Analysis and optimization of gyrokinetic toroidal simulations on homogenous and heterogenous platforms

Khaled Z. Ibrahim; Kamesh Madduri; Samuel Williams; Bei Wang; Stephane Ethier; Leonid Oliker

doi:10.1177/1094342013492446

Analysis and optimization of gyrokinetic toroidal simulations on homogenous and heterogenous platforms

Khaled Z. Ibrahim
, Kamesh Madduri
, Samuel Williams
, Bei Wang
, Stephane Ethier
, Leonid Oliker

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

The Gyrokinetic Toroidal Code (GTC) uses the particle-in-cell method to efficiently simulate plasma microturbulence. This work presents novel analysis and optimization techniques to enhance the performance of GTC on large-scale machines. We introduce cell access analysis to better manage locality vs. synchronization tradeoffs on CPU and GPU-based architectures. Our optimized hybrid parallel implementation of GTC uses MPI, OpenMP, and NVIDIA CUDA, achieves up to a 2× speedup over the reference Fortran version on multiple parallel systems, and scales efficiently to tens of thousands of cores.

Original language	English (US)
Pages (from-to)	454-473
Number of pages	20
Journal	International Journal of High Performance Computing Applications
Volume	27
Issue number	4
DOIs	https://doi.org/10.1177/1094342013492446
State	Published - Nov 2013

All Science Journal Classification (ASJC) codes

Software
Theoretical Computer Science
Hardware and Architecture

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10.1177/1094342013492446

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@article{c23cfdb520f2451e899f191968943718,

title = "Analysis and optimization of gyrokinetic toroidal simulations on homogenous and heterogenous platforms",

abstract = "The Gyrokinetic Toroidal Code (GTC) uses the particle-in-cell method to efficiently simulate plasma microturbulence. This work presents novel analysis and optimization techniques to enhance the performance of GTC on large-scale machines. We introduce cell access analysis to better manage locality vs. synchronization tradeoffs on CPU and GPU-based architectures. Our optimized hybrid parallel implementation of GTC uses MPI, OpenMP, and NVIDIA CUDA, achieves up to a 2× speedup over the reference Fortran version on multiple parallel systems, and scales efficiently to tens of thousands of cores.",

author = "Ibrahim, \{Khaled Z.\} and Kamesh Madduri and Samuel Williams and Bei Wang and Stephane Ethier and Leonid Oliker",

note = "Funding Information: All authors from from Lawrence Berkeley National Laboratory were supported by the DOE Office of Advanced Scientific Computing Research (grant number DE-AC02-05CH11231). Dr Ethier is supported by the DOE (grant number DE-AC02-09CH11466). This research used resources of the Argonne Leadership Computing Facility at the Argonne National Laboratory, which is supported by the Office of Science of the US Department of Energy (grant number DE-AC02-06CH11357). This research also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy (grant number DE-AC02-05CH11231). Finally, this research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy (grant number DE-AC05-00OR22725).",

year = "2013",

month = nov,

doi = "10.1177/1094342013492446",

language = "English (US)",

volume = "27",

pages = "454--473",

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TY - JOUR

T1 - Analysis and optimization of gyrokinetic toroidal simulations on homogenous and heterogenous platforms

AU - Ibrahim, Khaled Z.

AU - Madduri, Kamesh

AU - Williams, Samuel

AU - Wang, Bei

AU - Ethier, Stephane

AU - Oliker, Leonid

N1 - Funding Information: All authors from from Lawrence Berkeley National Laboratory were supported by the DOE Office of Advanced Scientific Computing Research (grant number DE-AC02-05CH11231). Dr Ethier is supported by the DOE (grant number DE-AC02-09CH11466). This research used resources of the Argonne Leadership Computing Facility at the Argonne National Laboratory, which is supported by the Office of Science of the US Department of Energy (grant number DE-AC02-06CH11357). This research also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy (grant number DE-AC02-05CH11231). Finally, this research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy (grant number DE-AC05-00OR22725).

PY - 2013/11

Y1 - 2013/11

N2 - The Gyrokinetic Toroidal Code (GTC) uses the particle-in-cell method to efficiently simulate plasma microturbulence. This work presents novel analysis and optimization techniques to enhance the performance of GTC on large-scale machines. We introduce cell access analysis to better manage locality vs. synchronization tradeoffs on CPU and GPU-based architectures. Our optimized hybrid parallel implementation of GTC uses MPI, OpenMP, and NVIDIA CUDA, achieves up to a 2× speedup over the reference Fortran version on multiple parallel systems, and scales efficiently to tens of thousands of cores.

AB - The Gyrokinetic Toroidal Code (GTC) uses the particle-in-cell method to efficiently simulate plasma microturbulence. This work presents novel analysis and optimization techniques to enhance the performance of GTC on large-scale machines. We introduce cell access analysis to better manage locality vs. synchronization tradeoffs on CPU and GPU-based architectures. Our optimized hybrid parallel implementation of GTC uses MPI, OpenMP, and NVIDIA CUDA, achieves up to a 2× speedup over the reference Fortran version on multiple parallel systems, and scales efficiently to tens of thousands of cores.

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JO - International Journal of High Performance Computing Applications

JF - International Journal of High Performance Computing Applications

IS - 4

ER -

Analysis and optimization of gyrokinetic toroidal simulations on homogenous and heterogenous platforms

Abstract

All Science Journal Classification (ASJC) codes

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