DMRlib: Easy-coding and Efficient Resource Management for Job Malleability

TL;DR

DMRlib is a library that simplifies the development of malleable applications, enabling efficient resource management and significantly improving throughput in data centers.

Contribution

It introduces a minimalist MPI-like library with predefined communication patterns to facilitate process malleability adoption.

Findings

Resource allocation rate increased by over 3x with malleability.

Malleable jobs improved energy efficiency and throughput.

Demonstrated positive impact across various scalability scenarios.

Abstract

Process malleability has proved to have a highly positive impact on the resource utilization and global productivity in data centers compared with the conventional static resource allocation policy. However, the non-negligible additional development effort this solution imposes has constrained its adoption by the scientific programming community. In this work, we present DMRlib, a library designed to offer the global advantages of process malleability while providing a minimalist MPI-like syntax. The library includes a series of predefined communication patterns that greatly ease the development of malleable applications. In addition, we deploy several scenarios to demonstrate the positive impact of process malleability featuring different scalability patterns. Concretely, we study two job submission modes (rigid and moldable) in order to identify the best-case scenarios for malleability using metrics such as resource allocation rate, completed jobs per second, and energy consumption. The experiments prove that our elastic approach may improve global throughput by a factor higher than 3x compared to the traditional workloads of non-malleable jobs.