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      Use Cases for Linode Dedicated CPU Instances


      Updated by Linode

      Written by Ryan Syracuse

      Why Dedicated CPU

      Dedicated CPU Linodes offer a complement to CPU intensive tasks, and have the potential to significantly reduce issues that arise from shared cloud hosting environments. Normally, when creating a Linode via our standard plan, you are paying for access to virtualized CPU cores, which are allocated to you from a host’s shared physical CPU. While a standard plan is designed to maximize performance, the reality of a shared virtualized environment is that your processes are scheduled to use the same physical CPU cores as other customers. This can produce a level of competition that results in CPU steal, or a higher wait time from the underlying hypervisor to the physical CPU.

      CPU Steal can be defined more strictly as a measure of expected CPU cycles against actual CPU cycles as your virtualized environment is scheduled access to the physical CPU. Although this number is generally small enough that it does not heavily impact standard workloads and use cases, if you are expecting high and constant consumption of CPU resources, you are at risk of being negatively impacted by CPU Steal.

      Dedicated CPU Linodes have private access to entire physical CPU cores, meaning no other Linodes will have any processes on the same cores you’re using. Dedicated CPUs are therefore exempt from any competition for CPU resources and the potential problems that could arise because of CPU steal. Depending on your workload, you can experience an improvement in performance by using Dedicated CPU.

      Dedicated CPU Use Cases

      While a standard plan is usually a good fit for most use cases, a Dedicated CPU Linode may be recommended for a number of workloads related to high and constant CPU processing. Such examples include:

      CI/CD Toolchains and Build Servers

      CI and CD are abbreviations for Continuous Integration and Continuous Delivery, respectively, and refer to an active approach to DevOps that reduces overall workloads by automatically testing and regularly implementing small changes. This can help to prevent last-minute conflicts and bugs, and keeps tasks on schedule. For more information on the specifics of CI and CD, see our Introduction to CI/CD Guide.

      In many cases, the CI/CD pipeline can become resource-intensive if many new code changes are built and tested against your build server. When a Linode is used as a remote server and is expected to be regularly active, a Dedicated CPU Linode can add an additional layer of speed and reliability to your toolchain.

      Game Servers

      Depending on the intensity of demands they place on your Linode, game servers may benefit from a Dedicated CPU. Modern multiplayer games need to coordinate with a high number of clients, and require syncing entire game worlds for each player. If CPU resources are not available, then players will experience issues like stuttering and lag. Below is a short list of popular games that may benefit from a Dedicated CPU:

      Audio and Video Transcoding

      Audio and Video Transcoding (AKA Video/Audio Encoding) is the process of taking a video or audio file from its original or source format and converting it to another format for use with a different device or tool. Because this is often a time-consuming and resource-intensive task, a Dedicated CPU or Dedicated GPU Linode are suggested to maximize performance. FFmpeg is a popular open source tool used specifically for the manipulation of audio and video, and is recommended for a wide variety of encoding tasks.

      Big Data and Data Analysis

      Big Data and Data Analysis is the process of analyzing and extracting meaningful insights from datasets so large they often require specialized software and hardware. Big data is most easily recognized with the the “three V’s” of big data:

      • Volume: Generally, if you are working with terabytes, petabytes, exabytes, or more amounts of information you are in the realm of big data.
      • Velocity: With Big Data, you are using data that is being created, called, moved, and interacted with at a high velocity. One example is the real time data generated on social media platforms by their users.
      • Variety: Variety refers to the many different types of data formats with which you may need to interact. Photos, video, audio, and documents can all be written and saved in a number of different formats. It is important to consider the variety of data that you will collect in order to appropriately categorize it.

      Processing big data is often especially hardware-dependent. A Dedicated CPU can give you access to the isolated resources often required to complete these tasks.

      The following tools can be extremely useful when working with big data:

      • Hadoop – an Apache project for the creation of parallel processing applications on large data sets, distributed across networked nodes.

      • Apache Spark – a unified analytics engine for large-scale data processing designed with speed and ease of use in mind.

      • Apache Storm – a distributed computation system that processes streaming data in real time.

      Scientific Computing

      Scientific Computing is a term used to describe the process of using computing power to solve complex scientific problems that are either impossible, dangerous, or otherwise inconvenient to solve via traditional means. Often considered the “Third Pillar” of modern science behind Theoretical Analysis and Experimentation, Scientific Computing has quickly become a prevalent tool in scientific spaces.

      Scientific Computing involves many intersecting skills and tools for a wide array of more specific use cases, though solving complex mathematical formulas dependent on significant computing power is considered to be standard. While there are a large number of open source software tools available, below are two general purpose tools we can recommend to get started with Scientific Computing.

      It’s worth keeping in mind that, beyond general use cases, there are many more example of tools and software available and often designed for individual fields of science.

      Machine Learning

      Machine learning is a powerful approach to data science that uses large sets of data to build prediction algorithms. These prediction algorithms are commonly used in “recommendation” features on many popular music and video applications, online shops, and search engines. When you receive intelligent recommendations tailored to your own tastes, machine learning is often responsible. Other areas where you might find machine learning being used are in self-driving cars, process automation, security, marketing analytics, and health care.

      Below is a list of common tools used for machine learning and AI that can be installed on a Linode CPU instance:

      • TensorFlow – a free, open-source, machine learning framework and deep learning library. Tensorflow was originally developed by Google for internal use and later fully released to the public under the Apache License.

      • PyTorch – a machine learning library for Python that uses the popular GPU-optimized Torch framework.

      • Apache Mahout – a scalable library of machine learning algorithms and distributed linear algebra framework designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms.

      Where to Go From Here

      If you’re ready to get started with a Dedicated CPU Linode, our Getting Started With Dedicated CPU guide will walk you through the process of an initial installation. Additionally, see our Pricing Page for a rundown of both hourly and monthly costs.

      Find answers, ask questions, and help others.

      This guide is published under a CC BY-ND 4.0 license.



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