It is found that making use of parallelism we were able to outperform an expensive computer which costs much more than our cluster. Then we compared the time it takes to run them on cluster in parallel and in sequential on a computer having 6500 core i7 Intel processor. We have also written sequential programs applying same algorithms in Python. We have written parallel programs of Monte Carlo's Simulation for finding value of pi and prime number generator in Python and C++ making use of MPI4py and MPICH respectively. MPI is a standard for writing codes for such clusters. We are using MPI4py, which a Python based implantation of Message Passing Interface (MPI) and MPICH which also an open source implementation of MPI and allows us to code in C, C++ and Fortran. For communication between nodes we have created a network over Ethernet. Master combines those results and show the final output to the user. Load is equally divided among all nodes and they send their results to master. There is a master node, which interacts with user and all other nodes are slave nodes. ![]() The motivation is to create a small sized, cheap device on which students and researchers can get hands on experience. Our Cluster works exactly similar to current day's supercomputers. Beowulf cluster means cluster of any Commodity hardware. Instead of making powerful computer by increasing number of transistor now we are moving toward Parallelism. We observed that with small size problems Dell cluster performed better against HP cluster while with large size problems HP cluster won the game. To analyze the performance of these two clusters we have executed two different parallel programs on the clusters for pi calculation and quick sort with different problem sizes. One cluster is made up of Dell core 2 duo systems and second cluster is made up of HP core 2 duo systems each with two nodes having almost same configurations. This paper compares the two different types of clusters to check the overall performance in execution time. At this present time, clusters technique has practical in numerous areas, for instance scientific calculations, weather forecasting, bioinformatics, signal processing, petroleum exploration and so on. High Performance Computing (HPC) is the field of computer science that emphases on making of cluster computers, supercomputers and parallel algorithms. Collection of personal computers (PCs) builds a cluster that provides us parallel execution. Cluster is the only technique that provides parallel computing, scalability and high availability at low cost. Parallel computing has become most important issue right this time but because of the high cost of supercomputer it is not accessible for everyone. The paper aims to make users aware of the various parameters in a cluster environment. This paper also introduces some of the tools and measures that rate efficiencies of clusters to help users assess the quality of cluster design. This paper introduces new users to the most commonly used frameworks and some recent developments that best exploit the capabilities of R-Pi when used in clusters. ![]() Users have started using the embedded networking capability to design portable clusters that replace the costlier machines. It runs on open source Linux, making it a preferred choice for lab-level research and studies. The Raspberry-Pi (R-Pi) is a small device capable of many functionalities akin to super-computing while being portable, economical and flexible. In addition, students and lab-level enthusiasts do not have the requisite access to modify the functionality to suit specific purposes. However, such setups, comprising of mainframes, servers and networking devices are inaccessible to many, costly, and are not portable. The underlying framework facilitating such possibilities is networking of servers, nodes, and personal computers. ![]() In present times, updated information and knowledge has become readily accessible to researchers, enthusiasts, developers, and academics through the Internet on many different subjects for wider areas of application.
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