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.