"Prediction-based Power-Performance Adaptation of Multithreaded Scientific Codes"

by Matthew Curtis-Maury, Filip Blagojevic, Christos D. Antonopoulos, and Dimitrios S. Nikolopoulos

In IEEE Transactions on Parallel and Distributed Systems (TPDS).

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Abstract
Computing has recently reached an inflection point with the
introduction of multi-core processors. On-chip thread-level
parallelism is doubling approximately every other year. Concurrency
lends itself naturally to allowing a program to trade performance for
power savings by regulating the number of active cores, however in
several domains users are unwilling to sacrifice performance to save
power. We present a prediction model for identifying energy-efficient
operating points of concurrency in well-tuned multithreaded scientific
applications, and a runtime system which uses live program analysis to
optimize applications dynamically. We describe a dynamic, phase-aware
performance prediction model that combines multivariate regression
techniques with runtime analysis of data collected from hardware event
counters to locate optimal operating points of concurrency. Using our
model, we develop a prediction-driven, phase-aware runtime
optimization scheme that throttles concurrency so that power
consumption can be reduced and performance can be set at the knee of
the scalability curve of each program phase. The use of prediction
reduces the overhead of searching the optimization space while
achieving near-optimal performance and power savings. A thorough
evaluation of our approach shows a reduction in power consumption of
10.8\% simultaneous with an improvement in performance of 17.9\%,
resulting in energy savings of 26.7\%.

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