Characterizing the Performance-Energy Tradeoff of Small ARM Cores in HPC Computation

  • Michael A. Laurenzano
  • Ananta Tiwari
  • Adam Jundt
  • Joshua Peraza
  • William A. WardJr.
  • Roy Campbell
  • Laura Carrington
Conference paper

DOI: 10.1007/978-3-319-09873-9_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8632)
Cite this paper as:
Laurenzano M.A. et al. (2014) Characterizing the Performance-Energy Tradeoff of Small ARM Cores in HPC Computation. In: Silva F., Dutra I., Santos Costa V. (eds) Euro-Par 2014 Parallel Processing. Euro-Par 2014. Lecture Notes in Computer Science, vol 8632. Springer, Cham

Abstract

Deploying large numbers of small, low-power cores has been gaining traction recently as a system design strategy in high performance computing (HPC). The ARM platform that dominates the embedded and mobile computing segments is now being considered as an alternative to high-end x86 processors that largely dominate HPC because peak performance per watt may be substantially improved using off-the-shelf commodity processors.

In this work we methodically characterize the performance and energy of HPC computations drawn from a number of problem domains on current ARM and x86 processors. Unsurprisingly, we find that the performance, energy and energy-delay product of applications running on these platforms varies significantly across problem types and inputs. Using static program analysis we further show that this variation can be explained largely in terms of the capabilities of two processor subsystems: single instruction multiple data (SIMD)/floating point and the cache/memory hierarchy; and that static analysis of this kind is sufficient to predict which platform is best for a particular application/input pair. In the context of these findings, we evaluate how some of the key architectural changes being made for upcoming 64-bit ARM platforms may impact HPC application performance.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michael A. Laurenzano
    • 1
    • 2
  • Ananta Tiwari
    • 1
    • 3
  • Adam Jundt
    • 1
  • Joshua Peraza
    • 1
  • William A. WardJr.
    • 4
  • Roy Campbell
    • 4
  • Laura Carrington
    • 1
    • 3
  1. 1.EP AnalyticsUSA
  2. 2.Dept. of Computer Science and EngineeringUniversity of MichiganUSA
  3. 3.Performance Modeling and Characterization Lab.San Diego Supercomputer CenterUSA
  4. 4.U.S. Dept. of DefenseHigh Performance Computing Modernization ProgramUSA

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