Experimental Evaluation of Statistical Model Checking Methods for Probabilistic Timing Analysis of Multiprocessor Systems
Résumé
Timing prediction of complex parallel data flow
applications on multiprocessor systems represents a difficult task
due to complex interferences observed by running software on
platforms shared resources. In this domain, classical analytical
or simulation-based approaches demonstrate scalability issues to
deliver fast yet accurate predictions. In this work, we present
an experimental evaluation of new simulation-based statistical
methods for timing analysis of multiprocessor systems. We adopt
a measurement-based approach for the creation of probabilistic
system-level models of the studied systems. Efficiency of statistical
methods is evaluated for platforms with different levels of
complexity from the point of view of shared resources. We
compare our approach against measurement and traditional
simulation methods on two case-studies from the computer vision
domain: a Sobel filter and a JPEG decoder. We show that the
accuracy and execution time of our simulation approach has good
potential for fast yet accurate design space exploration.