## 1 Introduction
The CPU-based ([[https://github.com/davidaquilue/TVBAdEx_ParSweep/tree/main](https://github.com/davidaquilue/TVBAdEx_ParSweep/tree/main)]([https://github.com/davidaquilue/TVBAdEx_ParSweep/tree/main](https://github.com/davidaquilue/TVBAdEx_ParSweep/tree/main)), accessed on 1 November 2023) version of the TVB-AdEx has previously been used to study scale-integrated mechanisms underlying the emergence of conscious- and unconscious-like brain state dynamics using a local PC, EBRAINS ([[https://www.ebrains.eu/](https://www.ebrains.eu/)]([https://www.ebrains.eu/](https://www.ebrains.eu/)), accessed on 1 November 2023), an online aggregate of models [9], and an MPI multi-node CPU setup to study limited compound parametrizations for five parameters known to be fundamental to the behaviour of this AdEx model [10]. Here, we report that our GPU implementation of the TVB-AdEx model substantially ameliorates the original CPU instantiation by accelerating model performance and facilitating post data-analysis. The TVB-HPC framework is not another platform; it represents an expanded framework built upon RateML, TVB's [21] model generator [22]. RateML is performant, modular, reusable, and outperforms solutions such as neurolib [11], FastTVB [23], and Pyrates [24] in terms of magnitude of explorable parameter space and concurrent TVB instances. Specifically, our results show that it is an excellent compute accelerator for TVB-AdEx simulations, allowing for high-dimensional explorations of the parameter space and the identification of parameters acting and interacting to bias brain state dynamics. This work highlights the importance of performant processing for complex brain models, such as biophysical multi-scale TVB-AdEx models, since the dimensionality of parameter space grows easily, quickly turning the problem intractable on previously existing simulation platforms. By applying a wide range of accessible analysis methods, users are further enabled to explore observables describing simulated brain dynamics, including power spectral densities, exhibitory and inhibitory firing rates, the quantification of up and down states, and structure-function relationships, using a repository where these tools are publicly available in EBRAINS ([[https://wiki.ebrains.eu/bin/view/Collabs/rateml-tvb/Lab](https://wiki.ebrains.eu/bin/view/Collabs/rateml-tvb/Lab)]([https://wiki.ebrains.eu/bin/view/Collabs/rateml-tvb/Lab](https://wiki.ebrains.eu/bin/view/Collabs/rateml-tvb/Lab)), accessed on 1 November 2023). The TVB-HPC project, including the driver and GPU AdEx model, analysis toolkit, and graph script are also available at the following link: [[https://github.com/DeLaVlag/vast-TVB-space](https://github.com/DeLaVlag/vast-TVB-space)]([https://github.com/DeLaVlag/vast-TVB-space](https://github.com/DeLaVlag/vast-TVB-space))(accessed on 1 November 2023). We report that a single GPU, an NVIDIA A100 Tensor Core GPU with 40 GB and Compute Unified Device Architecture (CUDA) version 12.2.0, can be employed to explore roughly 17,000 parameter combinations (unique TVB simulations), simulating 68 brain regions for 50,000 simulation steps of 0.1 ms each. Utilizing a configuration comprising 384 nodes--the maximum available nodes for regular production--with four GPUs each facilitated the concurrent execution of approximately 25 million TVB instances. Theoretically, employing all 936 of the GPU-enhanced nodes within the Juwels Booster supercomputing cluster ([[https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html)]([https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html)), accessed on 1 November 2023), each with four GPUs connected via NVLink3 (high-speed communication technology), would allow a simulation to concurrently execute approximately 60 million TVB instances, each configured with a distinct set of parameters.