput improvements on the opposite two applications, respectively. 2) DDT significantly reduces data processing time.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : GPU-Accelerated Parallel Sparse LU Factorization Method for Fast Circuit Analysis - 2016 ABSTRACT: Lower higher (LU) factorization for sparse matrices is the foremost necessary computing step for circuit simulation
PROJECT TITLE :Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing SystemsABSTRACT:High-throughput genotyping technologies (like SNP-arrays) allow the rapid assortment of up to a couple million genetic
PROJECT TITLE :The Generalization Ability of Online Algorithms for Dependent Data - 2013ABSTRACT:We study the generalization performance of online learning algorithms trained on samples coming from a dependent source of data.
ABSTRACT:A new, low-power software-defined radio baseband supports up to 600 Mbps. Wireless LAN and WiMax are implemented on a dynamically reconfigurable processor that's fully utilized in the baseband operation. Power is estimated

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry