The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is th...The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.展开更多
Emulating massively parallel computer architectures represents a very important tool for the parallel programmers. It allows them to implement and validate their algorithms. Due to the high cost of the massively paral...Emulating massively parallel computer architectures represents a very important tool for the parallel programmers. It allows them to implement and validate their algorithms. Due to the high cost of the massively parallel real machines, they remain unavailable and not popular in the parallel computing community. The goal of this paper is to present an elaborated emulator of a 2-D massively parallel re-configurable mesh computer of size n x n processing elements (PE). Basing on the object modeling method, we develop a hard kernel of a parallel virtual machine in which we translate all the physical properties of its different components. A parallel programming language and its compiler are also devel-oped to edit, compile and run programs. The developed emulator is a multi platform system. It can be installed in any sequential computer whatever may be its operating system and its processing unit technology (CPU). The size n x n of this virtual re-configurable mesh is not limited;it depends just on the performance of the sequential machine supporting the emulator.展开更多
文摘The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.
文摘Emulating massively parallel computer architectures represents a very important tool for the parallel programmers. It allows them to implement and validate their algorithms. Due to the high cost of the massively parallel real machines, they remain unavailable and not popular in the parallel computing community. The goal of this paper is to present an elaborated emulator of a 2-D massively parallel re-configurable mesh computer of size n x n processing elements (PE). Basing on the object modeling method, we develop a hard kernel of a parallel virtual machine in which we translate all the physical properties of its different components. A parallel programming language and its compiler are also devel-oped to edit, compile and run programs. The developed emulator is a multi platform system. It can be installed in any sequential computer whatever may be its operating system and its processing unit technology (CPU). The size n x n of this virtual re-configurable mesh is not limited;it depends just on the performance of the sequential machine supporting the emulator.
