In computer operating systems, a light-weight process (LWP) is a means of achieving multitasking. In the traditional meaning of the term, as used in Unix System V and Solaris, a LWP runs in user space on top of a single kernel thread and shares its address space and system resources with other LWPs within the same process. Multiple user-level threads, managed by a thread library, can be placed on top of one or many LWPs - allowing multitasking to be done at the user level, which can have some performance benefits.
In some operating systems, there is no separate LWP layer between kernel threads and user threads. This means that user threads are implemented directly on top of kernel threads. In those contexts, the term "light-weight process" typically refers to kernel threads and the term "threads" can refer to user threads. On Linux, user threads are implemented by allowing certain processes to share resources, which sometimes leads to these processes to be called "light weight processes". Similarly, in SunOS version 4 onwards (prior to Solaris) "light weight process" referred to user threads.
Kernel threads are handled entirely by the kernel. They need not be associated with a process; a kernel can create them whenever it needs to perform a particular task. Kernel threads cannot execute in user mode. LWPs (in systems where they are a separate layer) bind to kernel threads and provide a user-level context. This includes a link to the shared resources of the process to which the LWP belongs. When a LWP is suspended, it needs to store its user-level registers until it resumes, and the underlying kernel thread must also store its own kernel-level registers.
LWPs are slower and more expensive to create than user threads. Whenever an LWP is created a system call must first be made to create a corresponding kernel thread, causing a switch to kernel mode. These mode switches would typically involve copying parameters between kernel and user space, also the kernel may need to have extra steps to verify the parameters to check for invalid behavior.
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.
Multiprocessors are a core component in all types of computing infrastructure, from phones to datacenters. This course will build on the prerequisites of processor design and concurrency to introduce
Go is a statically typed, compiled high-level programming language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson. It is syntactically similar to C, but also has memory safety, garbage collection, structural typing, and CSP-style concurrency. It is often referred to as Golang because of its former domain name, golang.org, but its proper name is Go. There are two major implementations: Google's self-hosting "gc" compiler toolchain, targeting multiple operating systems and WebAssembly.
In computer programming, a green thread (virtual thread) is a thread that is scheduled by a runtime library or virtual machine (VM) instead of natively by the underlying operating system (OS). Green threads emulate multithreaded environments without relying on any native OS abilities, and they are managed in user space instead of kernel space, enabling them to work in environments that do not have native thread support. Green threads refers to the name of the original thread library for the programming language Java (that was released in version 1.
In computing, a context switch is the process of storing the state of a process or thread, so that it can be restored and resume execution at a later point, and then restoring a different, previously saved, state. This allows multiple processes to share a single central processing unit (CPU), and is an essential feature of a multiprogramming or multitasking operating system. In a traditional CPU, each process - a program in execution - utilizes the various CPU registers to store data and hold the current state of the running process.
In a non-uniform memory access machine, the placement of software threads to hardware cores can have a significant effect on the performance of concurrent applications. Detecting the best possible placement for each application is a necessity for thread sc ...
Covers the basics of parallel programming, including concurrency, forms of parallelism, synchronization, and programming models like PThreads and OpenMP.
Modern data-center network operating systems rely on proprietary user-space daemons wrapping SDKs from switch vendors. Linux-based variants of these operating systems have benefited from increasing and simplified dataplane offloading support in recent year ...
2018
,
We present a lightweight library for testing concurrent Scala programs by systematically exploring multiple interleavings between user-specified operations on shared objects. Our library is targeted at beginners of concurrent programming in Scala, runs on ...