[Colloquium] Reminder: Hammer/Dissertation Defense/Jul 20, 2012

[Margaret Jaffey]

This is a reminder about Matthew's defense, which will be held one
week from today on Friday, July 20th.

       Department of Computer Science/The University of Chicago

                     *** Dissertation Defense ***


Candidate:  Matthew Hammer

Date:  Friday, July 20, 2012

Time:  3:00 PM

Place:  Ryerson 276

Title: Self-Adjusting Machines

Abstract:
In computer systems, the interaction of computations and data often
causes incremental changes, not wholesale ones. Hence, there exists
the possibility of improving the efficiency of a system by recording
and reusing computations, rather than blindly remaking them anew. To
this end, self-adjusting computation is a technique for systematically
constructing computational structures that evolve efficiently and
incrementally. Past work has explored several facets of this
programming language-based approach, but no prior formulations have
given a low-level account of self-adjusting computation. By low-level,
we mean an account where machine resources are defined and managed
explicitly, e.g., as in the C programming language.

We offer self-adjusting machines, a concept based on an operational
interpretation of self-adjusting computation with explicit machine
resources. By making their resources explicit, self-adjusting machines
give an operational account of self-adjusting computation suitable for
interoperation with low-level languages; via practical compilation and
run-time techniques, these machines are programmable, sound and
efficient.

Abstractly, we formally define self-adjusting machines that run an
intermediate language; we prove that this abstract machine semantics
is sound. Concretely, we give techniques based on this semantics that
construct self-adjusting machines by compiling a C-like surface
language into C target code that runs within an extensible, C-based
run-time system. By creating new programming abstractions, library
programmers can extend this C-based system with new self-adjusting
behavior. We demonstrate that this extension framework is powerful by
using it to express a variety of both new and old self-adjusting
abstractions. We give an empirical evaluation showing that our
approach is efficient and well-suited for programming space and
time-efficient self-adjusting computations.

Matthew's advisors are Prof. John Reppy and Prof. Umut Acar

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

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

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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
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