Deploying a Task-based Runtime System on Raspberry Pi Clusters

TL;DR

This paper explores deploying a task-based runtime system on Raspberry Pi clusters, focusing on configuration, performance limitations, network effects, and power consumption for HPC-like applications.

Contribution

It provides practical insights and lessons learned in configuring and benchmarking Raspberry Pi clusters with HPX/Phylanx for HPC workloads.

Findings

Configuration adjustments improve performance

Limited memory bandwidth constrains core utilization

Low network bandwidth impacts distributed performance

Abstract

Arm technology is becoming increasingly important in HPC. Recently, Fugaku, an \arm-based system, was awarded the number one place in the Top500 list. Raspberry Pis provide an inexpensive platform to become familiar with this architecture. However, Pis can also be useful on their own. Here we describe our efforts to configure and benchmark the use of a Raspberry Pi cluster with the HPX/Phylanx platform (normally intended for use with HPC applications) and document the lessons we learned. First, we highlight the required changes in the configuration of the Pi to gain performance. Second, we explore how limited memory bandwidth limits the use of all cores in our shared memory benchmarks. Third, we evaluate whether low network bandwidth affects distributed performance. Fourth, we discuss the power consumption and the resulting trade-off in cost of operation and performance.