Scalable black-box prediction models for multi-dimensional adaptation on NUMA multi-cores

Aleksandr Khasymski, Dimitrios S. Nikolopoulos

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This paper presents a scalable, statistical ‘black-box’ model for predicting the performance of parallel programs on multi-core non-uniform memory access (NUMA) systems. We derive a model with low overhead, by reducing data collection and model training time. The model can accurately predict the behaviour of parallel applications in response to changes in their concurrency, thread layout on NUMA nodes, and core voltage and frequency. We present a framework that applies the model to achieve significant energy and energy-delay-square (ED2) savings (9% and 25%, respectively) along with performance improvement (10% mean) on an actual 16-core NUMA system running realistic application workloads. Our prediction model proves substantially more accurate than previous efforts.
Original languageEnglish
Pages (from-to)193-210
JournalInternational Journal of Parallel, Emergent and Distributed Systems
Issue number3
Early online date03 Apr 2014
Publication statusPublished - Apr 2015

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software


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    Nikolopoulos, D.


    Project: Research

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