Software support to strengthen measurement-based timing analysis

In critical domains, the advent of high-performance (complex) hardware, used to provide the rising levels of guaranteed performance, complicates providing evidence of reliable software execution specially on aspects related to the timing dimension. Caches and multicores are two of the hardware features that have the potential to significantly reduce worst-case execution time (WCET) estimates, yet they pose new challenges on current-practice measurement-based timing analysis (MBTA) approaches. MBTA methods rely on some form of instrumentation, either at hardware or software level, of the target program or fragments thereof to collect execution-time measurement data. Instrumentation does affect the timing and functional behavior of a program, resulting in the so-called probe effect: leaving the instrumentation code in the final executable can negatively affect average performance and could not be even admissible under stringent industrial qualification and certification standards; removing it before operation jeopardizes the results of timing analysis as the WCET estimates on the instrumented version of the program cannot be valid any more due, for example, to the timing effects incurred by different cache alignments. Measurement-Based Probabilistic Timing Analysis (MBPTA) is a variant of MBTA that aims at increasing the confidence on WCET estimates. MBPTA aims at relieving the user from controlling hardware sources of jitter. MBPTA implicitly controls the impact of jittery resources on measurements captured at analysis. Some hardware resources are randomized so that their execution times at analysis vary according to a probabilistic execution time distribution that can be used to upperbound the latencies during operation and give a WCET prediction with a certain probability. In this Master Thesis we present our approach to mitigate the impact of instrumentation code on cache behavior by reducing the instrumentation overhead while at the same time preserving and consolidating the results of timing analysis. We further propose a technique for multilevel-cache multicores that combines deterministic and probabilistic jitter-bounding approaches to reliably handle variability in execution time generated by caches and the contention in accessing shared hardware resources