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Summary: Visualizing Complex Dynamics in Many-Core
Accelerator Architectures
Aaron Ariel, Wilson W. L. Fung, Andrew E. Turner and Tor M. Aamodt
University of British Columbia,
Vancouver, BC, Canada
aaronariel@hotmail.com {wwlfung,aturner,aamodt}@ece.ubc.ca
Abstract--While many-core accelerator architectures, such
as today's Graphics Processing Units (GPUs), offer orders
of magnitude more raw computing power than contemporary
CPUs, their massive parallelism often produces complex dynamic
behaviors even with the simplest applications. Using a fixed
set of hardware or simulator performance counters to quantify
behavior over a large interval of time such as an entire application
execution run or program phase may not capture this behavior.
Software and/or hardware designers may consequently miss out
on opportunities to optimize for better performance. Similarly,
significant effort may be expended to find metrics that explain
anomalous behavior in architecture design studies. Moreover,
the increasing complexity of applications developed for today's
GPU has created additional difficulties for software developers
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