From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator

George Chatzikonstantis; Diego Jiménez; Esteban Meneses; Christos Strydis; Harry Sidiropoulos; Dimitrios Soudris

doi:10.1007/978-3-319-67630-2_27

From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator

George Chatzikonstantis^*, Diego Jiménez, Esteban Meneses, Christos Strydis, Harry Sidiropoulos, Dimitrios Soudris

^*Corresponding author for this work

Neurosciences

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

2 Citations (Scopus)

Abstract

Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel’s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previous generation of Xeon Phi models, the Knights Corner (KNC). Our neuron simulator of choice features a Hodgkin-Huxley-based (HH) model which has been ported on both generations of Xeon Phi platforms and aggressively draws on both platforms’ computational assets. The application uses the OpenMP interface for efficient parallelization and the Xeon Phi’s vectorization buffers for Single-Instruction Multiple Data (SIMD) processing. In this study we offer insight into the efficiency with which the application utilizes the assets of the two Xeon Phi generations and we evaluate the merits of utilizing the KNL over its predecessor. In our case, an out-of-the-box transition on Knights Landing, offers on average 2.4x speed up while consuming 48% less energy than KNC.

Original language	English
Title of host publication	High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers
Editors	Rio Yokota, Julian M. Kunkel, Michela Taufer, John Shalf
Publisher	Springer-Verlag
Pages	363-375
Number of pages	13
ISBN (Print)	9783319676296
DOIs	https://doi.org/10.1007/978-3-319-67630-2_27
Publication status	Published - 2017
Event	32nd International Conference on High Performance Computing, ISC High Performance 2017 - Frankfurt, Germany Duration: 18 Jun 2017 → 22 Jun 2017

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10524 LNCS
ISSN	0302-9743

Conference

Conference	32nd International Conference on High Performance Computing, ISC High Performance 2017
Country/Territory	Germany
City	Frankfurt
Period	18/06/17 → 22/06/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Access to Document

10.1007/978-3-319-67630-2_27

Cite this

Chatzikonstantis, G., Jiménez, D., Meneses, E., Strydis, C., Sidiropoulos, H., & Soudris, D. (2017). From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator. In R. Yokota, J. M. Kunkel, M. Taufer, & J. Shalf (Eds.), High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers (pp. 363-375). Springer-Verlag. https://doi.org/10.1007/978-3-319-67630-2_27

Chatzikonstantis, George ; Jiménez, Diego ; Meneses, Esteban et al. / From knights corner to landing : A case study based on a hodgkin-huxley neuron simulator. High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers. editor / Rio Yokota ; Julian M. Kunkel ; Michela Taufer ; John Shalf. Springer-Verlag, 2017. pp. 363-375 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10524 LNCS).

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title = "From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator",

abstract = "Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel{\textquoteright}s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previous generation of Xeon Phi models, the Knights Corner (KNC). Our neuron simulator of choice features a Hodgkin-Huxley-based (HH) model which has been ported on both generations of Xeon Phi platforms and aggressively draws on both platforms{\textquoteright} computational assets. The application uses the OpenMP interface for efficient parallelization and the Xeon Phi{\textquoteright}s vectorization buffers for Single-Instruction Multiple Data (SIMD) processing. In this study we offer insight into the efficiency with which the application utilizes the assets of the two Xeon Phi generations and we evaluate the merits of utilizing the KNL over its predecessor. In our case, an out-of-the-box transition on Knights Landing, offers on average 2.4x speed up while consuming 48\% less energy than KNC.",

author = "George Chatzikonstantis and Diego Jim{\'e}nez and Esteban Meneses and Christos Strydis and Harry Sidiropoulos and Dimitrios Soudris",

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booktitle = "High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P\textasciicircum{}3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers",

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Chatzikonstantis, G, Jiménez, D, Meneses, E, Strydis, C, Sidiropoulos, H & Soudris, D 2017, From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator. in R Yokota, JM Kunkel, M Taufer & J Shalf (eds), High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers. Springer-Verlag, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10524 LNCS, pp. 363-375, 32nd International Conference on High Performance Computing, ISC High Performance 2017, Frankfurt, Germany, 18/06/17. https://doi.org/10.1007/978-3-319-67630-2_27

From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator. / Chatzikonstantis, George; Jiménez, Diego; Meneses, Esteban et al.
High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers. ed. / Rio Yokota; Julian M. Kunkel; Michela Taufer; John Shalf. Springer-Verlag, 2017. p. 363-375 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10524 LNCS).

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

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AU - Chatzikonstantis, George

AU - Jiménez, Diego

AU - Meneses, Esteban

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AU - Sidiropoulos, Harry

AU - Soudris, Dimitrios

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N2 - Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel’s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previous generation of Xeon Phi models, the Knights Corner (KNC). Our neuron simulator of choice features a Hodgkin-Huxley-based (HH) model which has been ported on both generations of Xeon Phi platforms and aggressively draws on both platforms’ computational assets. The application uses the OpenMP interface for efficient parallelization and the Xeon Phi’s vectorization buffers for Single-Instruction Multiple Data (SIMD) processing. In this study we offer insight into the efficiency with which the application utilizes the assets of the two Xeon Phi generations and we evaluate the merits of utilizing the KNL over its predecessor. In our case, an out-of-the-box transition on Knights Landing, offers on average 2.4x speed up while consuming 48% less energy than KNC.

AB - Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel’s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previous generation of Xeon Phi models, the Knights Corner (KNC). Our neuron simulator of choice features a Hodgkin-Huxley-based (HH) model which has been ported on both generations of Xeon Phi platforms and aggressively draws on both platforms’ computational assets. The application uses the OpenMP interface for efficient parallelization and the Xeon Phi’s vectorization buffers for Single-Instruction Multiple Data (SIMD) processing. In this study we offer insight into the efficiency with which the application utilizes the assets of the two Xeon Phi generations and we evaluate the merits of utilizing the KNL over its predecessor. In our case, an out-of-the-box transition on Knights Landing, offers on average 2.4x speed up while consuming 48% less energy than KNC.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Chatzikonstantis G, Jiménez D, Meneses E, Strydis C, Sidiropoulos H, Soudris D. From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator. In Yokota R, Kunkel JM, Taufer M, Shalf J, editors, High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers. Springer-Verlag. 2017. p. 363-375. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10524 LNCS). doi: 10.1007/978-3-319-67630-2_27

From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator

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