Abstract:
Static program analyses have to over-approximate the run-time behavior
of programs to follow the requirement of safety.  This sacrifices
significant optimization potential.  To overcome this limitation, we
propose to use unsafe, but more precise analyses together with
speculative optimizations, which ensure correctness in case of
misspeculation.

In this talk, we present our Framework for Intelligent Speculative
Compiler Optimizations (FrISCO) and its application to the
optimization of memory accesses. The framework uses Machine Learning
to yield precise predictors for dynamic program behavior on the one
hand and novel speculative optimizations which use these predictors to
derive precise cost models on the other.

For memory accesses, speculative optimization is of particular
importance due to the memory gap and the high imprecision of alias
analyses.  We implemented the optimization within our compiler
framework for the Intel Itanium.  Our experimental results show that
the heuristics generated by Machine Learning yield precise results,
especially due to our concept of program classification.  Overall, our
optimization together with the automatically generated heuristics
leads to significant performance improvement for the SPEC CPU2006
benchmarks.

Kurzbiographie von Dipl.-Inf. Lars ALVINCZ

Lars Alvincz (ne Gesellensetter) is a PhD student in the "Software
Engineering for Embedded Systems" group of Prof. Dr. Sabine Glesner at
the Technical University of Berlin.  He studied computer science at
the University of Ulm, at the DCU Dublin, and at the University of
Bielefeld, where he received his Diploma Degree in 2004.  In his
research, he investigates methods to improve compilers (including
program analyses and program optimization) by using techniques from
the field of machine learning. (<link service.zid.tuwien.ac.at/ts/goto.php _blank>http://pes.cs.tu-berlin.de/alvincz)</link>