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GPUs can accelerate edge scan performance of graph processing applications; however, the capacity of device memory on GPUs limits the size of graph to process, whereas efficient techniques to handle GPU memory overflows, including overflow detection and performance analysis in large-scale systems, are not well investigated. To address the problem, we propose a MapReduce-based out-of-core GPU memory...
Fast processing for extremely large-scale graph is becoming increasingly important in various domains such as health care, social networks, intelligence, system biology, and electric power grids. The GIM-V algorithm based on MapReduce programing model is designed as a general graph processing method for supporting petabyte-scale graph data. On the other hand, recent large-scale data-intensive computing...
Fast processing for extremely large-scale graph, which consists of millions to trillions of vertices and 100 billions to 100 trillions of edges, is becoming increasingly important in various domains such as health care, social networks, intelligence, system biology, and electric power grid, etc. The GIM-V algorithm based on MapReduce programing model is designed as general graph processing method...
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