High-performance Simulation Environment Research Articles

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.

Performing Structural Design and Modeling of Transformers Using ANSYS-Maxwell

Transformers have attracted great interest since they have been used due to their robustness and application in power systems. Therefore, the nominal values of transformers grow even more in larger power systems due to the constantly increasing power demand. Many types of research are carried out to increase the performance characteristics of transformers and their compatibility with power systems. There are different methods and analysis tools for these studies. One of them is ANSYS@Maxwell, which performs analysis based on the Finite Element Method (FEM). With this program, the design, modeling, analysis, and performance evaluation of the transformer in a high-performance simulation environment can be achieved through effective strategic modeling. In this study, the design and modeling of a three-phase core-type transformer with coils and terminals are explained in detail in ANSYS @ Maxwell simulation platform. Besides, the transformer models examined were adapted using ANSYS@MAXWELL software based on the finite element method. Analyzes are performed to estimate the core-losses, leak-losses, DC-losses, and winding-eddy current losses-of transformers with this program. A large number of meshes were used in FEM analysis of 2D and 3D models to examine the losses in detail.

CLONES : a closed-loop simulation framework for body, muscles and neurons

The activity of neurons does not only reflect cognitive states, it is also highly constrained by mechanical properties of the body: sensors, muscles, tendons, and their interaction with the environment. Thus, in order to model the activity of neurons, it seems necessary to take a holistic approach, where the behavior of an animal is modeled through the interaction between neurons, muscles and the environment. This involves knowledge in both neurophysiology and biomechanics. It also requires the development of software that can simulate the interaction between neurons and muscles efficiently. In order to achieve this, we have developed an open-source library called CLONES (Closed Loop Neural Simulation). CLONES is a communication interface between the BRIAN neural simulator [1], and SOFA, a physics engine for biomedical applications [2]. BRIAN and SOFA are both intuitive and high performance simulation environments. Communication between BRIAN and SOFA is achieved through shared memory and semaphores. A single step of simulation typically takes place on different CPU cores. Once a simulation step has been completed in both simulators, sensory inputs to the neurons and motor outputs to the muscles are updated. SOFA uses an interpreted XML description of a physical scene. The scene can be modified without interrupting the simulation. CLONES contains a SOFA plugin and a PYTHON module. The Python module provides a function to be called after each step of the simulation. The Sofa plugin provides components for the simulation of muscles, sensors, drag forces. In addition, several demonstration examples are provided with the library. In particular, a neuro-mechanical model of undulatory locomotion in the worm caenorhabditis elegans[3] is available.

Design of high level modelling / high performance simulation environments

Advances in massively parallel platforms are increasing the prospects for high performance discrete event simulation. Still the difficulty in parallel programming persists and there is increasing demand for high level support for building discrete event models to execute on such platforms. We present a parallel DEVS-based (Discrete Event System Specification) simulation environment that can execute on distributed memory multicomputer systems with benchmarking results of a class of high resolution, large scale ecosystem models. Underlying the environment is a parallel container class library for hiding the details of message passing technology while providing high level abstractions for hierarchical, modular DEVS models. The C++ implementation working on the Thinking Machines CM-5 demonstrates that the desire for high level modeling support need not be irreconcilable with sustained high performance.