Machine learning based contention delay prediction in multicore architectures

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, μBenchmarks B, 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 μBenchmark when operating in isolation; performing a feature correlation analysis on the PMCs resulting from the plurality of μBenchmarks to determine the degree of correlation between each resultant PMCs and the executed plurality of μBenchmarks; selecting a number of PMCs based upon their degree of correlation between the plurality of μBenchmarks to form a reduced PMC array.