Naila Farooqui
School of Computer Science
College of Computing
Georgia Institute of Technology

Date: October 2nd, 2014 (Thursday)
Time: 12:00 PM - 2:00 PM (ET)
Location: KACB 1315

Committee:
---------------
Dr. Karsten Schwan (Advisor, School of Computer Science, Georgia Tech)
Dr. Sudhakar Yalamanchili (School of Electrical and Computer Engineering, Georgia Tech)
Dr. Ada Gavrilovska (School of Computer Science, Georgia Tech)
Dr. Richard Vuduc (School of Computational Science and Engineering, Georgia Tech)
Dr. Vanish Talwar (Systems Research, HP Labs)

Abstract:
------------
Heterogeneous parallel architectures like those comprised of CPUs and GPUs
are a tantalizing compute fabric for performance-hungry developers. While
these platforms enable order-of-magnitude performance increases for many
data-parallel application domains, there remain several open challenges:
(i) the distinct execution models inherent in the heterogeneous devices
present on such platforms drives the need to dynamically match workload
characteristics to the underlying resources, (ii) the complex architecture
and programming models of such systems require substantial application
knowledge and effort-intensive program tuning to achieve high performance,
and (iii) as such platforms become prevalent, there is a need to extend
their utility from running known regular data-parallel applications to
the broader set of input-dependent, irregular applications common in
enterprise settings.
The key contribution of our research is to employ dynamic instrumentation
to drive profile-driven resource management and optimizations for such
heterogeneous hybrid CPU/GPU platforms, in order to enable high application
performance and system throughput. Towards this end, this research will:
(a) enable dynamic instrumentation for GPU-based parallel architectures,
specifically targeting the complex Single-Instruction Multiple-Data (SIMD)
execution model, to gain real-time introspection into application behavior;
(b) leverage such dynamic performance data to support novel online resource
management methods that improve application performance and system throughput,
particularly for irregular, input-dependent applications; and (c) automate some
of the programmer effort required to exercise specialized architectural
features of such platforms via instrumentation-driven dynamic code optimizations.