Search results
Results From The WOW.Com Content Network
Multitasking is a common feature of computer operating systems since at least the 1960s. It allows more efficient use of the computer hardware; when a program is waiting for some external event such as a user input or an input/output transfer with a peripheral to complete, the central processor can still be used with another program.
Even though it is very difficult to further speed up a single thread or single program, most computer systems are actually multitasking among multiple threads or programs. Thus, techniques that improve the throughput of all tasks result in overall performance gains. Two major techniques for throughput computing are multithreading and ...
Concurrency is pervasive in computing, occurring from low-level hardware on a single chip to worldwide networks. Examples follow. At the programming language level: Channel; Coroutine; Futures and promises; At the operating system level: Computer multitasking, including both cooperative multitasking and preemptive multitasking
Multitasking may refer to: Computer multitasking, the concurrent execution of multiple tasks (also known as processes) over a certain period of time Cooperative multitasking; Pre-emptive multitasking; Human multitasking, the apparent performance by an individual of handling more than one task at the same time
[[{{#File:Atanasoff-Berry Computer at Durhum Center.jpg|thumb|Atanasoff–Berry computer, the first computer with parallel processing [1]]] Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program. More specifically, ILP refers to the average number of instructions run per ...
Coarse-grain multithreading is more common for less context switch between threads. For example, Intel's Montecito processor uses coarse-grained multithreading, while Sun's UltraSPARC T1 uses fine-grained multithreading. For those processors that have only one pipeline per core, interleaved multithreading is the only possible way, because it ...
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.
This type of multitasking is called cooperative because all programs must cooperate for the scheduling scheme to work. In this scheme, the process scheduler of an operating system is known as a cooperative scheduler whose role is limited to starting the processes and letting them return control back to it voluntarily.