Robinia-BLAST: An Extensible Parallel BLAST Based on Data-Intensive Distributed Computing

2014 ◽  
Author(s):  
Yang Gu ◽  
Zhenchun Huang
Keyword(s):  
Distributed Computing ◽  
Data Intensive

A data intensive distributed computing architecture for “Grid” applications

2000 ◽  
Vol 16 (5) ◽  
pp. 473-481 ◽  
Author(s):  
Brian Tierney ◽  
William Johnston ◽  
Jason Lee ◽  
Mary Thompson
Keyword(s):  
Distributed Computing ◽  
Computing Architecture ◽  
Data Intensive ◽  
Grid Applications

scholarly journals Nakamoto Consensus to Accelerate Supervised Classification Algorithms for Multiparty Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhen Zhang ◽  
Bing Guo ◽  
Yan Shen ◽  
Chengjie Li ◽  
Xinhua Suo ◽  
...  
Keyword(s):  
Distributed Computing ◽  
Supervised Classification ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Classification Algorithms ◽  
Distributed Data ◽  
Data Intensive ◽  
Private Data ◽  
Cooperation Mechanism ◽  
Mathematical Formulas

Bitcoin mining consumes tremendous amounts of electricity to solve the hash problem. At the same time, large-scale applications of artificial intelligence (AI) require efficient and secure computing. There are many computing devices in use, and the hardware resources are highly heterogeneous. This means a cooperation mechanism is needed to realize cooperation among computing devices, and a good calculation structure is required in the case of data dispersion. In this paper, we propose an architecture where devices (also called nodes) can reach a consensus on task results using off-chain smart contracts and private data. The proposed distributed computing architecture can accelerate computing-intensive and data-intensive supervised classification algorithms with limited resources. This architecture can significantly increase privacy protection and prevent leakage of distributed data. Our proposed architecture can support heterogeneous data, making computing on each device more efficient. We used mathematical formulas to prove the correctness and robustness of our system and deduced the condition to stop a given task. In the experiments, we transformed Bitcoin hash collision into distributed computing on several nodes and evaluated the training and prediction accuracy for handwritten digit images (MNIST). The experimental results demonstrate the effectiveness of the proposed method.

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