Neuromapp: A Mini-application Framework to Improve Neural Simulators

Ewart, Timothée; Planas, Judit; Cremonesi, Francesco; Langen, Kai; Schürmann, Felix; Delalondre, Fabien

doi:10.1007/978-3-319-58667-0_10

Cited by 5 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Write benchmarks made use of the Neuromapp library (https://github.com/BlueBrain/ neuromapp, revision f03d3ea) [49], which uses parallel HDF5 and MPI underneath. Read benchmarks were implemented using the Python binding of Brion/Brain (revision c16a694), the testing and plotting code can be found in the SONATA github repository in the benchmarks branch.…”

Section: Benchmarkingmentioning

confidence: 99%

The SONATA data format for efficient description of large-scale network models

et al. 2020

Self Cite

View full text Add to dashboard Cite

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

show abstract

Section: Benchmarkingmentioning

confidence: 99%

The SONATA data format for efficient description of large-scale network models

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Write benchmarks made use of the Neuromapp library (https://github.com/BlueBrain/neuromapp, revision f03d3ea) (Ewart et al, 2017), which uses parallel HDF5 and MPI underneath. Read benchmarks were implemented using the Python binding of Brion/Brain (revision c16a694), the testing and plotting code can be found in the SONATA github repository in the benchmarks branch.…”

Section: Simulation Output Benchmarksmentioning

confidence: 99%

The SONATA Data Format for Efficient Description of Large-Scale Network Models

Dai

Hernando

Billeh

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

22Increasing availability of comprehensive experimental datasets and of high-performance 23 computing resources are driving rapid growth in scale, complexity, and biological realism of 24 computational models in neuroscience. To support construction and simulation, as well as 25 sharing of such large-scale models, a broadly applicable, flexible, and high-performance data 26 format is necessary. To address this need, we have developed the Scalable Open Network 27 Architecture TemplAte (SONATA) data format. It is designed for memory and computational 28 efficiency and works across multiple platforms. The format represents neuronal circuits and 29 simulation inputs and outputs via standardized files and provides much flexibility for adding new 30 conventions or extensions. SONATA is used in multiple modeling and visualization tools, and 31 we also provide reference Application Programming Interfaces and model examples to catalyze 32 further adoption. SONATA format is free and open for the community to use and build upon 33 with the goal of enabling efficient model building, sharing, and reproducibility.34 68 compute environments. Another is that existing formats describe either static models or 69 simulation outputs, but not both. And, for broad adoption of a modeling data format, it needs to 70 be flexible enough to represent a variety of model types (point neuron, biophysically detailed, 71 etc.) and compatible with more specialized formats (e.g., SWC for neuronal morphologies 72 (Cannon et al., 1998)), without compromising computational performance. 73 3 74

show abstract

“…Even though it was not critical at this point, to further speed up this part of the code, it is worthwhile to point out that an alternative implementation referred as multisend (sending spike trains during multiple rounds through a minimum time delay [3]) has been migrated from NEURON to coreNEURON. In addition, several implementations of the queuing algorithm which is part of the Spike Exchange functionality have been investigated and summarized in [5] as part of the NeuroMapp library development. Using these optimal setups (KNL in cache mode, 32 MPI processes and 4 OMP threads per node, auto-vectorized and the used of SOA layout, OMP guided policy), we have performed a strong scaling study of coreNEURON on up to 2048 nodes of Theta (Figure 6).…”

Section: Configurations and Optimization For Thetamentioning

confidence: 99%

“…As introducing a new solver in coreNEURON or NEURON requires a lot of code refactoring, we decided to first use the NeuroMapp library [5], a miniapp library to evaluate the implementation of several solvers. As such, both implicit and explicit methods were introduced into NeuroMapp and comparison were made between the various solvers at the level of a single compartment.…”

Section: Supporting Very Strong Scalingmentioning

confidence: 99%

Large-Scale Simulation of Brain Tissue, Blue Brain Project, EPFL

Williams

Balakrishnan

Delalondre

et al. 2018

Self Cite

View full text Add to dashboard Cite

The initial version of this project described in the awarded proposal aimed at largely focusing on the evaluation of Intel KNL hardware and the development of new scientific use cases such as the neurorobotics and whole brain modeling. However, during the course of the project EPFL has been awarded a high impact INCITE project focused on the development of models to support brain plasticity which is considered as the key toward understanding memory. Considering this very prestigious opportunity a large part of our developments has been refocused on the development of methods to support the plasticity use case which is described in Annex 1 along with the awarded INCITE proposal. [Proposal not included here. -Ed.]

show abstract

Neuromapp: A Mini-application Framework to Improve Neural Simulators

Cited by 5 publications

References 10 publications

The SONATA data format for efficient description of large-scale network models

The SONATA data format for efficient description of large-scale network models

The SONATA Data Format for Efficient Description of Large-Scale Network Models

Large-Scale Simulation of Brain Tissue, Blue Brain Project, EPFL

Contact Info

Product

Resources

About