skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Vectorised Computation of Diverging Ensembles. In: ICPP 2018 Proceedings of the 47th International Conference on Parallel Processing, Article No. 51

Abstract

Ensemble computations are used to evaluate a function for multiple inputs, for example in uncertainty quantification. Embedded ensemble computations perform several evaluations within the same program, often enabling a reduced overall runtime by exploiting vectorisation and parallelisation opportunities that are not present in individual ensemble members. This is challenging if members take different control flow paths. We present a source-to-source transformation that turns a given C program into an embedded ensemble program that computes members in a single-instruction-multiple-data fashion using OpenMP SIMD pragmas. We use techniques from whole-function vectorisation, achieving effective vectorisation for moderate amounts of branch divergence, particularly on processors with masked instructions such as recent Xeon Phi or Skylake processors with AVX-512.

Authors:
 [1];  [2];  [2];  [3]
  1. Imperial College London, London
  2. Argonne National Laboratory, Argonne, Illinois
  3. Intel Corporation, Argonne, Illinois
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1567654
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 47th International Conference on Parallel Processing, Eugene, OR, USA, August 13 - 16, 2018
Country of Publication:
United States
Language:
English

Citation Formats

Hückelheim, Jan, Hovland, Paul, Narayanan, Sri Hari Krishna, and Velesko, Paulius. Vectorised Computation of Diverging Ensembles. In: ICPP 2018 Proceedings of the 47th International Conference on Parallel Processing, Article No. 51. United States: N. p., 2018. Web. doi:10.1145/3225058.3225138.
Hückelheim, Jan, Hovland, Paul, Narayanan, Sri Hari Krishna, & Velesko, Paulius. Vectorised Computation of Diverging Ensembles. In: ICPP 2018 Proceedings of the 47th International Conference on Parallel Processing, Article No. 51. United States. doi:10.1145/3225058.3225138.
Hückelheim, Jan, Hovland, Paul, Narayanan, Sri Hari Krishna, and Velesko, Paulius. Mon . "Vectorised Computation of Diverging Ensembles. In: ICPP 2018 Proceedings of the 47th International Conference on Parallel Processing, Article No. 51". United States. doi:10.1145/3225058.3225138.
@article{osti_1567654,
title = {Vectorised Computation of Diverging Ensembles. In: ICPP 2018 Proceedings of the 47th International Conference on Parallel Processing, Article No. 51},
author = {Hückelheim, Jan and Hovland, Paul and Narayanan, Sri Hari Krishna and Velesko, Paulius},
abstractNote = {Ensemble computations are used to evaluate a function for multiple inputs, for example in uncertainty quantification. Embedded ensemble computations perform several evaluations within the same program, often enabling a reduced overall runtime by exploiting vectorisation and parallelisation opportunities that are not present in individual ensemble members. This is challenging if members take different control flow paths. We present a source-to-source transformation that turns a given C program into an embedded ensemble program that computes members in a single-instruction-multiple-data fashion using OpenMP SIMD pragmas. We use techniques from whole-function vectorisation, achieving effective vectorisation for moderate amounts of branch divergence, particularly on processors with masked instructions such as recent Xeon Phi or Skylake processors with AVX-512.},
doi = {10.1145/3225058.3225138},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:

Works referenced in this record:

The Market Model of Interest Rate Dynamics
journal, April 1997


Parallel Implementation and Practical Use of Sparse Approximate Inverse Preconditioners with a Priori Sparsity Patterns
journal, February 2001

  • Chow, Edmond
  • The International Journal of High Performance Computing Applications, Vol. 15, Issue 1
  • DOI: 10.1177/109434200101500106

Kokkos: Enabling manycore performance portability through polymorphic memory access patterns
journal, December 2014

  • Carter Edwards, H.; Trott, Christian R.; Sunderland, Daniel
  • Journal of Parallel and Distributed Computing, Vol. 74, Issue 12
  • DOI: 10.1016/j.jpdc.2014.07.003

Fast greeks by simulation in forward LIBOR models
journal, January 1999

  • Glasserman, Paul; Zhao, Xiaoliang
  • The Journal of Computational Finance, Vol. 3, Issue 1
  • DOI: 10.21314/JCF.1999.037

Ensemble forecasting
journal, March 2008


Quantification of modelling uncertainties in a large ensemble of climate change simulations
journal, August 2004

  • Murphy, James M.; Sexton, David M. H.; Barnett, David N.
  • Nature, Vol. 430, Issue 7001
  • DOI: 10.1038/nature02771

Embedded Ensemble Propagation for Improving Performance, Portability, and Scalability of Uncertainty Quantification on Emerging Computational Architectures
journal, January 2017

  • Phipps, E.; D'Elia, M.; Edwards, H. C.
  • SIAM Journal on Scientific Computing, Vol. 39, Issue 2
  • DOI: 10.1137/15M1044679