Table of Contents
Historical context and the rise of Linux in HPC
High performance computing started in an era dominated by proprietary Unix systems and custom operating systems from vendors such as Cray, IBM, and SGI. Each vendor often provided its own OS variant, its own tools, and its own way of managing resources. This made HPC environments fragmented and expensive to maintain.
Linux began as a free Unix-like system for commodity hardware. Over time it acquired features that made it robust enough for servers, and then for clusters. When inexpensive x86 processors became fast enough for scientific workloads, researchers started to assemble “Beowulf” clusters from off-the-shelf PCs, interconnected by standard networks, and running Linux. These early clusters showed that you could achieve impressive performance without proprietary hardware or software.
Vendors noticed that their customers increasingly demanded open, flexible, and cheaper platforms. Many major HPC systems gradually shifted away from custom operating systems to Linux-based solutions. Today, essentially all entries on the TOP500 list of the fastest supercomputers use some form of Linux, often heavily customized but still rooted in the same kernel and user space ecosystem that is available on ordinary servers and workstations.
Open source as a strategic advantage
Linux is open source, which means that its source code can be inspected, modified, and redistributed under well defined licenses. In HPC, this openness has several critical consequences.
System vendors and supercomputing centers can adapt Linux to very specific hardware, such as custom interconnects, new processor instruction sets, and exotic memory architectures. They are not limited to waiting for a proprietary vendor to implement support. Instead, they can hire engineers, collaborate with the community, or participate in consortia that maintain specialized patches.
Researchers in computer science can experiment directly with low level OS features. For example, they can modify memory management policies, design new scheduling algorithms, or extend networking stacks, then test these ideas on real clusters. This feedback loop between research and production systems accelerates innovation in HPC.
There is also a long-term stability advantage. With a proprietary OS, a vendor can discontinue support or change direction abruptly. With an open source system like Linux, HPC centers are less dependent on the strategy of a single company. Even if a particular vendor leaves the market, the source code, documentation, and tooling remain accessible.
Linux dominates HPC in large part because its open source nature allows deep customization, rapid innovation, and reduced dependence on single vendors while still providing a shared, standard platform.
Hardware flexibility and scalability
HPC systems range from small departmental clusters with a few nodes to national supercomputers with hundreds of thousands of compute nodes. These systems use a wide variety of processors, accelerators, and interconnects. Linux is capable of running across almost all of them.
The Linux kernel supports many CPU architectures used in HPC such as x86_64, ARM, POWER, and RISC-V. It also integrates with device drivers for high performance network interfaces, parallel filesystems, and GPUs. Vendors of specialized hardware often provide Linux drivers and libraries first, because they know that the majority of their customers operate Linux clusters.
This broad hardware support simplifies procurement. An institution can choose hardware based on price, performance, and energy efficiency, and still retain essentially the same OS environment. Linux scales well from a single-node workstation to a petascale or exascale machine. System administrators can manage a unified software stack across login nodes, compute nodes, and management nodes, which greatly reduces operational complexity.
Linux also exposes kernel features that are particularly important for scalability. Examples include control groups for resource isolation, NUMA awareness for memory placement, and scheduler hooks used by batch systems. These will be discussed more deeply elsewhere in the course, but at a high level they allow HPC clusters to run many jobs from many users concurrently while still providing predictable performance.
Customization, tuning, and control
HPC workloads differ significantly from typical server or desktop workloads. They often require predictable performance, low latency communication between nodes, and careful control of memory behavior. Linux is attractive in this context because it allows fine grained control of the operating system behavior.
System administrators can build custom kernels that include or exclude specific features in order to reduce overhead or improve stability. They can choose particular schedulers, tune memory management parameters, modify networking settings, and configure kernel options that influence interrupt handling and CPU affinity. These choices can have a measurable impact on the performance of scientific applications.
At user level, Linux provides a rich set of tools for controlling and observing processes. Users can pin processes or threads to specific cores, adjust environment variables that affect libraries and runtime systems, and query performance counters exposed by the hardware. Many of these capabilities are available on other Unix-like systems, but Linux tends to receive new features and optimizations first because of its dominant role in HPC.
Because Linux is modular, clusters can be stripped down to provide only what is necessary for computation. For instance, a compute node image may omit graphical components and unnecessary services in order to reduce background activity. This lean configuration can reduce jitter, which is the small but important variation in performance caused by extra OS work.
Integration with HPC software ecosystems
The majority of scientific and engineering software is developed and tested primarily on Linux. This includes compilers, MPI implementations, numerical libraries, parallel filesystems, batch schedulers, monitoring tools, and debuggers. As a result, Linux becomes the natural target for HPC deployments.
When researchers publish new algorithms or open source HPC codes, they often provide build instructions for Linux, rely on Linux-specific scripting, and assume standard Linux tools such as bash, gcc, and make. Porting these codes to other operating systems is possible, but it usually requires extra effort, and sometimes features are missing or slower.
Commercial vendors that serve the HPC market also treat Linux as the primary supported platform. Proprietary simulation codes, engineering analysis tools, and domain-specific packages frequently offer Linux cluster versions as their flagship products. Licensing models, deployment templates, and support procedures are optimized for Linux-based clusters.
Linux distributions that are popular in HPC, such as those derived from Red Hat, SUSE, or Debian, often include prepackaged versions of essential HPC components. System administrators can combine these with external repositories or vendor-provided stacks to build a consistent and maintainable environment.
Cost, licensing, and procurement considerations
Operating a large HPC system is expensive, and software licensing is an important part of the total cost. Linux offers attractive properties in this area because it is typically available without per-node or per-core licensing fees.
This economic advantage matters more as systems grow larger. For small clusters the OS cost might be modest, but at thousands of nodes proprietary OS licenses can become prohibitive. Linux allows institutions to invest more in hardware, networking, cooling, and staffing instead of operating system licenses.
Open source licensing also simplifies some aspects of collaboration. Institutions can share cluster images, configuration scripts, and management tools with fewer legal barriers. Community projects that build complete HPC software distributions can be used and modified freely, as long as they respect the underlying licenses.
Even when commercial support is purchased for enterprise-grade Linux distributions, the pricing models are usually more favorable to large scale deployments than traditional proprietary operating systems. This combination of flexibility and lower licensing burden has strongly influenced the adoption of Linux in HPC.
Unix philosophy and the command line culture
Linux inherits its design style from Unix, which emphasizes small tools that perform specific tasks and can be combined in flexible ways. This model fits naturally with the way many scientists and engineers work.
On a Linux system, users interact heavily with the command line. They launch jobs with scripts, manipulate data with pipelines, automate workflows with shell languages, and integrate external tools with relative ease. HPC schedulers, resource managers, and monitoring utilities are all designed with this environment in mind.
Although other operating systems can provide command line interfaces, the density and maturity of text based tools on Linux is exceptional. Utilities like grep, awk, sed, and ssh are standard parts of daily HPC work. Scripts that orchestrate complex simulations and analysis pipelines often assume a Linux shell environment.
The shared command line culture also helps with training and portability of skills. A user who learns basic Linux commands and scripting on a laptop can apply the same knowledge on a supercomputer. From the perspective of an HPC center, this reduces the barrier to entry for new users, since educational material, tutorials, and examples are widely available and usually written for Linux.
Vendor support and industry momentum
Once a platform becomes dominant in a field, it benefits from positive feedback. In HPC, Linux reached a point where most hardware vendors, software developers, and research institutions centered their efforts on it. This created strong industry momentum that further reinforced its dominance.
Hardware vendors design and validate their products first on Linux. They coordinate driver releases, firmware updates, and performance testing for Linux-based systems. Batch schedulers and cluster management tools emphasize Linux support and release their most advanced features there first.
Cloud providers that offer virtual clusters for HPC workloads commonly use Linux as the guest operating system. Researchers can prototype workloads in the cloud, using Linux images, and then migrate them to on-premises supercomputers with fewer changes.
Standardization efforts around MPI, OpenMP, and other parallel programming interfaces also tend to assume a Linux-like environment when defining example code and best practices. While these standards are technically portable, the fact that almost all reference implementations target Linux shapes the ecosystem.
Alignment with HPC requirements
Many of the core technical requirements of HPC align well with Linux design choices. High throughput I/O, efficient networking, large memory support, and robust process management are all areas where Linux performs strongly.
Linux networking stacks are designed to be extensible, which allows integration with specialized interconnect technologies and high performance communication libraries. Kernel features for asynchronous I/O and large page memory support cater to bandwidth intensive and memory hungry applications.
Power management and energy accounting capabilities in Linux help operators optimize for energy efficiency, an increasingly important dimension of HPC. Node level power caps, CPU frequency scaling policies, and thermal monitoring interfaces are often exposed through Linux-specific mechanisms.
Security models in Linux are also compatible with multi-user, shared cluster environments. Features such as user and group permissions, access control lists, and namespaces allow HPC centers to enforce security policies while still giving users sufficient flexibility to run complex workflows. Container technologies that have become important in HPC, such as Singularity and Apptainer, are deeply integrated with Linux kernel features.
Practical implications for beginners
For someone new to HPC, the dominance of Linux has very direct practical consequences. You will interact with Linux almost every time you use a cluster, even if your personal computer runs a different operating system. You will log in to remote Linux systems, navigate their directories, compile code using Linux compilers, and submit jobs using Linux-based schedulers.
It is therefore important to become comfortable with basic Linux command line usage and common tools. These skills are not only necessary but portable. Once you learn to use the shell, text editors, and file permissions on one Linux cluster, you can transfer that knowledge to almost any other HPC system in the world.
You do not need to be a Linux expert to start using HPC systems effectively. However, understanding why Linux is the standard platform will help you make sense of the environment you encounter. You will also appreciate why documentation, tutorials, and examples almost always assume a Linux context.
In the rest of this part of the course, we will focus on the practical aspects of working in a Linux environment on HPC clusters. You will learn how to use basic commands, navigate filesystems, and interact with software stacks that rely on Linux features.