A quantitative analysis of OS noise
Alessandro Morari, Roberto Gioiosa, et al.
IPDPS 2011
This paper describes the implementation of an online feedback-directed optimization system. The system is fully automatic; it requires no prior (offline) profiling run. It uses a previously developed low-overhead instrumentation sampling framework to collect control flow graph edge profiles. This profile information is used to drive several traditional optimizations, as well as a novel algorithm for performing feedback-directed control flow graph node splitting. We empirically evaluate this system and demonstrate improvements in peak performance of up to 17% while keeping overhead low, with no individual execution being degraded by more than 2% because of instrumentation.
Alessandro Morari, Roberto Gioiosa, et al.
IPDPS 2011
G. Ramalingam
Theoretical Computer Science
Maciel Zortea, Miguel Paredes, et al.
IGARSS 2021
Martin Hirzel, Johannes Henkel, et al.
ACM SIGPLAN Notices