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On Linux, the CPU affinity of a process can be altered with the taskset(1) program [3] and the sched_setaffinity(2) system call. The affinity of a thread can be altered with one of the library functions: pthread_setaffinity_np(3) or pthread_attr_setaffinity_np(3). On SGI systems, dplace binds a process to a set of CPUs. [4]
CPU shielding is a practice where on a multiprocessor system or on a CPU with multiple cores, real-time tasks can run on one CPU or core while non-real-time tasks run on another. The operating system must be able to set a CPU affinity for both processes and interrupts .
An affinity mask is a bit mask indicating what processor(s) a thread or process should be run on by the scheduler of an operating system. [1] Setting the affinity mask for certain processes running under Windows can be useful as there are several system processes (especially on domain controllers) that are restricted to the first CPU / Core.
OpenMP is an application programming interface (API) that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran, [3] on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows.
In computer engineering, instruction pipelining is a technique for implementing instruction-level parallelism within a single processor. Pipelining attempts to keep every part of the processor busy with some instruction by dividing incoming instructions into a series of sequential steps (the eponymous "pipeline") performed by different processor units with different parts of instructions ...
[2] As a simple example, if a system is running code on a 2-processor system (CPUs "a" & "b") in a parallel environment and we wish to do tasks "A" and "B", it is possible to tell CPU "a" to do task "A" and CPU "b" to do task "B" simultaneously, thereby reducing the run time of the execution.
In the instance where for each task, its period is an exact multiple of every other task that has a shorter period, the task set can be thought of as being composed of n harmonic task subsets of size 1 and therefore =, which makes this generalization equivalent to Liu and Layland's least upper bound.
One benefit of a thread pool over creating a new thread for each task is that thread creation and destruction overhead is restricted to the initial creation of the pool, which may result in better performance and better system stability. Creating and destroying a thread and its associated resources can be an expensive process in terms of time.