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Analyzing CUDA Workloads Using a Detailed GPU Simulator Ali Bakhoda, George L. Yuan, Wilson W. L. Fung, Henry Wong and Tor M. Aamodt
 

Summary: Analyzing CUDA Workloads Using a Detailed GPU Simulator
Ali Bakhoda, George L. Yuan, Wilson W. L. Fung, Henry Wong and Tor M. Aamodt
University of British Columbia,
Vancouver, BC, Canada
{bakhoda,gyuan,wwlfung,henryw,aamodt}@ece.ubc.ca
Abstract
Modern Graphic Processing Units (GPUs) provide suffi-
ciently flexible programming models that understanding their
performance can provide insight in designing tomorrow's
manycore processors, whether those are GPUs or other-
wise. The combination of multiple, multithreaded, SIMD cores
makes studying these GPUs useful in understanding trade-
offs among memory, data, and thread level parallelism. While
modern GPUs offer orders of magnitude more raw comput-
ing power than contemporary CPUs, many important ap-
plications, even those with abundant data level parallelism,
do not achieve peak performance. This paper characterizes
several non-graphics applications written in NVIDIA's CUDA
programming model by running them on a novel detailed
microarchitecture performance simulator that runs NVIDIA's

  

Source: Aamodt, Tor - Department of Electrical and Computer Engineering, University of British Columbia

 

Collections: Engineering; Computer Technologies and Information Sciences