IGITUGraz/L2L: v1.0.0-beta

Subramoney, Anand; Diaz-Pier, Sandra; Rao, A. Ravishankar; Scherr, Franz; Salaj, Darjan; Bohnstingl, Thomas; Jordan, Jakob; Kopp, Nikolaus; Hackhofer, Daniel; Stekovic, Sinisa

doi:10.5281/zenodo.2590760

Cited by 2 publications

(1 citation statement)

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“…For instance, to optimize resource utilization one could make use of stochastic gradient descent or other algorithms which selectively focus on regions of interest within the parameter space. While RateML can scale models to tens of thousands of parameter combinations, hyper-parameter optimization can be delegated to a framework such as Learning to Learn (L2L) (Subramoney et al, 2019). This framework is a hyper parameter optimizing network consisting of two loops, an inner loop which handles the process to be optimized and an outer loop which handles the hyper parameter optimization.…”

Section: Discussionmentioning

confidence: 99%

RateML: A Code Generation Tool for Brain Network Models

Vlag

Woodman

Fousek

et al. 2022

Front. Netw. Physiol.

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Whole brain network models are now an established tool in scientific and clinical research, however their use in a larger workflow still adds significant informatics complexity. We propose a tool, RateML, that enables users to generate such models from a succinct declarative description, in which the mathematics of the model are described without specifying how their simulation should be implemented. RateML builds on NeuroML’s Low Entropy Model Specification (LEMS), an XML based language for specifying models of dynamical systems, allowing descriptions of neural mass and discretized neural field models, as implemented by the Virtual Brain (TVB) simulator: the end user describes their model’s mathematics once and generates and runs code for different languages, targeting both CPUs for fast single simulations and GPUs for parallel ensemble simulations. High performance parallel simulations are crucial for tuning many parameters of a model to empirical data such as functional magnetic resonance imaging (fMRI), with reasonable execution times on small or modest hardware resources. Specifically, while RateML can generate Python model code, it enables generation of Compute Unified Device Architecture C++ code for NVIDIA GPUs. When a CUDA implementation of a model is generated, a tailored model driver class is produced, enabling the user to tweak the driver by hand and perform the parameter sweep. The model and driver can be executed on any compute capable NVIDIA GPU with a high degree of parallelization, either locally or in a compute cluster environment. The results reported in this manuscript show that with the CUDA code generated by RateML, it is possible to explore thousands of parameter combinations with a single Graphics Processing Unit for different models, substantially reducing parameter exploration times and resource usage for the brain network models, in turn accelerating the research workflow itself. This provides a new tool to create efficient and broader parameter fitting workflows, support studies on larger cohorts, and derive more robust and statistically relevant conclusions about brain dynamics.

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Section: Discussionmentioning

confidence: 99%