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Thermal-Constrained Task Scheduling on 3-D Multicore Processors for Throughput-and-Energy Optimization

机译:3-D多核处理器上的热约束任务调度,以实现吞吐量和能量优化

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Thermal-constrained task scheduler for throughput optimization on 3-D multicore processors (3-D MCPs) has been studied extensively. However, these throughput-optimized strategies often ignore energy consumption and overuse thermal simulations. Therefore, in this brief, a new strategy named thermal-aware mapping and VoltagE scaling (TAMVES) is proposed to optimize throughput and energy consumption while satisfying thermal constraints (in terms of both peak temperature and temperature gradient) simultaneously. Layer-by-layer task-to-core mapping and thermal-and-energy-aware voltage scaling are incorporated in TAMVES to reduce peak temperature and temperature gradient without extensive thermal simulation. Furthermore, idle time slots are also utilized by voltage scaling for minimizing energy consumption. Our experimental results show that under thermal constraints, TAMVES outperforms a previous work (3-D Wave) by 35.30% averagely on throughput. In addition, TAMVES that features three-order faster speed under timing constraints outperforms 3-D Wave for saving 51.17% more energy and reducing 8.37% more peak temperature and 5.67% more temperature gradient. As a result, TAMVES has proven itself an effective task scheduler that optimizes throughput and energy on 3-D MCPs under thermal constraints.
机译:在3-D多核处理器(3-D MCP)上用于吞吐量优化的热约束任务调度程序已得到广泛研究。但是,这些优化吞吐量的策略通常会忽略能耗并过度使用热模拟。因此,在本简介中,提出了一种新的策略,称为热感知映射和VoltagE缩放(TAMVES),以优化吞吐量和能耗,同时满足热约束(就峰值温度和温度梯度而言)。 TAMVES中包含了逐层任务到核心的映射以及热和能量感知的电压缩放功能,以在不进行大量热仿真的情况下降低峰值温度和温度梯度。此外,电压缩放还利用空闲时隙来最小化能量消耗。我们的实验结果表明,在热约束下,TAMVES的吞吐量平均要比以前的工作(3-D Wave)高出35.30%。此外,在时序约束下具有三阶更快速度的TAMVES优于3-D Wave,可节省51.17%的能量,并降低8.37%的峰值温度和5.67%的温度梯度。结果,TAMVES证明了自己是有效的任务调度程序,可以在热约束下优化3-D MCP的吞吐量和能量。

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