The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter stra...The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.展开更多
The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects.Several of the state of the art supercomputers use networks based on the increasingly popular Dra...The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects.Several of the state of the art supercomputers use networks based on the increasingly popular Dragonfly topology.It is crucial to study the behavior and performance of different parallel applications running on Dragonfly networks in order to make optimal system configurations and design choices,such as job scheduling and routing strategies.However,in order to study these temporal network behavior,we would need a tool to analyze and correlate numerous sets of multivariate time-series data collected from the Dragonfly's multi-level hierarchies.This paper presents such a tool-a visual analytics system-that uses the Dragonfly network to investigate the temporal behavior and optimize the communication performance of a supercomputer.We coupled interactive visualization with time-series analysis methods to help reveal hidden patterns in the network behavior with respect to different parallel applications and system configurations.Our system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies,which effectively helps visual analysis tasks.We demonstrate the effectiveness of the system with a set of case studies.Our system and findings can not only help improve the communication performance of supercomputing applications,but also the network performance of next-generation supercomputers.展开更多
基金funded by the NationalKey Research and Development Program of China under Grant No.11974373.
文摘The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.
基金This research was sponsored by the Advanced Scientific Computing Research Program,the Office of Science,U.SDepartment of Energy through grants DE-SC0014917,DE-SC0012610,and DE-AC02-06CH11357.
文摘The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects.Several of the state of the art supercomputers use networks based on the increasingly popular Dragonfly topology.It is crucial to study the behavior and performance of different parallel applications running on Dragonfly networks in order to make optimal system configurations and design choices,such as job scheduling and routing strategies.However,in order to study these temporal network behavior,we would need a tool to analyze and correlate numerous sets of multivariate time-series data collected from the Dragonfly's multi-level hierarchies.This paper presents such a tool-a visual analytics system-that uses the Dragonfly network to investigate the temporal behavior and optimize the communication performance of a supercomputer.We coupled interactive visualization with time-series analysis methods to help reveal hidden patterns in the network behavior with respect to different parallel applications and system configurations.Our system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies,which effectively helps visual analysis tasks.We demonstrate the effectiveness of the system with a set of case studies.Our system and findings can not only help improve the communication performance of supercomputing applications,but also the network performance of next-generation supercomputers.
