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Modeling the Impact of Reduced Memory Bandwidth on HPC Applications

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8632)

Abstract

To deliver the energy efficiency and raw compute throughput necessary to realize exascale systems, projected designs call for massive numbers of (simple) cores per processor. An unfortunate consequence of such designs is that the memory bandwidth per core will be significantly reduced, which can significantly degrade the performance of many memory-intensive HPC workloads. To identify the code regions that are most impacted and to guide them in developing mitigating solutions, system designers and application developers alike would benefit immensely from a systematic framework that allowed them to identify the types of computations that are sensitive to reduced memory bandwidth and to precisely identify those regions in their code that exhibit sensitivity. This paper introduces a framework for identifying the properties in computations that are associated with memory bandwidth sensitivity, extracting those same properties from HPC applications, and for associating bandwidth sensitivity to specific structures in the application source code. We apply our framework to a number of large scale HPC applications, observing that the bandwidth sensitivity model shows an absolute mean error that averages less than 5%.

Keywords

Memory Bandwidth Lawrence Livermore National Laboratory Memory Access Pattern Computational Phasis Binary Instrumentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Performance Modeling and Characterization LabSan Diego Supercomputer CenterUSA
  2. 2.Computational and Applied Statistics LabSan Diego Supercomputer CenterUSA
  3. 3.Department of Computer Science and EngineeringUniversity of MichiganUSA
  4. 4.Lawrence Livermore National Laboratory (LLNL)USA

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