Abstract: A computer-implemented method schedules a plurality of tasks for execution by a multi-processor system. A first schedule is generated that assigns each task of the plurality of tasks a time window and a processor of the multi-processor system. A contention model for the multi-processor system is queried to determine a contention delay for the assignment of tasks to processors according to the first schedule. The contention delay determined from the contention model is used to generate, from the first schedule, a revised schedule that assigns each task a time window and a processor of the multi-processor system, wherein the revised schedule is determined in dependence on the determined contention delay.

Abstract: A computer-implemented method schedules a plurality of tasks for execution by a processor system. A first execution model for the plurality of tasks is accessed. Data is generated that identifies which tasks in the execution model are not direct-feedthrough tasks. The data is used to determine an order for executing the tasks at least partly in dependence on whether or not each task is a direct-feedthrough task.

Abstract: A method of generating training data for training a Machine Learning based Task Contention Model, ML based TCM, to predict time delays resulting from contention between tasks running in parallel on a multi-processor system is provided herein. The method includes: executing a plurality of microbenchmarks, ?Benchmarks Bj, on the multi-processor system in isolation and measuring a number of resultant Performance Monitoring Counters, PMCs, over time to extract ideal characteristic footprints of each ?Benchmark when operating in isolation; performing a feature correlation analysis on the PMCs resulting from the plurality of ?Benchmarks to determine the degree of correlation between each resultant PMCs and the executed plurality of ?Benchmarks; selecting a number of PMCs based upon their degree of correlation between the plurality of ?Benchmarks to form a reduced PMC array.

Abstract: A computer-implemented method of producing a trained Machine Learning based Task Contention Model to predict time delays resulting from contention between tasks running in parallel on a multi-processor system is provided herein.