Quantifying Scheduling Challenges for Exascale System Software

Abstract

The move towards high-performance computing (HPC) applications comprised of coupled codes and the need to dramatically reduce data movement is leading to a reexamination of time-sharing vs. space-sharing in HPC systems. In this paper, we discuss and begin to quantify the performance impact of a move away from strict space-sharing of nodes for HPC applications. Specifically, we examine the potential performance cost of time-sharing nodes between application components, we determine whether a simple coordinated scheduling mechanism can address these problems, and we research how suitable simple constraint-based optimization techniques are for solving scheduling challenges in this regime. Our results demonstrate that current generalpurpose HPC system software scheduling and resource allocation systems are subject to significant performance deficiencies which we quantify for six representative applications. Based on these results, we discuss areas in which additional research is needed to meet the scheduling challenges of next-generation HPC systems.

An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

“…The authors of [32], describe some of the disadvantages of applying quadratic programming to the problem of mapping virtual machines to NUMA domains. They show that for a problem formulated as a nonconvex quadratic, the solutions are suboptimal, and that reducing the complexity by dividing the mapping problem into subproblems can improve the algorithm.…”

An evaluation of the state of time synchronization on leadership class supercomputers

“…However, as node counts increase, the likelihood of one task experiencing an interruption becomes a key factor to performance; as the BSP per‐loop iteration time decreases, the effects of even smaller noise perturbations increase, further emphasizing the usefulness of tightly synchronized clocks. () To mitigate the potentially disastrous hit on performance, sophisticated runtime systems and operating systems seek to overlap any disruption in progress across all compute nodes. When system software is able to schedule all interrupting tasks on every node such that they occur simultaneously, the cascading effect on the parallel algorithm is kept in check—a strategy known as coordinated scheduling .…”

“…The scheduler must guarantee precise CPU reservations for cooperative codes and accurate timing to run applications with coordination needs across nodes. () In this scenario, a precise inter‐node time agreement is critical. Moreover, the absence of suitable time agreement prevents effective gang‐scheduling and coordinated‐scheduling.…”