SCHEDULE: NOV 16-22, 2013
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A Framework for Input and Architecture Aware Code Variant Autotuning
SESSION: Doctoral Showcase - Early Research Showcase
EVENT TYPE: Doctoral Showcase - Early Research Showcases
TIME: 3:30PM - 5:00PM
SESSION CHAIR: Wojtek James Goscinski
Presenter(s):Saurav Muralidharan
ROOM:601/603
ABSTRACT:
With computer systems becoming increasingly parallel and heterogeneous, autotuning is turning out to be essential in achieving performance portability for parallel applications.
My current research focuses on input-aware autotuning, since autotuners that take characteristics of the input data along with other execution context into account can optimize more aggressively. I describe a programmer-directed autotuning framework that facilitates autotuning of code variants, or alternative implementations of the same computation, and selects code variants at execution based on features of the input data set.
In real-world irregular benchmarks from sparse numerical methods and graph computations, variants selected using our system achieve up to 99.85% of the performance achieved by variants selected through exhaustive search.
I also discuss my future research focus: how autotuning systems can be made more accessible to non-expert users by integrating them with higher-level programming models that decouple computation and implementation.
Chair/Presenter Details:
Wojtek James Goscinski (Chair) - Monash University
Saurav Muralidharan - University of Utah
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