Jonathan Appavoo wins NSF Early Career Award

Jonathan Appavoo has won an NSF Faculty Early Career Development Award to
support his research and teaching efforts in operating systems, in
particular his project “Programmable Smart Machines.”

Congratulations to Jonathan on this prestigious grant award, which is very
well deserved indeed!

“Programmable Smart Machines” Abstract:
Faster computers have enabled advances in science, commerce and daily
life. Unfortunately, computers have also become complex and more and
more difficult to program efficiently. This trend threatens the
sustainability of future advances. Perhaps, however, we can draw upon
biologically inspired learning techniques to shed light into a new
model of hybrid computer, a “Programmable Smart Machine”, that
inherently learns from its past behavior to automatically improve its
performance without the burden of more complex programming.
Specifically this work explores the addition of a smart memory to a
computer that gives it the abilities to learn, store and exploit
patterns in past execution to improve its performance.

Central to this work is the introduction of a new kind of global
long-term machine learning based ‘cache’ that can be viewed as an
auto-associative memory. The ‘cache’ is fed raw low-level traces of
execution, from which it extracts and stores commonly occurring
patterns that can be recognized and predicted. The core execution
process is modified to send the trace to the ‘cache’ and to exploit
its feedback to enact acceleration. The long-term goal is a system
whose performance improves with the size and contents of the ‘cache’,
which can be constructed with local associative memory devices and a
shared online repository that is contributed to and leveraged by many
systems. In this way a kind of shared computational history is
naturally created and exploited.

This work experimentally explores questions with respect to
concretizing the “Programmable Smart Machine” model. What are
useful and tractable traces for detecting patterns in execution? Can
current unsupervised deep learning techniques detect, store and recall
useful patterns? How can the predictions from the machine learning
based memory be utilized to automatically improve performance? How
big does the machine learning based memory need to be to yield useful
predictions and acceleration? This work explores these questions
using simulation and controlled workload experiments to create
complete traces including all instructions, register values, and I/O
events. Using the traces, at least two deep learning approaches will
be evaluated with respect to the number and size of patterns they
recognize. The resulting trained models will be integrated into the
published auto-parallelization methodology that established
preliminary results for this work. The simulation infrastructure,
trace data and experimental results will be made publicly available to
enable broader study.

This work produces unique trace data of computer operation. The PI
has found that visual and audio presentations of the preliminary data
reflect the kind of intuition that computer scientists develop about
how computers work. This aspect will be leveraged to develop both a
seminar, “From Bits to Chess to Supercomputers” and an associated
“Computing Intuition” website that engages K-12 students with

Featured in the Winter-Spring 2013 Edition of Bostonia, and BU Today.

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