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首页> 外文期刊>ACM Transactions on Design Automation of Electronic Systems >Static Mapping of Applications on Heterogeneous Multi-Core Platforms Combining Logic-Based Benders Decomposition with Integer Linear Programming
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Static Mapping of Applications on Heterogeneous Multi-Core Platforms Combining Logic-Based Benders Decomposition with Integer Linear Programming

机译:用整数线性规划结合逻辑基弯曲分解的异构多核平台应用的静态映射

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摘要

The proper mapping of an application on a multi-core platform and the scheduling of its tasks are key elements to achieve the maximum performance. In this article, a novel hybrid approach based on integrating the Logic-Based Benders Decomposition (LBBD) principle with a pure Integer Linear Programming (ILP) model is introduced for mapping applications described by Directed Acyclic Graphs (DAGs) on platforms consisting of heterogeneous cores. The LBBD approach combines two optimization techniques with complementary strengths, namely ILP and Constraint Programming (CP), and is employed as a cut generation scheme. The generated constraints are utilized by the ILP model to cut possible assignment combinations aiming at improving the solution or proving the optimality of the best-found one. The introduced approach was applied both on synthetic DAGs and on DAGs derived from real applications. Through the proposed approach, many problems were optimally solved that could not be solved by any of the above methods (ILP, LBBD) alone within a time limit of 2 hours, while the overall solution time was also significantly decreased. Specifically, the hybrid method exhibited speedups equal to 4.2 x for the synthetic instances and 10 x for the real-application DAGs over the LBBD approach and two orders of magnitude over the ILP model.
机译:在多核平台上的应用程序应用程序的适当映射和其任务的调度是实现最大性能的关键元素。在本文中,引入了一种基于集成逻辑的弯曲器分解(LBBD)原理的新型混合方法用纯整数线性编程(ILP)模型,用于映射由由异构芯组成的平台上的定向非循环图(DAG)描述的应用程序。 LBBD方法与互补强度,即ILP和约束编程(CP)结合了两个优化技术,并且被用作截止生成方案。 ILP模型利用生成的约束来切割可能的分配组合,旨在改善解决方案或证明最佳发现的解决方案。介绍的方法应用于合成DAG和来自真实应用的DAG。通过所提出的方法,许多问题得到了最佳解决,以通过上述方法(ILP,LBBD)在2小时的时间限制中不能通过任何上述方法(ILP,LBBD)来解决,而总体溶液时间也显着降低。具体地,混合方法表现出相同4.2×的加速,对于合成实例,10 x用于在LBBD方法上进行实际应用程序,并且在ILP模型上的两个数量级。

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