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Busy-waiting itself can be made much less wasteful by using a delay function (e.g., sleep()) found in most operating systems. This puts a thread to sleep for a specified time, during which the thread will waste no CPU time. If the loop is checking something simple then it will spend most of its time asleep and will waste very little CPU time.
The running time of LPT is dominated by the sorting, which takes O(n log n) time, where n is the number of inputs. LPT is monotone in the sense that, if one of the input numbers increases, the objective function (the largest sum or the smallest sum of a subset in the output) weakly increases. [2] This is in contrast to Multifit algorithm.
In computer programming, the async/await pattern is a syntactic feature of many programming languages that allows an asynchronous, non-blocking function to be structured in a way similar to an ordinary synchronous function.
The function that accepts a callback may be designed to store the callback so that it can be called back after returning which is known as asynchronous, non-blocking or deferred. Programming languages support callbacks in different ways such as function pointers, lambda expressions and blocks.
Illustration of the dining philosophers problem. Each philosopher has a bowl of spaghetti and can reach two of the forks. In computer science, the dining philosophers problem is an example problem often used in concurrent algorithm design to illustrate synchronization issues and techniques for resolving them.
The task with the highest priority for which all dependent tasks have finished is scheduled on the worker which will result in the earliest finish time of that task. This finish time depends on the communication time to send all necessary inputs to the worker, the computation time of the task on the worker, and the time when that processor ...
Time lags can also be negative. A negative time lag means that the second job can begin a fixed time before the first job finishes. ℓ: The time lag is the same for each pair of jobs.: Different pairs of jobs can have different time lags.
Objective function can be to minimize the makespan, the L p norm, tardiness, maximum lateness etc. It can also be multi-objective optimization problem. Jobs may have constraints, for example a job i needs to finish before job j can be started (see workflow). Also, the objective function can be multi-criteria. [4]