{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/coreneuron-an-optimized-compute-engine-for","title":"CoreNEURON : An Optimized Compute Engine for the NEURON Simulator","arxiv_id":"1901.10975","date":"2019-01-30","proceeding":null,"authors":[],"abstract":"The NEURON simulator has been developed over the past three decades and is\nwidely used by neuroscientists to model the electrical activity of neuronal\nnetworks. Large network simulation projects using NEURON have supercomputer\nallocations that individually measure in the millions of core hours.\nSupercomputer centers are transitioning to next generation architectures and\nthe work accomplished per core hour for these simulations could be improved by\nan order of magnitude if NEURON was able to better utilize those new hardware\ncapabilities. In order to adapt NEURON to evolving computer architectures, the\ncompute engine of the NEURON simulator has been extracted and has been\noptimized as a library called CoreNEURON. This paper presents the design,\nimplementation and optimizations of CoreNEURON. We describe how CoreNEURON can\nbe used as a library with NEURON and then compare performance of different\nnetwork models on multiple architectures including IBM BlueGene/Q, Intel\nSkylake, Intel MIC and NVIDIA GPU. We show how CoreNEURON can simulate existing\nNEURON network models with 4-7x less memory usage and 2-7x less execution time\nwhile maintaining binary result compatibility with NEURON.","url_abs":"http://arxiv.org/abs/1901.10975v1","url_pdf":"http://arxiv.org/pdf/1901.10975v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"coreneuron-an-optimized-compute-engine-for","repo_url":"https://github.com/bluebrain/CoreNeuron","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}