Extending SLURM for Dynamic Resource-Aware Adaptive Batch Scheduling

TL;DR

This paper extends the SLURM batch system to support malleable jobs using Invasive MPI, enabling dynamic resource management for improved performance and power efficiency in HPC systems.

Contribution

It introduces a new adaptive parallel paradigm called Invasive MPI and implements two malleable job scheduling strategies in SLURM for performance and power optimization.

Findings

Performance-aware scheduling improves makespan and system utilization.

Power-aware strategy enables dynamic power corridor management.

Enhanced resource adaptivity benefits HPC system efficiency.

Abstract

With the growing constraints on power budget and increasing hardware failure rates, the operation of future exascale systems faces several challenges. Towards this, resource awareness and adaptivity by enabling malleable jobs has been actively researched in the HPC community. Malleable jobs can change their computing resources at runtime and can significantly improve HPC system performance. However, due to the rigid nature of popular parallel programming paradigms such as MPI and lack of support for dynamic resource management in batch systems, malleable jobs have been largely unrealized. In this paper, we extend the SLURM batch system to support the execution and batch scheduling of malleable jobs. The malleable applications are written using a new adaptive parallel paradigm called Invasive MPI which extends the MPI standard to support resource-adaptivity at runtime. We propose two malleable job scheduling strategies to support performance-aware and power-aware dynamic reconfiguration decisions at runtime. We implement the strategies in SLURM and evaluate them on a production HPC system. Results for our performance-aware scheduling strategy show improvements in makespan, average system utilization, average response, and waiting times as compared to other scheduling strategies. Moreover, we demonstrate dynamic power corridor management using our power-aware strategy.