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Memory-aware optimal scheduling with communication overhead minimization for streaming applications on chip multiprocessors

机译:内存感知的最佳调度,将通信开销降至最低,适用于芯片多处理器上的流应用程序

摘要

In this paper, we focus on solving the problem of removing inter-core communication overhead for streaming applications on chip multiprocessors. The objective is to totally remove inter-core communication overhead while minimizing the overall memory usage. By totally removing inter-core communication overhead, a shorter period can be applied and system throughput can be improved. Our basic idea is to let tasks with intra-period data dependencies transform to inter-period data dependencies so as to overlap the execution of computation and inter-core communication tasks. To solve the problem, we first perform analysis and obtain the bounds of the times needed to reschedule each task. Then we formulate the scheduling problem as an integer linear programming (ILP) model and obtain an optimal schedule. We perform simulations on a set of benchmarks from both real-life streaming applications and synthetic task graphs. The simulation results show that the proposed approach can achieve significant reduction in schedule length and improve the memory usage compared with the previous work.
机译:在本文中,我们专注于解决为芯片多处理器上的流应用程序消除内核间通信开销的问题。目的是在最大程度减少总体内存使用量的同时,完全消除内核间的通信开销。通过完全消除内核间通信开销,可以应用更短的时间,并可以提高系统吞吐量。我们的基本思想是让具有周期内数据依赖性的任务转换为周期内数据依赖性,从而使计算和内核间通信任务的执行重叠。为了解决该问题,我们首先进行分析并获得重新安排每个任务所需的时间范围。然后,我们将调度问题公式化为整数线性规划(ILP)模型,并获得最佳调度。我们根据现实流应用程序和合成任务图上的一组基准执行模拟。仿真结果表明,与以前的工作相比,该方法可以显着减少调度时间,提高内存使用率。

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