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The NEURON simulator has been developed over the past three decades and is widely used by neuroscientists to model the electrical activity of neuronal networks. Large network simulation projects using NEURON have supercomputer allocations that individually measure in the millions of core hours. Supercomputer centers are transitioning to next generation architectures and the work accomplished per core hour for these simulations could be improved by an order of magnitude if NEURON was able to better utilize those new hardware capabilities. In order to adapt NEURON to evolving computer architectures, the compute engine of the NEURON simulator has been extracted and has been optimized as a library called CoreNEURON. This paper presents the design, implementation, and optimizations of CoreNEURON. We describe how CoreNEURON can be used as a library with NEURON and then compare performance of different network models on multiple architectures including IBM BlueGene/Q, Intel Skylake, Intel MIC and NVIDIA GPU. We show how CoreNEURON can simulate existing NEURON network models with 4–7x less memory usage and 2–7x less execution time while maintaining binary result compatibility with NEURON.

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A finite element model of three-dimensional high speed machining is developed. In order to catch Adiabatic Shear Band (ASB), which is about few microns wide, the simulation uses mesh adaptation triggered by an isotropic error estimator. An enhanced version of the Zienkiewicz and Booromand REP in Patches technique is used. As ASB is a much localized phenomenon, the adaptive procedure provides highly refined meshes with strong gradients of the element size, which makes it quite difficult to produce satisfactory 3D meshes. Furthermore, high speed machining leads to very important values of strain rate, deformation and possibly to extreme mesh distortion. So, an Arbitrary Lagrangian Eulerian (ALE) method is employed. With the utilized splitting method and linear finite element interpolation, the transport of nodal variables is based on the gradient calculated in the upwind element. For variables stored at the integration points, a remapping procedure using patch recovery techniques is preferred. Finally, because of the very strong thermo-mechanical coupling taking place in ASB, several thermo-mechanical coupling schemes are studied. Explicit and fully implicit schemes are compared, showing that the second one offers a stabilizing effect and a better accuracy. All of these ingredients provide a fully automatic and process independent procedure which allows detecting and following the formation of Adiabatic Shear Band in High Speed Machining. The creation of 3D segmented chip is observed and compared to 2D reference results obtained by Baker in [1]. The influence of numerical coefficients like the mesh size is investigated. Other application to actual 3D high speed machining such as blanking is also presented.

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