With the popularity of social network, the de- mand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintainin...With the popularity of social network, the de- mand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintaining and processing of dynamic graph is significantly high. In this pa- per, we design iGraph, an incremental graph processing sys- tem for dynamic graph with its continuous updates. The con- tribufions of iGraph include： 1） a hash-based graph partition strategy to enable fine-grained graph updates; 2） a vertex- based graph computing model to support incremental data processing; 3） detection and rebalance methods of hotspot to address the workload imbalance problem during incre- mental processing. Through the general-purpose API, iGraph can be used to implement various graph processing algo- rithms such as PageRank. We have implemented iGraph on Apache Spark, and experimental results show that for real life datasets, iGraph outperforms the original GraphX in respect of graph update and graph computation.展开更多
This paper preseflts a new approach of the synchronous homogeneous concurrent propagation of competitive waves for the purpose of hyper-distributed hyper-parallel heuristic problem-solving. The concurrent algorithm, m...This paper preseflts a new approach of the synchronous homogeneous concurrent propagation of competitive waves for the purpose of hyper-distributed hyper-parallel heuristic problem-solving. The concurrent algorithm, mechanism and their properties are given. In comparison with the traditional AI algorithms, the approach is featured by the knowledge-based problem-solving in the distributed parallel environment, the feasibility for hardware implementation and the various applications.展开更多
This paper proposes an asynchronous heterogeneous propagation approach of concurrent competitive waves for hyper-distributed hyper-parallel heuris tic problem-solving. This approach is much more powerful than the sync...This paper proposes an asynchronous heterogeneous propagation approach of concurrent competitive waves for hyper-distributed hyper-parallel heuris tic problem-solving. This approach is much more powerful than the synchronous homogeneous mechanisms and the asynchronous superimposition algorithms, and has universal validity and availability. The basic conception, concurrent algorithm and its properties are discussed. The theory and conclusions drawn in this paper are of essential importance for the hardware implementation of hyper-distributed hyper-parallel processing based on chaotic cellular networks.展开更多
文摘With the popularity of social network, the de- mand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintaining and processing of dynamic graph is significantly high. In this pa- per, we design iGraph, an incremental graph processing sys- tem for dynamic graph with its continuous updates. The con- tribufions of iGraph include： 1） a hash-based graph partition strategy to enable fine-grained graph updates; 2） a vertex- based graph computing model to support incremental data processing; 3） detection and rebalance methods of hotspot to address the workload imbalance problem during incre- mental processing. Through the general-purpose API, iGraph can be used to implement various graph processing algo- rithms such as PageRank. We have implemented iGraph on Apache Spark, and experimental results show that for real life datasets, iGraph outperforms the original GraphX in respect of graph update and graph computation.
文摘This paper preseflts a new approach of the synchronous homogeneous concurrent propagation of competitive waves for the purpose of hyper-distributed hyper-parallel heuristic problem-solving. The concurrent algorithm, mechanism and their properties are given. In comparison with the traditional AI algorithms, the approach is featured by the knowledge-based problem-solving in the distributed parallel environment, the feasibility for hardware implementation and the various applications.
文摘This paper proposes an asynchronous heterogeneous propagation approach of concurrent competitive waves for hyper-distributed hyper-parallel heuris tic problem-solving. This approach is much more powerful than the synchronous homogeneous mechanisms and the asynchronous superimposition algorithms, and has universal validity and availability. The basic conception, concurrent algorithm and its properties are discussed. The theory and conclusions drawn in this paper are of essential importance for the hardware implementation of hyper-distributed hyper-parallel processing based on chaotic cellular networks.
