Analyzing Resource Trade-offs in Hardware Overprovisioned Supercomputers
Analyzing Resource Trade-offs in Hardware Overprovisioned Supercomputers
复制标题
分析硬件过度配置的超级计算机中的资源权衡
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Schulz
中科院分区：
文献类型：
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作者：
Ryuichi Sakamoto;Tapasya Patki;Thang Cao;Masaaki Kondo;Koji Inoue;M. Ueda;D. Ellsworth;B. Rountree;M. Schulz
硬件过度配置系统最近被提议作为下一代超级计算机节能设计的可行替代方案。这类系统的一个关键挑战是确定过度配置的程度，即在给定的功率限制下需要安装的额外节点的数量。在本文中，我们首先证明了过度配置的程度取决于动态参数，如作业组合以及全局功率限制，而静态决策会导致有限的系统吞吐量。然后，我们研究了三种作业调度算法、四种功率封顶技术和三种节点启动机制的自适应资源管理策略的详尽组合，以了解所涉及的权衡空间。然后，我们得出结论，这些策略如何能够自适应地控制过度配置的程度，并分析它们对作业吞吐量和电力利用率的影响。
Hardware overprovisioned systems have recently been proposed as a viable alternative for a power-efficient design of next-generation supercomputers. A key challenge for such systems is to determine the degree of overprovisioning, which refers to the number of extra nodes that need to be installed under a given power constraint. In this paper, we first show that the degree of overprovisioning depends on dynamic parameters, such as the job mix as well as the global power constraint, and that static decisions can result in limited system throughput. We then study an exhaustive combination of adaptive resource management strategies that span three job scheduling algorithms, four power capping techniques, and three node boot-up mechanisms to understand the trade-off space involved. We then draw conclusions about how these strategies can adaptively control the degree of overprovisioning and analyze their impact on job throughput and power utilization.