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A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das
A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das
Computer Science Faculty Research & Creative Works
Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms …
A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das
A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das
Computer Science Faculty Research & Creative Works
Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms …
Deepsz: A Novel Framework To Compress Deep Neural Networks By Using Error-Bounded Lossy Compression, Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello
Deepsz: A Novel Framework To Compress Deep Neural Networks By Using Error-Bounded Lossy Compression, Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello
Computer Science Faculty Research & Creative Works
Today's deep neural networks (DNNs) are becoming deeper and wider because of increasing demand on the analysis quality and more and more complex applications to resolve. The wide and deep DNNs, however, require large amounts of resources (such as memory, storage, and I/O), significantly restricting their utilization on resource-constrained platforms. Although some DNN simplification methods (such as weight quantization) have been proposed to address this issue, they suffer from either low compression ratios or high compression errors, which may introduce an expensive fine-tuning overhead (i.e., a costly retraining process for the target inference accuracy). In this paper, we propose DeepSZ: …
Using Neural Networks For Aerodynamic Parameter Modeling, Gerald E. Peterson, William E. Bond, Roger Germann, Barry Streeter, James Urnes
Using Neural Networks For Aerodynamic Parameter Modeling, Gerald E. Peterson, William E. Bond, Roger Germann, Barry Streeter, James Urnes
Computer Science Faculty Research & Creative Works
Neural networks are being developed at McDonnell Douglas Corporation to provide an onboard model of an aircraft's aerodynamics to support advanced flight control systems. These flight control systems, constructed using neural networks and advanced controllers, have the potential to reduce flight control development costs and to improve inflight performance. Neural networks are useful in this situation because they can compactly represent the data and operate in real-time