Optimizing Programs with Intended Semantics

Modern object-oriented languages have complex features that cause
programmers to overspecify their programs. This overspecification
hinders automatic optimizers, since they must preserve the
overspecified semantics. If an optimizer knew which semantics the
programmer intended, it could do a better job.

Making a programmer clarify his intentions by placing assumptions
into the program is rarely practical. This is because the programmer
does not know which parts of the programs' overspecified semantics
hinder the optimizer. Therefore, the programmer has to guess
which assumption to add. Since the programmer can add many
different assumptions to a large program, he will need to place
many such assumptions before he guesses right and helps the optimizer.


We present IOpt, a practical optimizer that uses a
specification of the programmers' intended semantics to enable
additional optimizations. That way, our optimizer can significantly
improve the performance of a program. We present case studies in which
we use IOpt to speed up two programs by over 50%.


To make specifying the intended semantics practical, IOpt
communicates with the programmer. IOpt identifies which
assumptions the programmer textit{should} place, and where he should
place them. IOpt ranks each assumption by (i) the likelyhood that
the assumption conforms to the programmers' intended semantics and
(ii) how much the assumption will help IOpt improve the programs'
performance. IOpt proposes ranked assumptions to the programmer,
who just picks those that conform to his intended semantics. With
this approach, IOpt keeps the programmers' specification burden
low. Our case studies show that the programmer just needs to add a few
assumptions to realize the 50% speedup.

• Software Systems