Although manycore processors have plenty of cores, not all of them may run simultaneously at full speed and even some of them might need to be power-gated in order to keep the chip within safe temperature limits. Hence, a resource management technique, that allocates cores to application aiming at maximizing the system performance, will not be able to achieve its goal without taking into account the on-chip temperature and its impact on the availability of the chip's resources. However, considering a temperature constraint by the resource management will further increase its complexity, especially in manycores, and thus implementing it in a centralized scheme might lead to a computation bottleneck and a single point of failure. To avoid such scenarios, it is inevitable to distribute the computation required by the resource management technique throughout the chip. In this article, we propose a distributed resource management technique that considers temperature as an essential factor in allocating cores to applications and determining the power states of these cores and their voltage/frequency levels, while taking into account the performance models of the applications in order to maximize the overall system performance under a temperature constraint. Our proposed technique employs, for the first time, combinatorial auctions within an agent system to achieve the targeted goal in a distributed manner. The experimental evaluations show that our proposed technique achieves significant performance improvements with an average of 41% compared to several distributed resource management techniques.

中文翻译：

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多核中温度受限资源管理的组合拍卖

尽管多核处理器拥有大量内核，但并非所有内核都可以同时全速运行，甚至其中一些内核可能需要进行电源门控，以将芯片保持在安全温度范围内。因此，如果不考虑片上温度及其对芯片资源可用性的影响，将内核分配给旨在最大化系统性能的应用程序的资源管理技术将无法实现其目标。然而，考虑资源管理的温度约束将进一步增加其复杂性，尤其是在多核中，因此在集中式方案中实现它可能会导致计算瓶颈和单点故障。为避免此类情况，不可避免地将资源管理技术所需的计算分散到整个芯片中。在本文中，我们提出了一种分布式资源管理技术，该技术将温度作为将内核分配给应用程序并确定这些内核的电源状态及其电压/频率水平的基本因素，同时考虑应用程序的性能模型。在温度限制下最大限度地提高整体系统性能。我们提出的技术首次在代理系统内采用组合拍卖，以分布式方式实现目标。实验评估表明，与几种分布式资源管理技术相比，我们提出的技术实现了显着的性能改进，平均提高了 41%。