View source for Statistical Methods for Exascale Performance Modeling

== Principal Investigator ==
* [http://people.llnl.gov/gamblin2 Todd Gamblin], Lawrence Livermore National Laboratory

== Summary ==

Large computer simulations are critical for a broad range of scientific disciplines. Despite this
need, adapting a scientific simulation code to run efficiently on a new supercomputer is tedious and
time consuming. For the most complex applications, the process can take six months or more. Predictive
mathematical models of performance and power consumption could accelerate this process, but the
behavior of modern adaptive codes can change, depending on the input data. This makes existing
modeling techniques difficult to apply. This project will develop statistical models of applications that
can represent adaptive, data‐dependent behavior in a scalable manner. The project will also develop
techniques to reduce the complexity of application models so that they are easily understood by
application developers. These models will provide simulation developers with insights that allow them
to quickly optimize the performance of their code, ensuring that applications can take full advantage of
the performance of future exascale machines.

== Progress ==
; '''7/2014'''
:    Project start.