Shulou(Shulou.com)06/02 Report--

In the technology industry, the word innovation is almost as ubiquitous as revolution, so it is hard to distinguish the exaggerated from the really exciting. The Linux kernel is called innovation, but it is also called the biggest miracle in modern computing, a behemoth in the micro world.

Marketing and models aside, Linux is arguably the most popular kernel in the open source world, introducing some real rule changers in its nearly 30 years of life.

Cgroups (2.6.24)

Back in 2007, Paul Menage and Rohit Seth added esoteric control group (cgroups) functionality to the kernel (the current implementation of cgroups is rewritten by Tejun Heo). This new technology was originally used as a method, essentially to ensure the quality of service for a specific set of tasks.

For example, you can create a control group definition (cgroup) for all tasks associated with your WEB service, another cgroup for routine backups, and another cgroup for general operating system requirements. You can then control the percentage of resources for each group so that most of the system resources are available to your operating system and WEB services, while your backup process can access the remaining resources.

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However, cgroups has become so famous because of its role as a driver of cloud technology: containers. In fact, cgroups was originally named the process container. This is not surprising when they are adopted by projects such as LXC, CoreOS, and Docker.

Just like when the floodgates were opened, the word "container" became synonymous with Linux, and the microservice-style concept of cloud-based "application" quickly became the norm. Nowadays, it's hard to get rid of cgroups, they are so common. Every large-scale infrastructure (and possibly your laptop if you run Linux) uses cgroups in a reasonable way, making your computing experience more manageable and flexible than ever before.

For example, you may already have Flathub or Flatpak installed on your computer, or you may have used Kubernetes and / or OpenShift at work. In any case, if the term "container" is still vague to you, you can get the actual understanding of the container from behind through the Linux container.

LKMM (4.17)

In 2018, the hard work of Jade Alglave, Alan Stern, Andrea Parri, Luc Maranget, Paul McKenney, and several others was incorporated into the mainline Linux kernel to provide a formal memory model. The Linux kernel memory [consistency] model (LKMM) subsystem is a set of tools that describe the Linux memory consistency model and also generate use cases for testing (specifically named klitmus).

As systems become more complex in physical design (with more CPU cores, cache and memory growth, etc.), the harder it becomes to know which CPU needs which address space and when. For example, if CPU0 needs to write data to a shared variable in memory, and CPU1 needs to read that value, then CPU0 must write before CPU1 attempts to read. Similarly, if values are written to memory in one order, they are expected to be read in the same order, regardless of which or which CPU is being read.

Even on a single processor, memory management requires a specific sequence of tasks. A simple operation like x = y requires the processor to load the value of y from memory and then store that value in x. The value stored in y cannot be put into the x variable until the processor reads the value from memory. There is also address dependency: X [n] = 6 requires n to be loaded before the processor can store the value 6.

LKMM can help identify and track these memory patterns in your code. It is implemented in part through a tool called herd, which defines the constraints imposed by the memory model (in the form of logical formulas) and then enumerates all possible results that are consistent with those constraints.

Low latency patch (2.6.38)

A long time ago, before 2011, if you wanted to do multimedia work on Linux, you had to have a low-latency kernel. This mainly applies when recording adds a lot of real-time effects (such as singing into a microphone and adding a mix, and hearing your voice in the headset without delay). Some distributions, such as Ubuntu Studio, reliably provide such a kernel, so there is actually no obstacle, which is just an important reminder when artists choose a distribution.

However, if you are not using Ubuntu Studio, or if you need to update your kernel before your distribution is available, you must jump to the rt-patches page, download kernel patches, apply them to your kernel source code, compile, and then install them manually.

Later, with the release of kernel version 2.6.38, this process ended. The Linux kernel suddenly comes with low-latency code by default, like magic (latency has been reduced by at least 10 times according to the benchmark). You no longer need to download patches or compile. Everything went well because Mike Galbraith wrote a 200-line patch.

For open source multimedia artists around the world, this is a rule changer. Things got so good in 2011 that in 2016 I made my own challenge to build a digital audio workstation (DAW) on the raspberry pie v1 (model B) and found it to work surprisingly well.

RCU (2.5)

RCU, or read-copy-update Read-Copy-Update, is a system defined in computer science that allows multiple processor threads to read data from shared memory. It does this by delaying updates but also marking them as updated to ensure that the data is read as up-to-date. In effect, this means that reads and updates occur at the same time.

A typical RCU loop is something like this:

1. Delete the pointer to the data to prevent other reads from referencing it.

two。 Wait for the read operations to complete their critical processing.

3. Reclaim memory space.

Dividing the update phase into delete and recycling phases means that the updater performs deletions immediately, while delaying recycling until all active reads are complete (by blocking them or registering a callback to be called when complete).

Although the concept of RCU was not invented for the Linux kernel, its implementation in Linux is a defining example of the technology.

Cooperation (0.01)

The ultimate answer to the question of Linux kernel innovation is always collaboration. You can say this is a good time, or you can call it a technical advantage, called * capabilities, or simply open source, but the Linux kernel and many of the projects it supports are brilliant examples of collaboration and collaboration.

It goes far beyond the kernel. People from all walks of life have contributed to open source, it can be said that it is because of the Linux kernel. Linux was, and still is, the main force of free software, inspiring people to bring their code, art, ideas, or just themselves to a global, productive, diverse human community.

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