In computing, a loadable kernel module (LKM) is an that contains code to extend the running kernel, or so-called base kernel, of an operating system. LKMs are typically used to add support for new hardware (as device drivers) and/or s, or for adding system calls. When the functionality provided by an LKM is no longer required, it can be unloaded in order to free memory and other resources.
Most current Unix-like systems and Microsoft Windows support loadable kernel modules under different names, such as kernel loadable module (kld) in FreeBSD, kernel extension (kext) in macOS (although support for third-party modules is being dropped), kernel extension module in AIX, dynamically loadable kernel module in HP-UX, kernel-mode driver in Windows NT and downloadable kernel module (DKM) in VxWorks. They are also known as kernel loadable modules (or KLM), and simply as kernel modules (KMOD).
Without loadable kernel modules, an operating system would have to include all possible anticipated functionality compiled directly into the base kernel. Much of that functionality would reside in memory without being used, wasting memory, and would require that users rebuild and reboot the base kernel every time they require new functionality.
One minor criticism of preferring a modular kernel over a static kernel is the so-called fragmentation penalty. The base kernel is always unpacked into real contiguous memory by its setup routines; thus, the base kernel code is never fragmented. Once the system is in a state in which modules may be inserted, for example once the s have been mounted that contain the modules, it is likely that any new kernel code insertion will cause the kernel to become fragmented, thereby introducing a minor performance penalty by using more TLB entries, causing more TLB misses.
Loadable kernel modules in Linux are loaded (and unloaded) by the modprobe command. They are located in /lib/modules or /usr/lib/modules and have had the extension .ko ("kernel object") since version 2.6 (previous versions used the .
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
The kernel is a computer program at the core of a computer's operating system and generally has complete control over everything in the system. It is the portion of the operating system code that is always resident in memory and facilitates interactions between hardware and software components. A full kernel controls all hardware resources (e.g. I/O, memory, cryptography) via device drivers, arbitrates conflicts between processes concerning such resources, and optimizes the utilization of common resources e.
FreeBSD is a free and open-source Unix-like operating system descended from the Berkeley Software Distribution (BSD). The first version of FreeBSD was released in 1993. In 2005, FreeBSD was the most popular open-source BSD operating system, accounting for more than three-quarters of all installed and permissively licensed BSD systems. FreeBSD has similarities with Linux, with two major differences in scope and licensing: FreeBSD maintains a complete system, i.e.
Linux (ˈlɪnʊks ) is a family of open-source Unix-like operating systems based on the Linux kernel, an operating system kernel first released on September 17, 1991, by Linus Torvalds. Linux is typically packaged as a Linux distribution, which includes the kernel and supporting system software and libraries, many of which are provided by the GNU Project. Many Linux distributions use the word "Linux" in their name, but the Free Software Foundation uses the name "GNU/Linux" to emphasize the use and importance of GNU software in many distributions, causing some controversy.
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and imple
This is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternat
Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...
Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic r ...
Fuzzing reliably and efficiently finds bugs in software, including operating system kernels. In general, higher code coverage leads to the discovery of more bugs. This is why most existing kernel fuzzers adopt strategies to generate a series of inputs that ...