Estimation of numerical reproducibility on CPU and GPU

Jézéquel, Fabienne; Lamotte, Jean–Luc; Said, Issam

doi:10.15439/2015f29

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…The differences in firing activity seen with NEURON vs CoreNEURON are expected due to vectorisation of the compute kernels in CoreNEURON and potential differences due to different solvers when using NMODL with sympy. Further differences are to be expected once this is extended to GPUs (Kumbhar et al 2019; Jézéquel, Lamotte, and Saïd 2015).…”

Section: Discussionmentioning

confidence: 99%

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Borges

Moreira

Takarabe

et al. 2022

Preprint

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The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015 the Blue Brain Project developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly-detailed and complex model S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model closely reproduced the original cell morphologies, electrophysiological responses, spatial distribution for all 207 cell types; and the connectivity properties of the 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological rates and activity patterns across all populations, and, when STP was included, produced a 1 Hz oscillation comparable to that of the original model. We also extended the model by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical and corticothalamic connectivity derived from experimental data. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory thalamocortical circuits that can be utilized as a community tool for researchers to study neural dynamics, function and disease.

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Section: Discussionmentioning

confidence: 99%

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Borges

Moreira

Takarabe

et al. 2022

Preprint

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“…Because all operators are redefined for stochastic variables, the use of CADNA in a program requires only a few modifications: essentially changes in the declarations of variables and in input/output statements. CADNA has been successfully used for the numerical validation of academic and industrial simulation codes in various domains such as astrophysics, atomic physics, chemistry, climate science, fluid dynamics, geophysics [9].…”

Section: Numerical Validation Of Sequential Codes Using Cadnamentioning

confidence: 99%

Estimation of Round-off Errors in OpenMP Codes

Eberhart

Brajard

Fortin

et al. 2016

OpenMP: Memory, Devices, and Tasks

Self Cite

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Abstract. It is crucial to control round-off error propagation in numerical simulations, because they can significantly affect computed results, especially in parallel codes like OpenMP ones. In this paper, we present a new version of the CADNA library that enables the numerical validation of OpenMP codes. With a reasonable cost in terms of execution time, it enables one to estimate which digits in computed results are affected by round-off errors and to detect numerical instabilities that may occur during the execution. The interest of this new OpenMP-enabled CADNA version is shown on various applications, along with performance results on multi-core and many-core (Intel Xeon Phi) architectures.

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“…), a computational variability remains due to the numerical nondeterminism issued from the rounding of numbers associated with the stochastic order of arithmetic operations (Whitehead and Fit-Florea, 2011; Chou et al, 2020). It is notably observed when the same code is evaluated in different hardware (Jézéquel et al, 2015) or with different floating-point precisions (Seznec et al, 2018). Currently, performance requirements make the use of graphics processing units (GPUs) mandatory in DL, their major drawback being the difficulty to perform deterministic operations.…”

Section: Introductionmentioning

confidence: 99%

On the reproducibility of fully convolutional neural networks for modeling time–space-evolving physical systems

Pinto

Alguacil

Bauerheim

2022

DCE

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Reproducibility of a deep-learning fully convolutional neural network is evaluated by training several times the same network on identical conditions (database, hyperparameters, and hardware) with nondeterministic graphics processing unit operations. The network is trained to model three typical time–space-evolving physical systems in two dimensions: heat, Burgers’, and wave equations. The behavior of the networks is evaluated on both recursive and nonrecursive tasks. Significant changes in models’ properties (weights and feature fields) are observed. When tested on various benchmarks, these models systematically return estimations with a high level of deviation, especially for the recurrent analysis which strongly amplifies variability due to the nondeterminism. Trainings performed with double floating-point precision provide slightly better estimations and a significant reduction of the variability of both the network parameters and its testing error range.

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Estimation of numerical reproducibility on CPU and GPU

Cited by 13 publications

References 19 publications

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Estimation of Round-off Errors in OpenMP Codes

On the reproducibility of fully convolutional neural networks for modeling time–space-evolving physical systems

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