Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.

Multi-core based parallel computing technique for content-based image retrieval

In this paper, we propose a parallel computing technique for content-based image retrieval （CBIR） system. This technique is mainly used for single node with multi-core processor, which is different from those based on cluster or network computing architecture. Due to its specific applications （such as medical image processing） and the harsh terms of hardware resource requirement, the CBIR system has been prevented from being widely used. With the increasing volume of the image database, the widespread use of multi-core processors, and the requirement of the retrieval accuracy and speed, we need to achieve a retrieval strategy which is based on multi-core processor to make the retrieval faster and more convenient than before. Experimental results demonstrate that this parallel architecture can significantly improve the performance of retrieval system. In addition, we also propose an efficient parallel technique with the combinations of the cluster and the multi-core techniques, which is supposed to gear to the new trend of the cloud computing.

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized;the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.

Efficient computation of Hash Hirschberg protein alignment utilizing hyper threading multi-core sharing technology

Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data.There-fore,Multi-processing CPUs technology can be used including physical and logical processors(Hyper Threading)to significantly increase the performance of computations.Accordingly,sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal alignments.This research used the Hash Table-NGram-Hirschberg(HT-NGH)algo-rithm to represent this pairwise alignment utilizing hashing capabilities.The authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise alignment.The proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units,that is,reducing idle processing unit situations.The authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking perfor-mance of 24.8%average speed up to 34.4%as the highest boosting rate.The benefit of this work improvement is shown preserving acceptable accuracy,that is,reaching 2.08,2.88,and 3.87 boost-up as well as the efficiency of 1.04,0.96,and 0.97,using 2,3,and 4 cores,respectively,as attractive remarkable results.

Analysis on intersections between fractures by parallel computation

The discrete fracture network model is a powerful tool for fractured rock mass fluid flow simulations and supports safety assessments of coal mine hazards such as water inrush.Intersection analysis,which identifies all pairs of intersected fractures(the basic components composing the connectivity of a network),is one of its crucial procedures.This paper attempts to improve intersection analysis through parallel computing.Considering a seamless interfacing with other procedures in modeling,two algorithms are designed and presented,of which one is a completely independent parallel procedure with some redundant computations and the other is an optimized version with reduced redundancy.A numerical study indicates that both of the algorithms are practical and can significantly improve the computational performance of intersection analysis for large-scale simulations.Moreover,the preferred application conditions for the two algorithms are also discussed.

A UNIVERSAL ALGORITHM FOR PARALLEL CRC COMPUTATION AND ITS IMPLEMENTATION

Derived from a proposed universal mathematical expression, this paper investigates a novel algo-rithm for parallel Cyclic Redundancy Check (CRC) computation, which is an iterative algorithm to update the check-bit sequence step by step and suits to various argument selections of CRC computation. The algorithm proposed is quite suitable for hardware implementation. The simulation implementation and performance analysis suggest that it could efficiently speed up the computation compared with the conventional ones. The algorithm is implemented in hardware at as high as 21Gbps, and its usefulness in high-speed CRC computa-tions is implied, such as Asynchronous Transfer Mode (ATM) networks and 10G Ethernet.

A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

This paper aims to solve large-scale and complex isogeometric topology optimization problems that consumesignificant computational resources. A novel isogeometric topology optimization method with a hybrid parallelstrategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equationsolving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency ofCPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload betweenCPU and GPU. To illustrate the advantages of the proposedmethod, three benchmark examples are tested to verifythe hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster thanserial CPU and parallel GPU, while the speedups can be up to two orders of magnitude.

Parallel computation of unified finite-difference time-domain for underwater sound scattering

In this work, we treat scattering objects, water, surface and bottom in a truly unified manner in a parallel finitedifference time-domain （FDTD） scheme, which is suitable for distributed parallel computing in a message passing interface （MPI） programming environment. The algorithm is implemented on a cluster-based high performance computer system. Parallel computation is performed with different division methods in 2D and 3D situations. Based on analysis of main factors affecting the speedup rate and parallel efficiency, data communication is reduced by selecting a suitable scheme of task division. A desirable scheme is recommended, giving a higher speedup rate and better efficiency. The results indicate that the unified parallel FDTD algorithm provides a solution to the numerical computation of acoustic scattering.

Parallel Computation of Shallow-water Model on Workstations Cluster

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Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance Computing

This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.

Parallel Computation of the Noncooperative Equilibria for Dynamic Games

A method of the parallel computation of the linear quadratic non cooperative dynamic games problem is proposed. The Lyapunov function is introduced, through which the form adapted to parallel computation of the open loop Nash equilibrium strategies is gi

A PARALLEL COMPUTATION SCHEME FOR IMPLICIT RUNGE-KUTTA METHODS AND THE ITERATIVELY B-CONVERGENCE OF ITS NEWTON ITERATIVE PROCESS

In this paper, based on the implicit Runge-Kutta(IRK) methods, we derive a class of parallel scheme that can be implemented on the parallel computers with Ns(N is a positive even number) processors efficiently, and discuss the iteratively B-convergence of the Newton iterative process for solving the algebraic equations of the scheme, secondly we present a strategy providing initial values parallelly for the iterative process. Finally, some numerical results show that our parallel scheme is higher efficient as N is not so large.

Preconditioned method in parallel computation

The grid equations in decomposed domain by parallel computation are soled, and a method of local orthogonalization to solve the large-scaled numerical computation is presented. It constructs preconditioned iteration matrix by the combination of predigesting LU decomposition and local orthogonalization, and the convergence of solution is proved. Indicated from the example, this algorithm can increase the rate of computation efficiently and it is quite stable.

CLUSTER OF WORKSTATIONS BASED ON DYNAMIC LOAD BALANCING FOR PARALLEL TREE COMPUTATION DEPTH-FIRST-SEARCH

The real problem in cluster of workstations is the changes in workstation power or number of workstations or dynmaic changes in the run time behavior of the application hamper the efficient use of resources. Dynamic load balancing is a technique for the parallel implementation of problems, which generate unpredictable workloads by migration work units from heavily loaded processor to lightly loaded processors at run time. This paper proposed an efficient load balancing method in which parallel tree computations depth first search (DFS) generates unpredictable, highly imbalance workloads and moves through different phases detectable at run time, where dynamic load balancing strategy is applicable in each phase running under the MPI(message passing interface) and Unix operating system on cluster of workstations parallel platform computing.

PARALLEL COMPUTATION OF SUPER-SONIC BLUNT BODY VISCOUS FLOWFIELDS IN PVM

Supersonic viscous flows past blunt bodies is calculated with TVD difference scheme and implicit Lower Upper Symmetric Gauss Seidel (LU SGS) method, and parallel programming designing software platform PVM is used based on message passing to distribute a large task according to some patching strategies to a large number of processors in the network. These processors accomplish this large task together. The marked improvement of computational efficiency in networks, especially in MPP system, demonstrates the potential vitality of CFD in engineering design.

Realistic Efficiency Evaluations for Parallel Computations under Workstation Cluster

In recent years, high performance scientific computing under workstation cluster connected by local area network is becoming a hot point. Owing to both the longer latency and the higher overhead for protocol processing compared with the powerful single workstation capacity, it is becoming severe important to keep balance not only for numerical load but also for communication load, and to overlap communications with computations while parallel computing. Hence,our efficiency evaluation rules must discover these capacities of a given parallel algorithm in order to optimize the existed algorithm to attain its highest parallel efficiency. The traditional efficiency evaluation rules can not succeed in this work any more. Fortunately, thanks to Culler's detail discuss in LogP model about interconnection networks for MPP systems, we present a system of efficiency evaluation rules for parallel computations under workstation cluster with PVM3.0 parallel software framework in this paper. These rules can satisfy above acquirements successfully. At last, two typical synchronous,and asynchronous applications are designed to verify the validity of these rules under 4 SGIs workstations cluster connected by Ethernet.

Combination Method for Parallel Computation in ODEs

In this paper, a 3rd order combination method with three processes and a 4th order combination method with five processes for solving ODEs are discussed. These methods are the Runge-Kutta method combined with a linear multistep method, which overcomes the defect of the 3rd order parallel Runge-Kutta method discussed in [1].

A Class of Stable Difference Schemes For Linear Elliptic PDEs And Their Asynchronous Parallel Computation

This paper improves and generalizes the two difference schemes presented in paper [1] and gives a new difference scheme for second order linear elliptic partial differential equations, its difference matrix is a matrix and because of the stability of the M-matrix, it is convergent by the asynchronous iterative method on multiprocessors. Then this paper gives a class of differeifce schemes for linear elliptic PDEs so that their difference matrixes are all M-matrixes and their asynchronous parallel computation are convergent.

A Parallel Computational Scheme on Hybrid Methods

Based on the efficient hybrid methods for solving initial value problems of stiff ODEs, this paper derives a parallel scheme that can be used to solve the problems on parallel computers with N processors, and discusses the iteratively B-convergence of the Newton iterative process, finally, the paper provides some numberical results which show that the parallel scheme is highly efficient as N is not too large.

Development of high performance casting analysis software by coupled parallel computation

Up to now,so much casting analysis software has been continuing to develop the new access way to real casting processes. Those include the melt flow analysis,heat transfer analysis for solidification calculation,mechanical property predictions and microstructure predictions. These trials were successful to obtain the ideal results comparing with real situations,so that CAE technologies became inevitable to design or develop new casting processes. But for manufacturing fields,CAE technologies are not so frequently being used because of their difficulties in using the software or insufficient computing performances. To introduce CAE technologies to manufacturing field,the high performance analysis is essential to shorten the gap between product designing time and prototyping time. The software code optimization can be helpful,but it is not enough,because the codes developed by software experts are already optimized enough. As an alternative proposal for high performance computations,the parallel computation technologies are eagerly being applied to CAE technologies to make the analysis time shorter. In this research,SMP (Shared Memory Processing) and MPI (Message Passing Interface) (1) methods for parallelization were applied to commercial software "Z-Cast" to calculate the casting processes. In the code parallelizing processes,the network stabilization,core optimization were also carried out under Microsoft Windows platform and their performances and results were compared with those of normal linear analysis codes.