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Throughput Optimization for Pipeline Workflow Scheduling with Setup Times

机译:具有设置时间的管道工作流调度的吞吐量优化

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We tackle pipeline workflow applications that are executed on a distributed platform with setup times. Several computation stages are interconnected as a linear application graph, and each stage holds a buffer of limited size where intermediate results are stored and a processor setup time occurs when passing from one stage to another. In this paper, we focus on interval mappings (consecutive stages mapped on a same processor), and the objective is the throughput optimization. Even when neglecting setup times, the problem is NP-hard on heterogeneous platforms and we therefore restrict to homogeneous resources. We provide an optimal algorithm for constellations with identical buffer capacities. When buffer sizes are not fixed, we deal with the problem of allocating the buffers in shared memory and present a b/(b + 1)-approximation algorithm.
机译:我们处理具有设置时间的,在分布式平台上执行的管道工作流应用程序。几个计算阶段以线性应用图的形式互连,并且每个阶段都拥有一个有限大小的缓冲区,用于存储中间结果,并且当从一个阶段传递到另一阶段时,会发生处理器设置时间。在本文中,我们关注间隔映射(在同一处理器上映射的连续阶段),目标是吞吐量优化。即使忽略设置时间,问题仍然是异构平台上的NP问题,因此我们只能使用同质资源。我们为具有相同缓冲区容量的星座图提供了一种最佳算法。当缓冲区大小不固定时,我们将处理在共享内存中分配缓冲区的问题,并提出一种b /(b + 1)近似算法。

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