Introduction

Rocks Cluster Distribution is an open source software toolkit designed to simplify the deployment, management, and maintenance of high performance computing (HPC) clusters. For researchers, system administrators, and educators, building and sustaining a cluster environment can be daunting. Rocks addresses these challenges by providing pre-configured packages, automated installation tools, and a modular architecture. In this article, we explore what Rocks is, how it works, its intended orientations, and several curiosities that make it unique in the world of HPC.

What is Rocks Cluster Distribution?

Definition

Rocks Cluster Distribution is a Linux distribution derived from Red Hat Enterprise Linux or CentOS. It includes a set of customized scripts and “rolls” (modular add-ons) to automate the installation and management of compute clusters. By combining operating system packages with cluster-specific tools, Rocks transforms commodity hardware into a cohesive HPC environment.

History and Origins

Rocks was born in the late 1990s at the San Diego Supercomputer Center (SDSC). The goal was to lower the barrier to entry for universities and labs seeking to build HPC resources without extensive system administration expertise. Early releases focused on streamlining the installation of Beowulf-style clusters. Over time, Rocks expanded features, added support for additional hardware and networking fabrics, and fostered an active community of contributors.

How Rocks Cluster Distribution Works

System Architecture

Rocks employs a master-controller model. A designated “front-end” node manages system configuration, user authentication, and package distribution. Compute nodes are deployed under the control of this front-end.

Front-end Node

The front-end node handles:

• Kickstart Server: Automates OS installation on compute nodes via PXE boot and Kickstart files.
• Roll Management: Installs, upgrades, and removes rolls containing middleware, libraries, and applications.
• Cluster Services: Manages DNS, DHCP, NFS, batch schedulers (such as TORQUE or SLURM), and monitoring daemons.

Compute Nodes

Compute nodes provide the raw CPU, memory, and local storage used for computational workloads. They boot via network instructions from the front-end and join the cluster automatically. Nodes receive:

• Operating system installation.
• Rolls and software packages.
• Configuration files (such as /etc/hosts and scheduler settings).

Storage Nodes

Though optional, Rocks can integrate dedicated storage nodes. These nodes export filesystems via NFS or provide parallel file systems (for example, Lustre) to accelerate I/O-bound applications.

Installation and Configuration

Rocks streamlines cluster provisioning through a two-stage process:

1. Install the front-end node from a Rocks ISO. During installation, administrators define basic network parameters, hostnames, and select initial rolls.
2. Boot compute nodes using a PXE environment. Each node fetches Kickstart definitions from the front-end, installs the base OS, applies rolls, and reboots into a ready-to-use state.

Post-installation, the rocks command-line tool manages the cluster:

• rocks list: View installed rolls, nodes, and services.
• rocks enable: Add new node profiles to the cluster.
• rocks run roll: Deploy new software stacks across all nodes.
• rocks sync host: Synchronize configuration changes to nodes.

Package and Roll System

Rocks divides software into rolls, which are collections of RPMs, scripts, and configuration templates. Common rolls include:

• Roll-Base: Core OS and cluster middleware.
• Roll-MPI: Message Passing Interface libraries (MVAPICH, OpenMPI).
• Roll-GPU: CUDA drivers and GPU utilities.
• Roll-Lustre: Parallel file system support.

Rolls can be custom-built to integrate proprietary applications or specialized libraries. The modular design ensures that adding or removing functionality is as simple as enabling or disabling rolls.

Orientation and Use Cases

Rocks is tailored to environments where rapid deployment, scalability, and reproducibility are critical.

Scientific Research

Research labs in physics, chemistry, biology, and climate science often rely on high-throughput computing. Rocks enables scientists to:

• Quickly assemble clusters for simulation workloads.
• Manage software versions consistently through roll snapshots.
• Integrate specialized hardware such as GPUs or FPGAs.

Education and Training

Universities teaching parallel and distributed computing use Rocks to:

• Provide students with hands-on cluster experience.
• Deploy classroom clusters that can be reset between lab sessions.
• Offer turnkey environments for workshops on MPI, Hadoop, or OpenMP.

Commercial Applications

Firms in finance, engineering, and data analytics deploy Rocks clusters for:

• Batch risk modeling and Monte Carlo simulations.
• Rendering farms for animation and visual effects.
• Big data processing using Hadoop and Spark rolls.

Key Features

• Automated Provisioning: PXE and Kickstart integration eliminates manual node setup.
• Modularity: Rolls allow targeted addition of middleware, libraries, and applications.
• Scalability: Supports clusters from a handful of nodes to thousands.
• Consistency: Centralized configuration prevents “drift” between nodes.
• Community Support: Active mailing lists, user forums, and periodic workshops.
• Hardware Agnostic: Works with x86, x86_64, and ARM architectures (with community ports).
• Integration with Modern Schedulers: Native support for SLURM, PBS, TORQUE, and other workload managers.

Comparison with Other Distributions

Curiosities

• “Rolls” Concept Inspired by Cake Layers: The name derives from layering software stacks like the layers of a cake.
• Embedded Campus Clusters: Several universities maintain permanently installed Rocks clusters dedicated to teaching parallel programming.
• International Deployments: Rocks has been used in Brazil, India, Europe, and Africa to democratize HPC access in developing countries.
• Custom APIs: Some sites have built RESTful interfaces on top of the rocks command to integrate with modern CI/CD pipelines.
• ARM Support: Community-driven ports enable clusters of ARM boards such as Raspberry Pi or ODROID devices.

Conclusion

Rocks Cluster Distribution remains a robust solution for organizations seeking a rapid, reliable method to deploy HPC clusters. Its modular roll system, automated provisioning, and active community support make it an ideal choice for scientific research, education, and commercial computing. By abstracting complex tasks into simple commands and providing a standardized framework, Rocks continues to lower the barriers of entry to high performance computing.

References

• Rocks Cluster Distribution Official Site: http://www.rocksclusters.org/
• Rocks Documentation: http://rocks.org/docs/
• OpenHPC Project: https://openhpc.community/