The method generates a static task execution schedule along with the structure of the multiprocessor system and a mapping of subtasks to processors.
While processor caches can effectively reduce execution time there are several factors, such as cache size, system data sharing, and task execution schedule, which make it hard to quantify, predict, and optimize the cache usage of a DRE system.
In multiprocessor SoC performance evaluation, simulating the profiled or estimated execution times (or number of operations) of tasks on abstract HW resource models is an effective way of observing combined effects of task execution times, mapping, scheduling, and HW platform parameters on resulting task response times, response time jitters, and processing element utilizations.
The proposed solution is only suitable for off-line approaches, where the probabilistic genetic algorithm tries to find a partitioning between mandatory and optional feasible tasks, thus producing a possible execution schedule for the task set.
In addition, a schedule of task execution can be represented graphically in what is called a Gantt chart.
Businesses and governments seeking to make improvements to their processes may model them for the purpose of seeking improvements in schedule and task execution, product quality, risk reduction, and lifecycle/operating costs.
This pre-assumes the capability of partitioning the arriving jobs into independent tasks that can be executed simultaneously, assigning the tasks to processors and scheduling the task execution on each processor.
Its most essential characteristics are task execution without using interrupts, as well as scheduling in direct reference to Universal Time Co-ordinated.
To limit the length of the canonical schedules and predict their task execution, tasks with arbitrary periods are first transformed into harmonic periods and their key features are profiled.
By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time.
In this work, three important and necessary design decisions, (1) workload partitioning, (2) node assignment, and (3) task execution order, are identified for real-time divisible load scheduling.
