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 计算机科学技术学报 , 2002, Abstract: In this paper we propose a novel scheme for scheduling divisible task on parallel processors connected by system interconnection network with arbitrary topology. The divisible task is a computation that can be divided into arbitrary independent subtasks solved in parallel. Our model takes into consideration communication initial time and communication delays between processors. Moreover, by constructing the corresponding Network Spanning Tree (NST) for a network, our scheme can be applied to all kinds of network topologies. We present the concept of Balanced Task Distribution Tree and use it to design the Equation Set Creation Algorithm in which the set of linear equations is created by traversing the NST in post-order. After solving the created equations, we get the optimal task assignment scheme. Experiments confirm the applicability of our scheme in real-life situations.
 Tae-Young Choe International Journal on Computer Science and Engineering , 2012, Abstract: Some task scheduling algorithms generate the shortest schedule, when its input DAG satisfies a specified condition. Among those scheduling algorithms, TDS algorithm proposed a DAG condition where allocation of two parent tasks of a join task in the same processor cause longer schedule length than allocation in different processors, and it generates the shortest schedule if any input DAG satisfies the condition. In the paper, we propose a post-processing scheduling algorithm that reduces the number of processors while preserving its schedule length. Especially, we propose conditions where two processescan be merged without increasing schedule length. Experimental results show that the number of processor is reduced to 92.3% ~ 98.0% if schedule length is reserved and required computing power isreduced to 84.3% ~ 91.2% if schedule length can be increased.
 International Journal of Advanced Computer Research , 2012, Abstract: Multi-core processor technology has been enhancedspectacularly and it is reasonably good inperformance than single core processors therebyhaving the potential to enable computationintensivereal-time applications with precise timingconstraints. Mostly traditional multiprocessor real -time scheduling is stick to Sequential models whichignore intra-task parallelism while Parallel modelssuch as OpenMP have the capability to parallelizespecific segments of tasks, thereby leading to shorterresponse times when possible. In this paper variousresearch papers have been reviewed and arecategorized as Sequential Real-Time Task basedResearch and Parallel Real-Time Task basedResearch. Also various approaches such as tasksplitting techniques, scheduling policies andtechniques used are considered for comparing realtime task scheduling in multi-core processors.
 计算机应用研究 , 2010, Abstract: With different task allocation and scheduling will lead to different time consumption and energy consumption on heterogeneous multi-core processors,using approprivate task matching and scheduling algorithms can save more energy. Now,it is widely recognized that the most promising of the task allocation and scheduling is first to use heuristic way to match the tasks, and then to use of genetic algorithm to shedule.This paper first made improvements in task matching, then used genetic algorithm to resolve the energy for the first time.The results show that it can use less time to reduce more energy consumption in less real time circumstances.
 计算机科学 , 2011, Abstract: For periodic hard real-time tasks running on homogeneous multi core processors, an energy-efficient approach based on dynamic voltage scaling (DVS) was designed. First, computation tasks are ordered by decreasing cycles and task mapping is arranged based on the principle of the shortest scheduling length for computation tasks. Then the computation task with the minimum commun- ication time is set as the last executed computation task while the order of other computation tasks keep unchanged for each processor core. The optimal execution order of computation tasks on each processor core is determined during execution time extension in the case of all computation tasks arranged with the highest frequency in the initial mapping. Experiments were conducted on several random task sets based on the power model of the Intel PXA270. Results show that the proposed approach can decrease energy of multi-core processors eff ectively.
 International Journal of Innovative Technology and Exploring Engineering , 2012, Abstract: Multiprocessor mapping and scheduling algorithms have been extensively studied over the past few decades and havebeen tackled from different perspectives.Task scheduling is an essential aspect of parallel programming. Most heuristics for this NP-hard problem are based on asimple system model that assumes fully connected processors and concurrent interprocessor communication. Hence, contention for communication resources is not considered in task scheduling, yet it has a strong influence on the execution time of a parallel program. This paper investigates the incorporation of contention awareness into task scheduling. The proposed methodology is runtime scheduling which is designed to reduce the wastage of time during static scheduling. We have assumed heterogeneous processors with broadcast and point-to-point communication models and have presented online algorithms for them. Experimental results shows that dynamic scheduling provides better performance than static scheduling.
 计算机系统应用 , 2010, Abstract: Efficient task scheduling is critical for obtaining high performance in a heterogeneous computing environment. Although there are many scheduling algorithms already, they may not have good results, and take high cost. In this paper, a new list scheduling heuristics, named Accurate Priority Scheduling (APS), is presented. The APS selects task with random directed acyclic graph (DAG) and assigns tasks to processors with the insertion based scheduling policy to minimize the makespan. Compared with LMT, HEFT, CPOP based on, show the APS results good performances in most situations.
 软件学报 , 1999, Abstract: Optimum degree of parallelism-based task dependence graph scheduling scheme fully utilizes the global information collected at compile-time, employs the techniques such as task merging in horizontal and vertical directions, processors pre-allocation, combination of static and dynamic scheduling, and integration of centralized scheduling and layer-scheduling. It is a simple, practical and effective scheduling method which addresses the problem of how to both reduce the execution time of programs and economize on processor resources.
 Jian-Jia Chen Computer Science , 2015, Abstract: In the federated scheduling approaches in multiprocessor systems, a task (1) either is restricted for sequential executions or (2) has exclusive access to the assigned processors. There have been several positive results to conduct good federated scheduling policies, with a constant speedup factor with respect to the optimal federated scheduling. This paper answers an open question: "For constrained-deadline task systems, is federated scheduling a good strategy, compared to the optimal schedules?" The answer is "No!". This paper presents an example, which demonstrates that an optimal federated schedule in multiprocessor scheduling has a speedup factor at least $\Omega(\min\{M, N\})$, where $N$ is the number of the given tasks and $M$ is the number of the given processors.
 Computer Science , 2013, Abstract: While previous work on energy-efficient algorithms focused on assumption that tasks can be assigned to any processor, we initially study the problem of task scheduling on restricted parallel processors. The objective is to minimize the overall energy consumption while speed scaling (SS) method is used to reduce energy consumption under the execution time constraint (Makespan $C_{max}$). In this work, we discuss the speed setting in the continuous model that processors can run at arbitrary speed in $[s_{min},s_{max}]$. The energy-efficient scheduling problem, involving task assignment and speed scaling, is inherently complicated as it is proved to be NP-Complete. We formulate the problem as an Integer Programming (IP) problem. Specifically, we devise a polynomial time optimal scheduling algorithm for the case tasks have a uniform size. Our algorithm runs in $O(mn^3logn)$ time, where $m$ is the number of processors and $n$ is the number of tasks. We then present a polynomial time algorithm that achieves an approximation factor of $2^{\alpha-1}(2-\frac{1}{m^{\alpha}})$ ($\alpha$ is the power parameter) when the tasks have arbitrary size work. Experimental results demonstrate that our algorithm could provide an efficient scheduling for the problem of task scheduling on restricted parallel processors.
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