Interference channel contention modelling using machine learning

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. The method includes: executing a plurality of microbenchmarks, μBenchmarks Bj, on the multi-processor system in isolation and measuring at least one resultant Performance Monitoring Counter, PMC, over time to extract ideal characteristic footprints of each μBenchmark when operating in isolation; executing possible pairing scenarios of the plurality of μBenchmarks in parallel on the multi-processor system and measuring the effect on the execution time of each μBenchmark, ΔTBj, resulting from contention over interference channels within the multi-processor system; and training a machine learning model using, as an input, the at least one PMC measure in isolation of each μBenchmark and, at the output, the corresponding ΔTBj during the parallel execution of each pairing scenario as training inputs.

1 . A computer-implemented method of producing a trained 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, the method comprising:
 executing a plurality of microbenchmarks, μBenchmarks B j , on the multi-processor system in isolation and measuring at least one resultant Performance Monitoring Counter, PMC, over time to extract ideal characteristic footprints of each μBenchmark when operating in isolation;   executing possible pairing scenarios of the plurality of μBenchmarks in parallel on the multi-processor system and measuring the effect on the execution time of each μBenchmark, ΔT B     j   , resulting from contention over interference channels within the multi-processor system;   training a machine learning model using, as an input, the at least one PMC measure in isolation of each μBenchmark and, at the output, the corresponding ΔT B     j    during the parallel execution of each pairing scenario as training inputs.   
     
     
         2 . The computer-implemented method of  claim 1 , comprising validating the training error of the ML based TCM by
 executing a plurality of actual execution tasks on the multi-processor system in isolation and measuring at least one resultant PMC over time for each actual execution task;   inferring, by the ML based TCM, the predicted effect on the execution time of each actual execution task given the at least one resultant PMC of each task as input;   executing the actual execution tasks in parallel and measuring the actual execution time;   comparing the predicted effect on the execution time with the actual execution time, thereby calculating an error between the predicted and actual execution time.   
     
     
         3 . The computer-implemented method of  claim 1 , wherein the training of the machine learning model comprises:
 a first iterative loop over all different pairing scenarios; and   a second interative loop over each of the PMC measures of each μBenchmark in isolation, as well as the corresponding ΔT B     j    during the parallel execution of each pairing scenario.   
     
     
         4 . The computer-implemented method of  claim 1 , wherein the at least one PMC is selected based an identification of the PMCs that are associated with interference channels on the multi-processor system. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the measuring of at least one PMC comprises measuring the at least one PMC at a variable monitoring frequency. 
     
     
         6 . The computer-implemented method of  claim 1 , each μBenchmark is a synthetic benchmark that is selected so as to stress certain interference channels of the multi-processor system in an isolated way. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the multi-processor system is a multi-core processor of an avionics system. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein the multi-processor system is a homogenous platform. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein the multi-processor system is a heterogenous platform or not symmetric. 
     
     
         10 . A Machine Learning based Task Contention Model produced by the method of  claim 1 . 
     
     
         11 . A computer system for producing a trained 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,
 wherein the computer system is configured perform the method of  claim 1 .   
     
     
         12 . A computer software comprising instructions which, when executed on a computer system, cause the computer system to produce a trained 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, by performing the method of  claim 1 . 
     
     
         13 . A computer-implemented method of predicting time delays resulting from contention between tasks running in parallel on a multi-processor system using a trained Machine Learning based Task Contention Model (ML based TCM), the method comprising:
 executing a plurality of actual execution tasks on the multi-processor system in isolation and measuring at least one resultant Performance Monitoring Counter (PMC) over time to extract ideal characteristic footprints of each actual execution task when operating in isolation;   inferring, from a ML based TCM, a predicted effect on the execution time of each actual execution task given the at least one resultant PMC of each task as input.   
     
     
         14 . The computer-implemented method of  claim 13 , wherein the predicted effect execution time of each actual execution task is aggregated for contending tasks so as to predict a worst case execution time, WCET, and/or wherein the trained ML based TCM is a trained ML based TCM produced by the method of any of  claims 1  to  9 . 
     
     
         15 . A computer-implemented method of scheduling a plurality of tasks for execution by a multi-processor system, wherein the scheduling uses time delays predicted by the method of  claim 13 .