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Graphic image is compulsory in delivering information and communication in this information technology era. Graphic applications are used widely to manipulate images in advertising industries and in animation movie productions. In order to manipulate numerous and complex images, it is required long period of time and high-performance computers. However, computers with high-performance specification...
Presence of triangles in massive graphs provides many important indications to different graph algorithms. In-memory algorithms don't work for massive graphs since these graphs cannot fit into the memory. Recently, external memory-based algorithms have been proposed for efficient triangle listing which focused on I/O efficiency to improve the performance of triangle listing. However, the existing...
This paper presents the performance evaluation of MRDataCube which we have previously proposed as an efficient algorithm for data cube computation with data reduction using MapReduce framework. We performed a large number of analyses and experiments to evaluate the MRDataCube algorithm in the MapReduce framework. In this paper, we compared it to simple MR-based data cube computation algorithms, e...
We provide a review of the state of the art on the design and implementation of random number generators (RNGs) for simulation, on both sequential and parallel computing environments. We focus on the need for multiple independent streams and substreams of random numbers, explain how they can be constructed and managed, review software libraries that offer them, and illustrate their usefulness via...
This paper presents the main challenges, the hot topics, and the intriguing issues in the area of parallel SAT solving which provides possible directions for future research. It gives a detailed summary for the main features and technologies used in the most widely known and successful parallel SAT solvers and shows the strong points and the shortcomings in them. In addition, it compares between the...
In this paper, the case where there are two parallel two-machine flow-shops available to process jobs is considered. We developed a method that creates sequences intended to dynamically minimize the makespan while jobs are added overtime. The solution procedure developed in this research is demonstrated using an illustrative example. The results revealed the effectiveness of the proposed approach...
The Big Data computing is one of hot spots of the internet of things and cloud computing. How to compute efficiently on the Big Data is the key of improving performance. By means of distributed computing or memory computing, many companies and institutions provide some technologies and produces. But they are invalid in the scene in which there are real-time demands in the low-configure cluster. To...
In the Big Data era, MapReduce has been the most utilized model in academia and industry. The main objective of MapReduce and its well-known implementation Hadoop is to run distributed applications to analyze huge amount of datasets, on very large clusters of commodity machines. This can be very time consuming. Also, the cluster that runs MapReduce applications has to be very scalable. In order to...
Parallel computing is a simultaneous use of multiple compute resources, for example, processors to solve complex computational problems. It has been used in high-end computing areas such as pattern recognition, medical diagnosis, national defense, and web search engine. This paper focuses on the implementation of pattern classification technique, Support Vector Machine (SVM) using vector processor...
This work describes research, efforts, and outcomes for several Computer Science courses after incorporating XSEDE High Performance Computing (HPC) Resources and recommended and required curriculum additions from the ACM 2013 Computer Science Curricula and IEEE Technical Committee on Parallel Processing Curriculum Guidelines. The work herein describes the courses affected by this work, including Computer...
In this paper we raise the issue of how to assess novice youths' learning of programming in an open-ended, project-based learning environment. One approach could be a way to apply quantitative measures to the analysis of programming education across frequent saves in a variety of open-ended projects. This paper focuses on the first stage of this endeavor: the development of exploratory quantitative...
In the field of artificial intelligence and game theory, GTS is a computational problem. Fast GTS algorithm is crucial in computer games. In this paper, to enhance the speed of game tree search and utilize a capability of parallel processing in game tree search using GPU, we concentrate on how to grip extensive parallelism capabilities of GPU. The system works on the real time game called Tic-Tac-Toe...
A design approach of an integrated guidance and control of missile-borne embedded computer system is proposed in this paper, which utilizes a multi-core DSP as the central data processing unit and a FPGA as the co-processing unit. The approach takes advantages of the multi-core processor's resources, which provides a powerful hardware platform base for data processing, data transmission and complex...
This article provides an effective way to alleviate the long running defect of multi-objective intelligent optimization algorithms and enhance its ability to solve the robust optimization (RO) design. It describes the implementation of parallel computing for intelligent optimization algorithms based on the MATLAB Parallel Computing Toolbox (PCT) and Distributed Computing Server (DCS). In order to...
The protective techniques of WINZIP files include compressing the original document and encrypting the AES key. If we want to decrypt the WINZIP file, we require very large computing power and techniques of reducing the password space. In this paper, we have developed a GPU-based parallelized computing system to quickly find out the correct password, and the new system can make parallel decryption,...
Since the before birth of computers we have strived to make intelligent machines that share some of the properties of our own brains. We have tried to make devices that quickly solve problems that we find time consuming, that adapt to our needs, and that learn and derive new information. In more recent years we have tried to add new capabilities to our devices: self-adaptation, fault tolerance, self-repair,...
In computational electromagnetics, surface integral equation (SIE) formulations are widely used to predict the electromagnetic scattering from arbitrary structures. These SIE formulations are discretized into a matrix form by the well-known method of moments (MoM). Up to now, the lack of proper compilers made it necessary for the MoM codes to be parallelized by hand in order to obtain reasonable performance...
Industrial and governmental organizations have accrued vast amounts of data contained in many databases. Many of these databases are developed by different organizations for different purposes, may contain millions of unique entities and may lack a dependable global unique identifier to link an individual’s records across multiple databases. Record Linkage (RL) is a process that connects records that...
Computer clusters with coprocessors/accelerators are typically leveraged to parallelize applications for reducing computation time. Given N parallel tasks and M processing cores, the typical strategy is to statically distribute those N tasks among M cores so that each core receives N/M tasks. However, for many sophisticated applications, the processing times of N tasks may vary. In other words, some...
In this paper, we propose a time-efficient and exact algorithm for the problem of discovering the densest subgraph in big data. Current algorithms for solving this problem have three problems: i) they cannot handle the dilemma between the efficiency of handing big data and the precision of the discovered densest subgraph; ii) they cannot take advantage of both the parallel computing on MapReduce and...
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