Abstract: Task/resources are randomly assigned a number of times and a score for each solution of the random assignment is calculated. Using machine learning and artificial intelligence, a subset of the solutions is selected. Assignment of task/resource within the subset may be randomly changed, e.g., a task/resource assignment between two entities, a task/resource within the selected subset may be replaced with another task/resource (without swapping), etc. The additional solutions form a super solution with the selected subset and the score associated with the additional solutions are calculated. The process of selection of assignments, random changes to the assignment and calculating the scores associated with the new solutions is repeated a number of times until a certain condition is met, e.g., a number of iterations, time out, improvement between two iterations is less than a certain threshold, etc. Once the certain condition is satisfied, a solution is selected.

Abstract: A method of executing coded instructions in a dynamically configurable multiprocessor having shared execution resources including steps of placing a first processor in an active state upon booting of the multiprocessor. In response to a processor create command, a second processor is placed in an active state. When either the first or second processor encounter a cache miss that has to be serviced by off-chip cache the processor requiring service is placed in nap state in which instruction fetching for that processor is disabled. When either the first or second processor encounter a cache miss that has to be serviced by main memory, the processor requiring services I placed in a sleep state by flushing all instructions from the processor in the sleep state and disabling instruction fetching for the processor in the sleep state.