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What's a cluster?#

A computing cluster is a federation of multiple compute nodes (independent computers), most commonly linked together through a high-performance interconnect network.

What makes it a "super-computer" is the ability for a program to address resources (such as memory, CPU cores) located in different compute nodes, through the high-performance interconnect network.


On a computing cluster, users typically connect to login nodes, using a secure remote login protocol such as SSH. Unlike in traditional interactive environments, users then need to prepare compute jobs to submit to a resource scheduler. Based on a set of rules and limits, the scheduler will then try to match the jobs' resource requirements with available resources such as CPUs, memory or computing accelerators such as GPUs. It will then execute the user defined tasks on the selected resources, and generate output files in one of the different storage locations available on the cluster, for the user to review and analyze.

Cluster components#

The terms that are typically used to describe cluster components could be confusing, so in an effort to clarify things, here's a schema of the most important ones, and their definition. components


A Central Processing Unit (CPU), or core, or CPU core, is the smallest unit in a microprocessor that can carry out computational tasks, that is, run programs. Modern processors typically have multiple cores.


A socket is the connector that houses the microprocessor. By extension, it represents the physical package of a processor, that typically contains multiple cores.


A node is a physical, stand-alone computer, that can handle computing tasks and run jobs. It's connected to other compute nodes via a fast network interconnect, and contains CPUs, memory and devices managed by an operating system.


A cluster is the complete collection of nodes with networking and file storage facilities. It's usually a group of independent computers connected via a fast network interconnect, managed by a resource manager, which acts as a large parallel computer.

Other commonly used terms#

To make this documentation more accessible, we try to explain key terms in a non-technical way. When reading these pages, please keep in mind the following definitions, presented in alphabetical order:


An application is a computer program designed to perform a group of coordinated functions, tasks, or activities for the benefit of the user. In the context of scientific computing, an application typically performs computations related to a scientific goal (molecular dynamics simulations, genome assembly, compuational fluid dynamics simulations, etc).


Backfill scheduling is a method that a scheduler can use in order to maximize utilization. It allows smaller (both in terms of size and time requirements), lower priority jobs to start before larger, higher priority ones, as long as doing so doesn't push back the higher-priority jobs expected start time.


A binary (or executable) program refers to the machine-code compiled version of an application. This is which is a binary file that a computer can execute directly. As opposed to the application source code, which is the human-readable version of the application internal instructions, and which needs to be compiled by a compiler to produce the executable binary.


A resource scheduler ranks jobs by priority for execution. Each job's priority in queue is determined by multiple factors, among which one being the user's fairshare score. A user's fairshare score is computed based on a target (the given portion of the resources that this user should be able to use) and the user's effetive usage, ie the amount of resources (s)he effectively used in the past. As a result, the more resources past jobs have used, the lower the priority of the next jobs will be. Past usage is computed based on a sliding window and progressively forgotten over time. This enables all users on a shared resource to get a fair portion of it for their own use, by giving higher priority to users who have been underserved in the past.


Floating-point Operations Per Second (FLOPS) are a measure of computing performance, and represent the number of floating-point operations that a CPU can perform each second. Modern CPUs and GPUs are capable of doing TeraFLOPS (10^12 floating-point operations per second), depending on the precision of those operations (half-precision: 16 bits, single-precision: 32 bits, double-precision: 64 bits).


A Graphical Processing Unit (GPU) is a specialized device initially designed to generate graphical output. On modern computing architecture, they are used to accelerate certain types of computation, which they are much faster than CPUs at. GPUs have their own memory, and are attached to CPUs, within a node. Each compute node can host one or more GPUs.


High Performance Computing (HPC) refers to the practice of aggregating computing power to achieve higher performance that would be possible by using a typical computer.


Infiniband is a networking standard that features high bandwidth and low latency. The current Infiniband devices are capable of transferring data at up to 200 Gbits/sec with less than a microsecond latency. As of this writing, the popular Infiniband versions are HDR (High Data Rate) with 200 Gbits/sec and EDR (Enhanced Data Rate) with 100 Gbits/sec.


Input/output operations per second (IOPS, pronounced eye-ops) is an input/output performance measurement used to characterize computer storage system performance.


A job, or batch job, is the scheduler’s base unit of computing by which resources are allocated to a user for a specified amount of time. Users create job submission scripts to ask the scheduler for resources such as cores, memory, runtime, etc. The scheduler puts the requests in a queue and allocates requested resources based on jobs’ priority.

Job step#

Job steps are sets of (possibly parallel) tasks within a job

Login nodes#

Login nodes are points of access to a compute cluster. Users usually connect to login nodes via SSH to compile and debug their code, review their results, do some simple tests, and submit their batch jobs to the parallel computer.

Login nodes are not for computing

Login nodes are usually shared among many users and therefore must not be used to run computationally intensive tasks. Those should be submitted to the scheduler which will dispatch them on compute nodes.


Environment modules, or software modules, are a type of software management tool used on in most HPC environments. Using modules enable users to selectively pick the software that they want to use and add them to their environment. This allows to switch between different versions or flavors of the same software, pick compilers, libraries and software components and avoid conflicts between them.


Message Passing Interface (MPI) is a standardized and portable message-passing system designed to exchange information between processes running on different nodes. There are several implementations of the MPI standard, which is the most common way used to scale parallel applications beyond a single compute node.


Open Multi Processing (OpenMP) is a parallel programming model designed for shared memory architecture. It's based on pragmas that can be added in applications to let the compiler generate a code that can run on multiple cores, within the same node.


A partition is a set of compute nodes within a cluster with a common feature. For example, compute nodes with GPU, or compute nodes belonging to same owner, could form a partition.

On Sherlock, you can see detailed partition information with the sh_part or sinfo commands.


A Quality Of Service (QOS) is the set of rules and limitations that apply to a categories of job. The combination of a partition (set of machines where a job can run) and QOS (set of rules that applies to that job) makes what is often referred to as a scheduler queue.

Run time#

The run time, or walltime, of a job is the time required to finish its execution.


The goal of a job scheduler is to find the appropriate resources to run a set of computational tasks in the most efficient manner. Based on resource requirements and job descriptions, it will prioritize those jobs, allocate resources (nodes, CPUs, memory) and schedule their execution.


Simple Linux Utility for Resource Management (SLURM) is a software that manages computing resources and schedule tasks on them. Slurm coordinates running of many programs on a shared facility and makes sure that resources are used in an optimal manner.


Secure Shell (SSH) is a protocol to securely access remote computers. Based on the client-server model, multiple users with an SSH client can access a remote computer. Some operating systems such as Linux and Mac OS have a built-in SSH client and others can use one of many publicly available clients.


A process, in the simplest terms, is an executing program. One or more threads run in the context of the process. A thread is the basic unit to which the operating system allocates processor time. A thread can execute any part of the process code, including parts currently being executed by another thread. Threads are co-located on the same node.


In the Slurm context, a task is to be understood as a process. A multi-process program is made of several tasks. A task is typically used to schedule a MPI process, that in turn can use several CPUs. By contrast, a multi-threaded program is composed of only one task, which uses several CPUs.