Kinetic Monte Carlo （KMC） is a widely used method for studying the evolution of materials at the microcosmic level. At present, while there are many simulation software programs based on this algorithm, most focus o...Kinetic Monte Carlo （KMC） is a widely used method for studying the evolution of materials at the microcosmic level. At present, while there are many simulation software programs based on this algorithm, most focus on the verification of a certain phenomenon and have no analog-scale requirement, so many are serial in nature. The dynamic Monte Carlo algorithm is implemented using a parallel framework called SPPARKS, but Jt does not support the Embedded Atom Method （EAM） potential, which is commonly used in the dynamic simulation of metal materials. Metal material - the preferred material for most containers and components -- plays an important role in many fields, including construction engineering and transportation. In this paper, we propose and describe the development of a parallel software program called CrystaI-KMC, which is specifically used to simulate the lattice dynamics of metallic materials. This software uses MPI to achieve a parallel multiprocessing mode, which avoid the limitations of serial software in the analog scale. Finally, we describe the use of the paralleI-KMC simulation software CrystaI-KMC in simulating the diffusion of vacancies in iron, and analyze the experimental results. In addition, we tested the performance of CrystaI-KMC in ＂meta -Era＂ supercomputing clusters, and the results show the CrystaI-KMC parallel software to have good parallel speedup and scalability.展开更多
In this paper, a fast-convergence distributed support vector machine （FDSVM） algorithm is proposed, aiming at efficiently solving the problem of distributed SVM training. Rather than exchanging information only amon...In this paper, a fast-convergence distributed support vector machine （FDSVM） algorithm is proposed, aiming at efficiently solving the problem of distributed SVM training. Rather than exchanging information only among immediate neighbor sites, the proposed FDSVM employs a deterministic gossip protocol-based commu nication policy to accelerate diffusing information around the network, in which each site communicates with others in a flooding and iterative manner. This communication policy significantly reduces the total number of iterations, thus further speeding up the convergence of the algorithm. In addition, the proposed algorithm is proved to converge to the global optimum in finite steps over an arbitrary strongly connected network （SCN）. Experiments on various benchmark data sets show that the proposed FDSVM consistently outperforms the related state-of-the art approach for most networks, especially in the ring network, in terms of the total training time.展开更多
基金supported by the National Key R & D Program of China (Nos. 2017YFB0202003 and 2017YFB0202 104)
文摘Kinetic Monte Carlo （KMC） is a widely used method for studying the evolution of materials at the microcosmic level. At present, while there are many simulation software programs based on this algorithm, most focus on the verification of a certain phenomenon and have no analog-scale requirement, so many are serial in nature. The dynamic Monte Carlo algorithm is implemented using a parallel framework called SPPARKS, but Jt does not support the Embedded Atom Method （EAM） potential, which is commonly used in the dynamic simulation of metal materials. Metal material - the preferred material for most containers and components -- plays an important role in many fields, including construction engineering and transportation. In this paper, we propose and describe the development of a parallel software program called CrystaI-KMC, which is specifically used to simulate the lattice dynamics of metallic materials. This software uses MPI to achieve a parallel multiprocessing mode, which avoid the limitations of serial software in the analog scale. Finally, we describe the use of the paralleI-KMC simulation software CrystaI-KMC in simulating the diffusion of vacancies in iron, and analyze the experimental results. In addition, we tested the performance of CrystaI-KMC in ＂meta -Era＂ supercomputing clusters, and the results show the CrystaI-KMC parallel software to have good parallel speedup and scalability.
文摘In this paper, a fast-convergence distributed support vector machine （FDSVM） algorithm is proposed, aiming at efficiently solving the problem of distributed SVM training. Rather than exchanging information only among immediate neighbor sites, the proposed FDSVM employs a deterministic gossip protocol-based commu nication policy to accelerate diffusing information around the network, in which each site communicates with others in a flooding and iterative manner. This communication policy significantly reduces the total number of iterations, thus further speeding up the convergence of the algorithm. In addition, the proposed algorithm is proved to converge to the global optimum in finite steps over an arbitrary strongly connected network （SCN）. Experiments on various benchmark data sets show that the proposed FDSVM consistently outperforms the related state-of-the art approach for most networks, especially in the ring network, in terms of the total training time.
