[os-reading] oopsla practice talk next Tuesday (10.14)

[Linhai <songlh@xxxxxxxxxxx>]

Hi all,

    My apologies if this is a duplicate email. I will present a 
practice talk for our oopsla paper next Tuesday at 4:00 pm in CS3310. 
The paper title is "Statistical Debugging for Real-World Performance 
Problems", and this is joint work with my advisor Professor Shan Lu. The 
paper abstract is attached at the end of this email. Cookie will be 
served during the practice talk. It would be great, if you can come and 
leave some comments.
    Thanks a lot!

    Best,

Linhai


Abstract:

Design and implementation defects that lead to inefficient computation 
widely
exist in software. These defects are difficult to avoid and discover.
They lead to severe performance
degradation and energy waste during production runs, and are becoming
increasingly critical with the meager increase of
single-core hardware performance and the increasing concerns about energy
constraints. Effective tools that diagnose performance
problems and point out the inefficiency root cause
are sorely needed.

The state of the art of performance diagnosis is preliminary. Profiling
can identify the functions that consume the most computation resources, 
but can neither identify the ones that waste the most resources nor 
explain why.
Performance-bug detectors can identify
specific type of inefficient computation, but are not suited for
diagnosing general performance problems. Effective failure
diagnosis techniques, such as statistical debugging, have been proposed for
functional bugs. However, whether they work for
performance problems is still an open question.

In this paper, we first conduct an empirical study to understand how 
performance
problems are observed and reported by real-world users. Our study shows that
statistical debugging is a natural fit for diagnosing
performance problems, which
are often observed through comparison-based approaches and
reported together with both good and bad inputs. We then thoroughly 
investigate
different design points in statistical debugging, including three different
predicates and two different types of statistical models, to understand 
which
design point works the best for performance diagnosis. Finally, we study 
how
some unique nature of performance bugs allows sampling techniques to
lower the overhead of run-time performance diagnosis without extending the
diagnosis latency.