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Title: Performance and Energy Usage of Workloads on KNL and Haswell Architectures. In: High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation

Abstract

Manycore architectures are an energy-efficient step towards exascale computing within a constrained power budget. The Intel Knights Landing (KNL) manycore chip is a specific example of this and has seen early adoption by a number of HPC facilities. It is therefore important to understand the performance and energy usage characteristics of KNL. In this paper, we evaluate the performance and energy efficiency of KNL in contrast to the Xeon (Haswell) architecture for applications representative of the workload of users at NERSC. We consider the optimal MPI/OpenMP configuration of each application and use the results to characterize KNL in contrast to Haswell. As well as traditional DDR memory, KNL contains MCDRAM and we also evaluate its efficacy. Our results show that, averaged over our benchmarks, KNL is 1.84 × more energy efficient than Haswell and has 1.27 × greater performance.

Authors:
 [1];  [2];  [2];  [2];  [2]
  1. Clemson Univ., SC (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory-National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1546612
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Conference
Journal Name:
Lecture Notes in Computer Science
Additional Journal Information:
Journal Volume: 10724; Conference: International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, Denver, CO, November 13, 2017
Publisher:
Springer, Cham, Switzerland
Country of Publication:
United States
Language:
English
Subject:
Benchmarking; Power consumption; Energy; Hyperthreads; Manycore architecture; Intel Knights Landing; Haswell

Citation Formats

Allen, Tyler, Daley, Christopher S., Doerfler, Douglas, Austin, Brian, and Wright, Nicholas J. Performance and Energy Usage of Workloads on KNL and Haswell Architectures. In: High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation. United States: N. p., 2018. Web. doi:10.1007/978-3-319-72971-8_12.
Allen, Tyler, Daley, Christopher S., Doerfler, Douglas, Austin, Brian, & Wright, Nicholas J. Performance and Energy Usage of Workloads on KNL and Haswell Architectures. In: High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation. United States. doi:10.1007/978-3-319-72971-8_12.
Allen, Tyler, Daley, Christopher S., Doerfler, Douglas, Austin, Brian, and Wright, Nicholas J. Mon . "Performance and Energy Usage of Workloads on KNL and Haswell Architectures. In: High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation". United States. doi:10.1007/978-3-319-72971-8_12.
@article{osti_1546612,
title = {Performance and Energy Usage of Workloads on KNL and Haswell Architectures. In: High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation},
author = {Allen, Tyler and Daley, Christopher S. and Doerfler, Douglas and Austin, Brian and Wright, Nicholas J.},
abstractNote = {Manycore architectures are an energy-efficient step towards exascale computing within a constrained power budget. The Intel Knights Landing (KNL) manycore chip is a specific example of this and has seen early adoption by a number of HPC facilities. It is therefore important to understand the performance and energy usage characteristics of KNL. In this paper, we evaluate the performance and energy efficiency of KNL in contrast to the Xeon (Haswell) architecture for applications representative of the workload of users at NERSC. We consider the optimal MPI/OpenMP configuration of each application and use the results to characterize KNL in contrast to Haswell. As well as traditional DDR memory, KNL contains MCDRAM and we also evaluate its efficacy. Our results show that, averaged over our benchmarks, KNL is 1.84 × more energy efficient than Haswell and has 1.27 × greater performance.},
doi = {10.1007/978-3-319-72971-8_12},
journal = {Lecture Notes in Computer Science},
number = ,
volume = 10724,
place = {United States},
year = {2018},
month = {1}
}

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Works referenced in this record:

Castro: a new Compressible Astrophysical Solver. i. Hydrodynamics and Self-Gravity
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