Performance evaluation of the IBM POWER8 architecture to support computational neuroscientific application using morphologically detailed neurons

Ewart, Timothée; Yates, Stuart; Cremonesi, Francesco; Kumbhar, Pramod; Schürmann, Felix; Delalondre, Fabien

doi:10.1145/2832087.2832088

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…These types of models have become possible because of more quantitative data, new computational approaches to predicting and inferring missing parameters, and the increase in computer performance. In fact, a large number of improvements to the NEURON software over the last decade were motivated by these types of models: a major common theme in these developments was functionality to support parallel execution on multiple compute nodes (Migliore et al, 2006 ; Hines et al, 2008a , b , 2011a ; Lytton et al, 2016 ), complemented by platform-specific optimizations (Ewart et al, 2015 ; Kumbhar et al, 2016 ). In particular, the platform-specific optimizations underwent a disturbing trend: optimizations of NEURON for the first vector computers had been discontinued in favor of memory optimizations for out-of-order CPUs with intricate cache hierarchies, only to return to SIMD analogous structure-of-array memory layouts for today's CPUs/GPUs with wide vector units.…”

Section: Discussionmentioning

confidence: 99%

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Awile

Kumbhar

Cornu

et al. 2022

Front. Neuroinform.

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The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

show abstract

Section: Discussionmentioning

confidence: 99%

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Awile

Kumbhar

Cornu

et al. 2022

Front. Neuroinform.

Self Cite

View full text Add to dashboard Cite

show abstract

“…These types of models have become possible because of more quantitative data, new computational approaches to predicting and inferring missing parameters, and the increase in computer performance. In fact, a large number of improvements to the NEURON software over the last decade were motivated by these types of models: a major common theme in these developments was functionality to support parallel execution on multiple compute nodes (Migliore et al, 2006; Hines et al, 2008a,b, 2011b; Lytton et al, 2016), complemented by platform-specific optimizations (Ewart et al, 2015; Kumbhar et al, 2016). In particular, the platform-specific optimizations underwent a disturbing trend: optimizations of NEURON for the first vector computers had been discontinued in favor of memory optimizations for out-of-order CPUs with intricate cache hierarchies, only to return to SIMD analogous structure-of-array memory layouts for today’s CPUs/GPUs with wide vector units.…”

Section: Discussionmentioning

confidence: 99%

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Awile

Kumbhar

Cornu

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON’s ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON’s reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

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“…Despite excluding some parts of the simulation algorithm, our analysis still maintains a lot of relevance with regards to the overall simulation performance. Indeed, in both G-based and I-based models, it was found that computational and communication kernels often constitute more than 90% of the total simulation runtimes (Kumbhar et al 2019a;Ewart et al 2015;Schenck et al 2014;Peyser and Schenck 2015). From the modeling point of view, an important aspect that we have neglected in this analysis is synaptic plasticity.…”

Section: Static and Dynamic Model Parameters Affect Performance In Simentioning

confidence: 99%

Understanding Computational Costs of Cellular-Level Brain Tissue Simulations Through Analytical Performance Models

Cremonesi

Schürmann

2020

Neuroinform

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Computational modeling and simulation have become essential tools in the quest to better understand the brain's makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in the brain has led to the development of a large variety of model abstractions and specialized tools, often times requiring high performance computing resources for their timely execution. What has been missing so far was an in-depth analysis of the complexity of the computational kernels, hindering a systematic approach to identifying bottlenecks of algorithms and hardware. If whole brain models are to be achieved on emerging computer generations, models and simulation engines will have to be carefully co-designed for the intrinsic hardware tradeoffs. For the first time, we present a systematic exploration based on analytic performance modeling. We base our analysis on three in silico models, chosen as representative examples of the most widely employed modeling abstractions: current-based point neurons, conductance-based point neurons and conductance-based detailed neurons. We identify that the synaptic modeling formalism, i.e. current or conductance-based representation, and not the level of morphological detail, is the most significant factor in determining the properties of memory bandwidth saturation and shared-memory scaling of in silico models. Even though general purpose computing has, until now, largely been able to deliver high performance, we find that for all types of abstractions, network latency and memory bandwidth will become severe bottlenecks as the number of neurons to be simulated grows. By adapting and extending a performance modeling approach, we deliver a first characterization of the performance landscape of brain tissue simulations, allowing us to pinpoint current bottlenecks for state-of-the-art in silico models, and make projections for future hardware and software requirements. Keywords Computational models of neurons • Brain tissue simulations • Performance modeling • High performance computing This study was supported by funding to the Blue Brain Project, a research center of theÉcole polytechnique fédérale de Lausanne (EPFL), from the Swiss governments ETH Board of the Swiss Federal Institutes of Technology Electronic supplementary material The online version of this article (

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Performance evaluation of the IBM POWER8 architecture to support computational neuroscientific application using morphologically detailed neurons

Cited by 11 publications

References 9 publications

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Understanding Computational Costs of Cellular-Level Brain Tissue Simulations Through Analytical Performance Models

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