超立方体多处理器中的时间共享

E. Filho, V. Barbosa
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引用次数: 6

摘要

在超多维数据集多处理器中,通常通过将多维数据集划分为不同维度的子多维数据集来支持多个用户。用户对子多维数据集的请求可能会被拒绝,这取决于以前如何分配其他子多维数据集,或者因为分配算法无法识别满足请求的现有空闲子多维数据集。在这两种情况下,主要后果都是系统利用率的降低。为了在支持多个用户的同时避免此类问题,作者建议将多维数据集中的所有处理器多路分配给用户。这样可以充分利用系统,将系统的所有资源提供给用户。作者在Intel iPSC/860系统上对这种新方法进行了实验,并将其性能与传统的立方体分区方法进行了比较。结果表明,对于计算密集型应用程序,多路复用处理器时每个用户的平均执行时间通常与为用户分配子数据集时相同的平均执行时间相当,并且通常要低得多。
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Time sharing in hypercube multiprocessors
In hypercube multiprocessors, multiple users are normally supported by dividing the cube into subcubes of different dimensions. A user request for a subcube may be denied depending on how other subcubes were previously allocated or because the allocation algorithm fails to recognize an existing free subcube that would satisfy the request. In both cases, the main consequence is a reduction in system utilization. To support multiple users while avoiding such problems, the authors propose to multiplex all the processors in the cube among the users. In this way, one can get full system utilization and offer all the resources of the system to the users. The authors have conducted experiments with this new approach on an Intel iPSC/860 system, comparing its performance with the one obtained in the conventional cube-partitioning approach. The results show that, for computationally intensive applications, the average execution time per user when multiplexing the processors is generally comparable to the same average when allocating subcubes to the users, and often significantly lower.<>
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