Parallel computing

PARALLEL COMPUTING IN CRYPTOGRAPHY A Literature Review Presented to Sharmin Khan Department of Computer Science San Jose State University In Partial Fulfillment of the Requirements for the Class CS 200W By Rachel Gonsalves May 2016 Abstract In today’s world a lot of data is being exchanged via the internet. Sensitive data such as official documents, bank details, credit card information, as well as personal information is being sent online. To maintain the confidentiality, integrity

Parallel Processing and Cloud Computing CS 4170/5170 Project Documentation Rohit Sharma, Siddharth Ravikumar & Abbas Shakiba Academic Supervisor: Dr. Hassan Rajaei Fall 2014 Department of Computer Science Bowling Green State University, Ohio Parallel Processing and Cloud Computing Rohit Sharma, Siddharth Ravikumar & Abbas Shakiba Supervisor: Dr. Rajaei Dept. of Computer Science Bowling Green State University, Ohio Reporting date: 12/02/2014 Abstract Cloud computing is

this necessitated software developers to write parallel software. Peter S.Pacheco in his book, An Introduction to Parallel Programming wrote,”Ordinary serial programs, which are programmers written for a conventional single core processor, usually cannot exploit the presence of multiple cores, and it’s unlikely that translation programs will be able to shoulder all the work of parallelizing serial programs, meaning converting them into parallel programs, which can make use of multiple cores. As

In the computer world, multiprocessing is currently the fastest and most reliable way to create faster computing computers. This will remain so in the future and will only allow computers to become faster and more reliable. In order to understand the impact of multiprocessors, it is important to understand what they are and what multiprocessing is. According to _________ Critcklow, author of “_______________,” the definition of multiprocessing is “independent and simultaneous processing accomplished

Parallel processing computers have many processors working in parallel; the difficulty is to make sure the problem is synchronized properly and the programs are organized to take advantage. Cray Computer's supercomputer XT3 is a scalable, parallel design which can be scaled up to very huge. In a single cabinet, it can hold 96 AMD Opteron processors, which can operate at 998 GFlops per cabinet using dual-core processors. To support this it has a 64K instruction cache, a 64K data cache, and a 1 MB

Introduction Many physical phenomena in stationary condition such as electrical and magnetic potential, heat conduction, fluid flow and elastic problems in static condition can be described by elliptic partial differential equations (EPDE). The EPDE does not involve a time variable, and so describes the steady state of problems. A linear EPDE has the general form as presented in Eq. (1), where a,b,c are coefficient functions, the term f is a source (excitation) function and u is the unknown variable

This paper is based on CUDA, a parallel computing platform model, which utilizes the resources of the Graphical Processing Unit (GPU), increasing the computing performance of our system, hence creating a robust parallel computing unit. In this paper, we will be introducing a brief history on CUDA, it’s execution flow and it’s architecture to handle processor intensive tasks. We will also be highlighting some of it’s real life applications and the difference in performance as compared of the only

Intro: Hi, my name is blank and the project I have been working on this year for computing 520 is differential evolution, DE, on the cloud, under the supervision of blank. Parallel programming, the utilisation of many small tasks to complete a larger one, has become far more prevalent in recent times as problems call for systems with higher performance, faster turnover times, easy access, and lower costs. While this has previously been cost-prohibitive, given that one would have had to purchase

The academic paper at hand aimed at assessing the performance of Compute Unified Device Architecture (CUDA) programming for machining simulation. It has three main dimensions, introducing the machining simulation algorithm parallelization, discussing the main constraints related to the use of the CUDA architecture proposing different solutions to the problem of parallelization with CUDA. Authors have argued that the main purpose of machining simulations in Computer Aided Manufacturing (CAM) software

MapReduce + Parallel-Platform Data Warehouse Architecture Shrujan Kotturi skotturi@uncc.edu College of Computing and Informatics Department of Computer Science Under the Supervision of Dr. Yu Wang yu.wang@uncc.edu Professor, Computer Science Investigation into deriving an Efficient Hybrid model of a - MapReduce + Parallel-Platform Data Warehouse Architecture Shrujan Kotturi University of North Carolina at Charlotte North Carolina, USA E-mail: skotturi@uncc.edu Abstract—Parallel databases