Parallel Spawning Strategies for Dynamic-Aware MPI Applications

TL;DR

This paper introduces a novel parallel spawning strategy for MPI applications that efficiently manages dynamic resource reallocation by reusing processes and terminating unneeded ones, significantly reducing reconfiguration costs.

Contribution

It proposes a cooperative parallel spawning approach that overcomes existing limitations in MPI malleability, enabling efficient expansion and shrinking of resources during runtime.

Findings

Strategy maintains competitive expansion times with minimal overhead.

Enables shrink operations that are at least 1387 times faster.

Validates effectiveness on systems with equal and different core counts.

Abstract

Dynamic resource management is an increasingly important capability of High Performance Computing systems, as it enables jobs to adjust their resource allocation at runtime. This capability can reduce workload makespan, substantially decreasing job waiting times and optimizing resource allocation. In this context, malleability refers to the ability of applications to adapt to new resource allocations during execution. Although beneficial, malleability incurs significant reconfiguration costs, making the reduction of these costs an important research topic. Some existing solutions for MPI applications respawn the entire application, which is an expensive solution that avoids the reuse of original processes. Other MPI solutions reuse them, but fail to fully release unneeded processes when shrinking, since some ranks within the same communicator remain active across nodes, preventing the application from returning those nodes to the system. This work overcomes both limitations by proposing a novel parallel spawning strategy, in which all processes cooperate in the spawning. This allows expansions to reuse processes while also terminating unneeded ones. This strategy has been validated on two systems with either machines with equal or different numbers of cores. Experiments show that this strategy preserves competitive expansion times with at most a $1.13\times$ and $1.25\times$ overhead for equal and different number of cores per node, respectively. More importantly, it enables fast shrink operations that reduce their cost by at least $1387\times$ and $20\times$ in the same scenarios.