Scheduling Beyond CPUs for HPC

Abstract

High performance computing (HPC) is undergoing significant changes. The emerging HPC applications comprise both compute-and dataintensive applications. To meet the intense I/O demand from emerging data-intensive applications, burst buffers are deployed in production systems. Existing HPC schedulers are mainly CPU-centric. The extreme heterogeneity of hardware devices, combined with workload changes, forces the schedulers to consider multiple resources (e.g., burst buffers) beyond CPUs, in decision making. In this study, we present a multi-resource scheduling scheme named BB-Sched that schedules user jobs based on not only their CPU requirements, but also other schedulable resources such as burst buffer. BBSched formulates the scheduling problem into a multi-objective optimization (MOO) problem and rapidly solves the problem using a multi-objective genetic algorithm. The multiple solutions generated by BBSched enables system managers to explore potential tradeoffs among various resources, and therefore obtains better utilization of all the resources. The trace-driven simulations with real system workloads demonstrate that BBSched improves scheduling performance by up to 41% compared to existing methods, indicating that explicitly optimizing multiple resources beyond CPUs is essential for HPC scheduling.

A Case For Intra-rack Resource Disaggregation in HPC

“…Further work has demonstrated reconigurable accelerators that rely on ield programmable gate arrays (FPGAs) [40,70] or ASICs [81]. Consequently, past work has examined how job scheduling should consider heterogeneous resource requests [8,30], how the operating system (OS) and runtime should adapt [42,57], how to write applications for heterogeneous systems [8,32], how to partition data-parallel applications onto heterogeneous compute resources [48], how to consider the diferent fault tolerances of heterogeneous resources [41], how to fairly compare the performance of diferent heterogeneous systems [44], and what the impact of heterogeneous resources is to application performance [52,74,80].…”

Job Scheduling in High Performance Computing

“…Therefore, multi-resource HPC scheduling demands more complicated scheduling methods. Optimization methods, especially multiobjective optimization methods, are leveraged to achieve better system performance in HPC scheduling [10], [11], [21].…”

DRAS-CQSim: A reinforcement learning based framework for HPC cluster scheduling

“…The underlying CQSim scheduling simulator has been successfully supporting a number of projects in this field over a decade [8,[19][20][21][22][23][24][25][26][27][28][29][30]. CQSim provides a unified platform to evaluate the performance of various methods with minimal overheads.…”

“…By identifying these factors, the system administrators could develop new policies to minimize the impacts of these factors. [20,21,23,26] aim to find the scheduling strategies to handle multiple resources, i.e., CPU, burst buffer, GPU, and power. CQSim plays a crucial role in these multi-resource projects, because CQSim simulator provides a virtual configurable platform to identify the best scheduling policy to schedule specific resources on a given system before deployment on real systems.…”