Isomorphic Strategy for Processor Allocation in k-Ary n-Cube Systems

Due to its topological generality and flexibility, the k-ary n-cube architecture has been actively researched for various applications. However, the processor allocation problem has not been adequately addressed for the k-ary n-cube architecture, even though it has been studied extensively for hypercubes and meshes. The earlier k-ary n-cube allocation schemes based on conventional slice partitioning suffer from internal fragmentation of processors. In contrast, algorithms based on job-based partitioning alleviate the fragmentation problem but require higher time complexity. This paper proposes a new allocation scheme based on isomorphic partitioning, where the processor space is partitioned into higher dimensional isomorphic subcubes. The proposed scheme minimizes the fragmentation problem and is general in the sense that any size request can be supported and the host architecture need not be isomorphic. Extensive simulation study reveals that the proposed scheme significantly outperforms earlier schemes in terms of mean response time for practical size k-ary and n-cube architectures. The simulation results also show that reduction of external fragmentation is more substantial than internal fragmentation with the proposed scheme

Isomorphic Strategy for Processor Allocation in k-Ary n-Cube Systems

Due to its topological generality and flexibility, the k-ary n-cube architecture has been actively researched for various applications. However, the processor allocation problem has not been adequately addressed for the k-ary n-cube architecture, even though it has been studied extensively for hypercubes and meshes. The earlier k-ary n-cube allocation schemes based on conventional slice partitioning suffer from internal fragmentation of processors. In contrast, algorithms based on job-based partitioning alleviate the fragmentation problem but require higher time complexity. This paper proposes a new allocation scheme based on isomorphic partitioning, where the processor space is partitioned into higher dimensional isomorphic subcubes. The proposed scheme minimizes the fragmentation problem and is general in the sense that any size request can be supported and the host architecture need not be isomorphic. Extensive simulation study reveals that the proposed scheme significantly outperforms earlier schemes in terms of mean response time for practical size k-ary and n-cube architectures. The simulation results also show that reduction of external fragmentation is more substantial than internal fragmentation with the proposed scheme

Processor Allocation in K-Ary N-Cube Multiprocessors

[[abstract]]Composed of various topologies, the k-ary n-cube system is desirable for accepting and executing topologically different tasks. In this paper, we propose a new allocation strategy to utilize the large amount of processor resources in the k-ary n-cubes. Our strategy is an extension of the TC strategy on hypercubes and is able to recognize all subcubes with different topologies. Simulation results show that with such full subcube recognition ability and no internal fragmentation, our strategy depicts constantly better performance than the other strategies, such as the Free-list strategy on k- ary n-cubes and the Sniffing strategy.[[sponsorship]]台灣大學[[conferencetype]]國內[[conferencedate]]19971218~19971220[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]臺北市, 臺

Task allocation and migration on a star-network

Modern day applications require computational power which cannot be satisfied with uniprocessor systems. So the use of multiprocessor systems in such jobs becomes necessary. This thesis presents an approach of allocating the tasks to a multiprocessor system called the star network. Generally, an incoming task requires only a part of the star network, and not the whole network, for its execution. So, we need a task allocation strategy which can identify the free processors forming a substar and allocate tasks to these substars. The task executes for a time equal to task residence time and then relinquishes the substar. Sometimes there might be enough free processors forming a substar in the network which can host the next incoming task. But the allocation strategy may not recognize the free processors as a substar. To create a substar of free processors to host the next task, task migration has to be performed such that the free processors are grouped into a substar. In this work, three processor allocation strategies: static, dynamic and dynamic work task migration are presented. Using simulations, a comparison of these strategies is done to obtain the percentage improvement of one strategy over the other. Also a comparative study of the working of these strategies in star-networks and hypercubes is done. A saving of 5-11% is achieved by for both the networks incorporating task-migration in dynamic allocation over simple dynamic allocation