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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 multithreading is known as block, cooperative or coarse-grained multithreading. The goal of multithreading hardware support is to allow quick switching between a blocked thread and another thread ready to run. Switching from one thread to another means the hardware switches from using one register set to another.
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 ...
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.
A multitasking operating system may just switch between processes to give the appearance of many processes executing simultaneously (that is, in parallel), though in fact only one process can be executing at any one time on a single CPU (unless the CPU has multiple cores, then multithreading or other similar technologies can be used). [a]
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Multithreading is mainly found in multitasking operating systems. Multithreading is a widespread programming and execution model that allows multiple threads to exist within the context of one process. These threads share the process's resources, but are able to execute independently.
Evolutionary multi-tasking has been explored as a means of exploiting the implicit parallelism of population-based search algorithms to simultaneously progress multiple distinct optimization tasks. By mapping all tasks to a unified search space, the evolving population of candidate solutions can harness the hidden relationships between them ...