Balancing job performance with system performance via locality-aware scheduling on torus-connected systems
Balancing job performance with system performance via locality-aware scheduling on torus-connected systems
复制标题
通过环面连接系统上的位置感知调度来平衡作业性能与系统性能
DOI：
10.1109/cluster.2014.6968751
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Z. Lan
中科院分区：
文献类型：
--
作者：
Xu Yang;Zhou Zhou;Wei Tang;Xingwu Zheng;Jia Wang;Z. Lan
环面连接网络由于其单节点成本的线性伸缩性和具有竞争力的整体性能而被广泛应用于现代超级计算机中。作业调度系统对超级计算机的高效使用起着至关重要的作用。随着超级计算机规模的不断扩大，一个基本问题出现了：如何有效地平衡环面连接机器上的作业性能和系统性能？在这项工作中，我们将提出一种新的调度设计命名为基于窗口的本地感知调度。我们的设计包含三个新颖的特点。首先，而不是一个接一个的作业调度，我们的设计需要一个“窗口”的作业，即多个作业，考虑作业优先级和资源分配。其次，我们的设计维护一个插槽列表，以保存节点邻接信息的资源分配。最后，我们把调度问题转化为一个0-1多背包问题，并给出了两个求解算法。一系列基于跟踪的仿真结果表明，该调度方案具有真实的潜力，能够有效地平衡作业性能和系统性能。
Torus-connected network is widely used in modern supercomputers due to its linear per node cost scaling and its competitive overall performance. Job scheduling system plays a critical role for the efficient use of supercomputers. As supercomputers continue growing in size, a fundamental problem arises: how to effectively balance job performance with system performance on torus-connected machines? In this work, we will present a new scheduling design named window-based locality-aware scheduling. Our design contains three novel features. First, rather than one-by-one job scheduling, our design takes a “window” of jobs, i.e. multiple jobs, into consideration for job prioritizing and resource allocation. Second, our design maintains a list of slots to preserve node contiguity information for resource allocation. Finally, we formulate our scheduling decision making into a 0-1 Multiple Knapsack Problem and present two algorithms to solve the problem. A series of trace-based simulations using job logs collected from production supercomputers indicate that this new scheduling design has real potentials and can effectively balance job performance and system performance.