Scheduling Parallel Quant Research Jobs with SLURM on a Raspberry Pi Cluster
Summary
This tutorial explains how to configure SLURM on a Raspberry Pi cluster so researchers can submit parallel workloads from a login node. It outlines the roles of the control node and computational nodes, shared configuration through NFS, resource allocation by CPU cores, and job queuing when demand exceeds available capacity. The example use cases include QSTrader backtest parameter sweeps and derivatives pricing workloads.
The setup walks through configuring node addresses, a partition, consumable core resources, cgroup limits, and Munge authentication, then enabling the relevant services on each machine. It demonstrates checking cluster availability and running a distributed hostname task as a basic operational check. The example describes a small cluster with three compute nodes, but the article is primarily systems configuration guidance rather than a trading method. Its device permissions are explicitly permissive, and configuration details may depend on the operating system and SLURM version in use.
Key ideas
- SLURM queues and schedules parallel tasks against the resources available in a cluster.
- A control node manages workload submissions while separate compute nodes execute jobs.
- Shared configuration and Munge authentication help the nodes coordinate securely.
- A partition groups compute nodes that SLURM can use for submitted workloads.
- A basic distributed command can confirm that the cluster is responding to jobs.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.