Analyzing the Energy and Power Consumption of Remote Memory Accesses in the OpenSHMEM Model

  • Siddhartha Jana
  • Oscar Hernandez
  • Stephen Poole
  • Chung-Hsing Hsu
  • Barbara M. Chapman
Conference paper

DOI: 10.1007/978-3-319-05215-1_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8356)
Cite this paper as:
Jana S., Hernandez O., Poole S., Hsu CH., Chapman B.M. (2014) Analyzing the Energy and Power Consumption of Remote Memory Accesses in the OpenSHMEM Model. In: Poole S., Hernandez O., Shamis P. (eds) OpenSHMEM and Related Technologies. Experiences, Implementations, and Tools. OpenSHMEM 2014. Lecture Notes in Computer Science, vol 8356. Springer, Cham

Abstract

PGAS models like OpenSHMEM provide interfaces to explicitly initiate one-sided remote memory accesses among processes. In addition, the model also provides synchronizing barriers to ensure a consistent view of the distributed memory at different phases of an application. The incorrect use of such interfaces affects the scalability achievable while using a parallel programming model. This study aims at understanding the effects of these constructs on the energy and power consumption behavior of OpenSHMEM applications. Our experiments show that cost incurred in terms of the total energy and power consumed depends on multiple factors across the software and hardware stack. We conclude that there is a significant impact on the power consumed by the CPU and DRAM due to multiple factors including the design of the data transfer patterns within an application, the design of the communication protocols within a middleware, the architectural constraints laid by the interconnect solutions, and also the levels of memory hierarchy within a compute node. This work motivates treating energy and power consumption as important factors while designing compute solutions for current and future distributed systems.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Siddhartha Jana
    • 1
  • Oscar Hernandez
    • 2
  • Stephen Poole
    • 2
  • Chung-Hsing Hsu
    • 2
  • Barbara M. Chapman
    • 1
  1. 1.HPCTools, Computer Science DepartmentUniversity of HoustonHoustonUSA
  2. 2.Computer Science and Mathematics DivisionOak Ridge National LaboratoryOak RidgeUSA

Personalised recommendations