[Colloquium] Reminder: Imes/Dissertation Defense/Apr 25, 2018

[Margaret Jaffey via Colloquium]

This is a reminder about Connor's defense tomorrow.

       Department of Computer Science/The University of Chicago

                     *** Dissertation Defense ***


Candidate:  Connor Imes

Date:  Wednesday, April 25, 2018

Time:  10:00 AM

Place:  Ryerson 255

Title: Balancing Performance and Energy in Computing Systems

Abstract:
This dissertation addresses challenges in balancing performance and
power/energy consump- tion in computing systems. Systems are often
underutilized, meaning applications do not require all of a system’s
resources in order to achieve desired behavior, e.g., an application
performance goal or reasonable system energy consumption. Modern
systems expose knobs for tuning resources, like processor frequency or
core allocation, which have a quantifiable impact on application
performance and system power consumption. The result is a tradeoff
space that can be navigated by resource schedulers to achieve desired
behavior, sacrificing one dimension in favor of another, e.g.,
increased performance at the cost of increased power or energy
consumption. The optimal knob settings required to achieve desired
behavior de- pend on both the application and system, even changing
during the course of execution as applications progress through
different processing phases. The challenge in designing gen- eral and
portable solutions to these problems arises from the diversity in both
hardware and software systems. We first address the problem of meeting
application performance goals while minimiz- ing energy consumption
with two projects—POET and CoPPer. Both projects use control theory,
which provides a formal framework for reasoning about dynamic systems,
including convergence guarantees and robustness to model inaccuracies.
In contrast, commonly used heuristic techniques cannot provide these
guarantees, nor are they always portable. POET is a general solution
that is portable between applications and systems—it is independent of
different knob types and their allowable settings. POET produces
resource schedules to exactly meet performance goals while achieving
optimal energy consumption. CoPPer lever- ages recent power capping
technology in place of software-managed dynamic voltage and frequency
scaling (DVFS), which is being deprecated by hardware vendors. CoPPer
over- comes challenges presented by the non-linear relationship
between performance and power to meet performance goals while leaving
the energy optimization to hardware, which responds more rapidly to
changes in application resource requirements than software. Finally,
we address the problem of optimizing energy efficiency to minimize the
execu- tion cost of running applications. We propose to use machine
learning classifiers, driven by low-level hardware performance
counters, to predict the most energy-efficient knob set- tings at
runtime based on current application resource utilization. By using
performance counters, no application modifications are necessary. We
evaluate this approach in the High Performance Computing (HPC) domain,
more aggressively trading performance for energy savings than has
historically been done, reducing the cost of scientific insight.
Extrapolating from empirical single-node performance and power
results, scaling the solution to hardware over-provisioned,
power-constrained clusters could increase total cluster throughput by
up to 24%. The three projects presented in this dissertation
dynamically adapt to changing applica- tion and system behavior at
runtime, and are thus able to provide more optimal results than
commonly-used static resource scheduling techniques. Furthermore, the
project designs are independent of particular applications and
systems, making them portable to a wide range of computing platforms.

Connor's advisor is Prof. Henry Hoffmann

Login to the Computer Science Department website for details,
including a draft copy of the dissertation:

 https://www.cs.uchicago.edu/phd/phd_announcements#ckimes

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Margaret P. Jaffey            margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (Ry 156)               (773) 702-6011
The University of Chicago      http://www.cs.uchicago.edu
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=