Intelligent Task Offloading and Collaborative Computation over D2D Communication

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

Heterogeneous Parallel Algorithm Design and Performance Optimization for WENO on the Sunway TaihuLight Supercomputer

A Weighted Essentially Non-Oscillatory scheme(WENO) is a solution to hyperbolic conservation laws,suitable for solving high-density fluid interface instability with strong intermittency. These problems have a large and complex flow structure. To fully utilize the computing power of High Performance Computing(HPC) systems, it is necessary to develop specific methodologies to optimize the performance of applications based on the particular system’s architecture. The Sunway TaihuLight supercomputer is currently ranked as the fastest supercomputer in the world. This article presents a heterogeneous parallel algorithm design and performance optimization of a high-order WENO on Sunway TaihuLight. We analyzed characteristics of kernel functions, and proposed an appropriate heterogeneous parallel model. We also figured out the best division strategy for computing tasks,and implemented the parallel algorithm on Sunway TaihuLight. By using access optimization, data dependency elimination, and vectorization optimization, our parallel algorithm can achieve up to 172× speedup on one single node, and additional 58× speedup on 64 nodes, with nearly linear scalability.

Helmholtz Solving and Performance Optimization in Global/Regional Assimilation and Prediction System

Despite efficient parallelism in the solution of physical parameterization in the Global/Regional Assimilation and Prediction System(GRAPES),the Helmholtz equation in the dynamic core,with the increase of resolution,can hardly achieve sufficient parallelism in the solving process due to a large amount of communication and irregular access.In this paper,optimizing the Helmholtz equation solution for better performance and higher efficiency has been an urgent task.An optimization scheme for the parallel solution of the Helmholtz equation is proposed in this paper.Specifically,the geometrical multigrid optimization strategy is designed by taking advantage of the data anisotropy of grid points near the pole and the isotropy of those near memory equator in the Helmholtz equation,and the Incomplete LU(ILU)decomposition preconditioner is adopted to speed up the convergence of the improved Generalized Conjugate Residual(GCR),which effectively reduces the number of iterations and the computation time.The overall solving performance of the Helmholtz equation is improved by thread-level parallelism,vectorization,and reuse of data in the cache.The experimental results show that the proposed optimization scheme can effectively eliminate the bottleneck of the Helmholtz equation as regards the solving speed.Considering the test results on a 10-node two-way server,the solution of the Helmholtz equation,compared with the original serial version,is accelerated by 100,with one-third of iterations reduced.

Text-enhanced network representation learning

Network representation learning called NRL for short aims at embedding various networks into low-dimensional continuous distributed vector spaces.Most existing representation learning methods focus on learning representations purely based on the network topology.i.e.,the linkage relationships between network nodes,but the nodes in lots of networks may contain rich text features,which are beneficial to network analysis tasks,such as node classification,link prediction and so on.In this paper,we propose a novel network representation learning model,which is named as Text-Enhanced Network Representation Learning called TENR for short,by introducing text features of the nodesto learn more discriminative network representations,which come from joint learning of both the network topology and text features,and include common influencing factors of both parties.In the experiments,we evaluate our proposed method and other baseline methods on the task of node classihication.The experimental results demonstrate that our method outperforms other baseline methods on three real-world datasets.