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Department of Electrical and Computer Engineering

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https://hdl.handle.net/10217/100411
These digital collections include faculty/student publications, theses, dissertations, and datasets from the Department of Electrical and Computer Engineering. Due to departmental name changes, materials from the following historical department are also included here: Electrical Engineering.

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Browsing Department of Electrical and Computer Engineering by Author "Academic Press, publisher"

Now showing 1 - 2 of 2
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    A methodology for exploiting concurrency among independent tasks in partitionable parallel processing systems
    (Colorado State University. Libraries, 1993) Siegel, Howard J., author; Maciejewski, Anthony A., author; Nation, Wayne G., author; Academic Press, publisher
    One benefit of partitionable parallel processing systems is their ability to execute multiple, independent tasks simultaneously. Previous work has identified conditions such that, when there are tasks to be processed, partitioning the system so that all k tasks are processed simultaneously results in a minimum overall execution time. An alternate condition is developed that provides additional insight into the effects of parallelism on execution time. This result and previous results, however, assume that execution times are data independent. It is shown that data-dependent tasks do not necessarily execute faster when processed simultaneously even if the condition is met. A model is developed that provides for the possible variability of a task's execution time and is used in a new framework to study the problem of finding an optimal mapping for identical, independent data-dependent execution time tasks onto partitionable systems. Executing one, some, or all of the k tasks simultaneously is considered. Because this new framework is general, it can also serve as a new method for the study of data-independent tasks. Extension of this framework to situations where the k tasks are nonidentical is discussed.
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    ItemOpen Access
    Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach
    (Colorado State University. Libraries, 1997) Maciejewski, Anthony A., author; Roychowdhury, Vwani P., author; Siegel, Howard Jay, author; Wang, Lee, author; Academic Press, publisher
    To exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random search.