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| 001 | 1010228 | ||
| 005 | 20231128201903.0 | ||
| 024 | 7 | _ | |a 10.5281/ZENODO.8233847 |2 doi |
| 037 | _ | _ | |a FZJ-2023-03030 |
| 100 | 1 | _ | |a Akar, Nora Abi |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Arbor Library (v0.9.0) |
| 250 | _ | _ | |a 0.9.0 |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Software |2 DCMI |
| 336 | 7 | _ | |a Software |b sware |m sware |0 PUB:(DE-HGF)33 |s 1701182961_28677 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Computer Program |0 6 |2 EndNote |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a Software |2 DataCite |
| 520 | _ | _ | |a After much more delay than anticipated, we are very happy to present a new Arbor release. Nearly 8 months of work is in it, much of which focussed on speed, optimisation, fixes and build system changes. This release includes Python 3.12 wheels as probably one of the first packages on PyPI.Major new features:(1) External Connectivity: Arbor can now interface with other simulators or processes through MPI. Contexts now accept a second MPI communicator and Recipes can implement a external_connections_on method to specify how simulations might be interacting. See documentation for more. (2) Arbor now supports checkpointing and resume from a previously stored checkpoint. Useful when a certain point in time needs to be explored in multiple directions, when you want to rewind to a previous state, etc. See documentation for more.(3) More painted parameters are now scalable through iexpr : temperature, capacitance, resistivity, membrane potential and the following ionic parameters: internal and external concentration, diffusivity, and reversal potential. See documentation.(4) A set of ANN activation functions for NMODL have been added: sigmoid(x) , relu(x) and tanh(x) . Control volume area is exposed through NMODL.(5) A revamped backend for ARM CPU's that support Scalable Vector Extension (SVE). Arbor and modcc are now fully compatible, so users who have access to A64FX-based HPC can take full advantage of that hardware. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
| 536 | _ | _ | |a SLNS - SimLab Neuroscience (Helmholtz-SLNS) |0 G:(DE-Juel1)Helmholtz-SLNS |c Helmholtz-SLNS |x 2 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a neuroscience |2 Other |
| 650 | _ | 7 | |a computational neuroscience |2 Other |
| 650 | _ | 7 | |a morpholically detailed simulator |2 Other |
| 700 | 1 | _ | |a Biddiscombe, John |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Cumming, Benjamin |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Kabic, Marko |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Karakasis, Vasileios |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Klijn, Wouter |0 P:(DE-Juel1)168169 |b 5 |u fzj |
| 700 | 1 | _ | |a Küsters, Anne |0 P:(DE-Juel1)166193 |b 6 |
| 700 | 1 | _ | |a Peyser, Alexander |0 P:(DE-Juel1)161525 |b 7 |
| 700 | 1 | _ | |a Yates, Stuart |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Hater, Thorsten |0 P:(DE-Juel1)176815 |b 9 |u fzj |
| 700 | 1 | _ | |a Huisman, Brent |0 P:(DE-Juel1)175163 |b 10 |e Corresponding author |
| 700 | 1 | _ | |a Hagen, Espen |0 P:(DE-Juel1)164166 |b 11 |
| 700 | 1 | _ | |a Schepper, Robin De |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Linssen, Charl |0 P:(DE-Juel1)176305 |b 13 |u fzj |
| 700 | 1 | _ | |a Stoppels, Harmen |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Schmitt, Sebastian |0 P:(DE-Juel1)161557 |b 15 |
| 700 | 1 | _ | |a Huber, Felix |0 P:(DE-Juel1)176143 |b 16 |
| 700 | 1 | _ | |a Engelen, Max |0 P:(DE-Juel1)188863 |b 17 |
| 700 | 1 | _ | |a Bösch, Fabian |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Luboeinski, Jannik |0 P:(DE-HGF)0 |b 19 |
| 700 | 1 | _ | |a Frasch, Simon |0 P:(DE-HGF)0 |b 20 |
| 700 | 1 | _ | |a Drescher, Lukas |0 P:(DE-HGF)0 |b 21 |
| 700 | 1 | _ | |a Landsmeer, Lennart |0 P:(DE-Juel1)184553 |b 22 |
| 773 | _ | _ | |a 10.5281/ZENODO.8233847 |
| 856 | 4 | _ | |u https://zenodo.org/record/8233847 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1010228/files/arbor-v0.9.0-full.tar.gz |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:1010228 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)168169 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)166193 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)161525 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)176815 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 10 |6 P:(DE-Juel1)175163 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 13 |6 P:(DE-Juel1)176305 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a sware |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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