Many industries nowadays use management and decision making based on artificial neural networks. However, the major drawback of neural networks lies in their time and computational complexity. the problem with computa...
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Many industries nowadays use management and decision making based on artificial neural networks. However, the major drawback of neural networks lies in their time and computational complexity. the problem with computational complexity could be eliminated using sharing of the computing needs on multiple computing nodes. this article focuses on the architectural design of a distributed system, which aims to solve large neural networks. the article describes the technology GPGPU and the next part of the article deals with an overview of methods for speeding up the calculation and distribution of artificial neural network. the main section describes the design of a model architecture description of the algorithm that allows correct data distribution on computational nodes.
BLAST[1] (Basic Local Alignment Search Tool) is a suite of programs used to identify similarity between genetic sequences. It is one of the most widely used tools in Bioinformatics. In recent years, withthe size of g...
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BLAST[1] (Basic Local Alignment Search Tool) is a suite of programs used to identify similarity between genetic sequences. It is one of the most widely used tools in Bioinformatics. In recent years, withthe size of gene and protein sequence database increasing exponentially, BLAST has become both a data-intensive and a computation-intensive application. How to run BLAST rapidly with low cost has always been the hotspot to researchers. parallelization is one of the most important ways to resolve this problem. In this paper, a new approach for parallelizing BLAST based on a parallel processing framework called Robinia is presented. Compared withparallel version of BLAST presented before, Robinia-based BLAST has easy public accessibility and good scalability. Most importantly, it can support operation on WAN, this make it possible to integrate computation and storage resources on Internet to service for super-large scale BLAST projects. We implemented the Robinia-based BLAST and experimented on it using two different datasets. the results show that parallel BLAST based on Robinia can achieve linear speedup based on number of used nodes with good scalability and low cost.
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