Alias – Wavefront OBJ meshes are a common text file type for transferring 3D mesh data between applications made by different vendors.However, as the mesh complexity gets higher and denser, the files become larger an...Alias – Wavefront OBJ meshes are a common text file type for transferring 3D mesh data between applications made by different vendors.However, as the mesh complexity gets higher and denser, the files become larger and slower to import.This paper explores the use of GPUs to accelerate the importing and parsing of OBJ files by studying file read-time, runtime, and load resistance. We propose a new method of reading and parsing that circumvents GPU architecture limitations and improves performance, seeing the new GPU method outperforms CPU methods with a 6×– 8× speedup. When running on a heavily loaded system, the new method only received an 80% performance hit, compared to the160% that the CPU methods received. The loaded GPU speedup compared to unloaded CPU methods was3.5×, and, when compared to loaded CPU methods,8×. These results demonstrate that the time is right for further research into the use of data-parallel GPU acceleration beyond that of computer graphics and high performance computing.展开更多
Recently,software defined networking(SDN)is a promising paradigm shift that decouples the control plane from the data plane.It can centrally monitor and control the network through softwarization,i.e.,controller.Multi...Recently,software defined networking(SDN)is a promising paradigm shift that decouples the control plane from the data plane.It can centrally monitor and control the network through softwarization,i.e.,controller.Multiple controllers are a necessity of current SDN based WAN.Placing multiple controllers in an optimum way is known as controller placement problem(CPP).Earlier,solutions of CPP only concentrated on propagation latency but overlooked the capacity of controllers and the dynamic load on switches,which is a significant factor in real networks.In this paper,we develop a novel optimization algorithm named varna-based optimization(VBO)and use it to solve CPP.To the best of our knowledge,this is the first attempt to minimize the total average latency of SDN along with the implementation of TLBO and Jaya algorithms to solve CPP for all twelve possible scenarios.Our experimental results show that TLBO outperforms PSO,and VBO outperforms TLBO and Jaya algorithms in all scenarios for all topologies.展开更多
文摘利用第一性原理模拟计算铜铟硒(CIS)太阳能电池CIS吸收层,及CIS中普遍存在的有序缺陷化合物(ordered defect compound,ODC)CuIn_5Se_8的性质.依据CuIn_5Se_8形成的方式,结合对称性越高、能量越低的原则,建立CuInS_2中的ODC-CuIn_5S_8结构,并从态密度角度讨论CuInS2与CuIn_5S_8的差异.分别选用ZnSe和CuI半导体作为CIS和CuInS_2电池的缓冲层,利用第一性原理计算得到价带偏移(valence band offset,VBO).在ZnSe/CIS界面处,CIS的价带顶(valence band maximum,VBM)比ZnSe高0.52 eV;在CuI/CuInS_2界面处,CuI的价带顶比CuInS_2低0.37 eV,表明CuI非常适合应用于CuInS_2电池缓冲层.ODC中由于Cu的缺失,其d轨道电子和阴离子p轨道电子的p-d排斥力减小,使ODC材料的价带顶相对于自身本征材料有所下降.
文摘Alias – Wavefront OBJ meshes are a common text file type for transferring 3D mesh data between applications made by different vendors.However, as the mesh complexity gets higher and denser, the files become larger and slower to import.This paper explores the use of GPUs to accelerate the importing and parsing of OBJ files by studying file read-time, runtime, and load resistance. We propose a new method of reading and parsing that circumvents GPU architecture limitations and improves performance, seeing the new GPU method outperforms CPU methods with a 6×– 8× speedup. When running on a heavily loaded system, the new method only received an 80% performance hit, compared to the160% that the CPU methods received. The loaded GPU speedup compared to unloaded CPU methods was3.5×, and, when compared to loaded CPU methods,8×. These results demonstrate that the time is right for further research into the use of data-parallel GPU acceleration beyond that of computer graphics and high performance computing.
文摘Recently,software defined networking(SDN)is a promising paradigm shift that decouples the control plane from the data plane.It can centrally monitor and control the network through softwarization,i.e.,controller.Multiple controllers are a necessity of current SDN based WAN.Placing multiple controllers in an optimum way is known as controller placement problem(CPP).Earlier,solutions of CPP only concentrated on propagation latency but overlooked the capacity of controllers and the dynamic load on switches,which is a significant factor in real networks.In this paper,we develop a novel optimization algorithm named varna-based optimization(VBO)and use it to solve CPP.To the best of our knowledge,this is the first attempt to minimize the total average latency of SDN along with the implementation of TLBO and Jaya algorithms to solve CPP for all twelve possible scenarios.Our experimental results show that TLBO outperforms PSO,and VBO outperforms TLBO and Jaya algorithms in all scenarios for all topologies.
