https://github.com/abrg-models/neoarealize
Neo-cortex arealization modelling
https://github.com/abrg-models/neoarealize
Last synced: 8 months ago
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Neo-cortex arealization modelling
- Host: GitHub
- URL: https://github.com/abrg-models/neoarealize
- Owner: ABRG-Models
- License: gpl-3.0
- Created: 2018-06-29T13:49:46.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-01-10T16:25:01.000Z (over 6 years ago)
- Last Synced: 2024-12-27T08:27:10.380Z (over 1 year ago)
- Language: C++
- Size: 6.15 MB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NeoArealize
A model of reaction-diffusion pattern formation in neocortex.
Pre-requisites:
Build and install morphologica from:
https://github.com/ABRG-Models/morphologica
Make sure these packages are installed (Debian/Ubuntu example):
sudo apt install python python-numpy xterm
Build and install jsoncpp (in a directory '~/src', just for example,
you can build it wherever suits):
```bash
mkdir -p ~/src
cd ~/src
git clone https://github.com/open-source-parsers/jsoncpp.git
cd jsoncpp
mkdir build
cd build
cmake -DCMAKE_INSTALL_PREFIX=/usr/local -DBUILD_SHARED_LIBS=YES ..
make
sudo make install
```
Now you can build NeoArealize:
```bash
cd NeoArealize
mkdir build
cd build
cmake ..
make
cd ..
```
To run:
```bash
./build/sim/james1 ./configs/c1.json
```
The program reads parameters from c1.json and writes results into
./logs/
The program is multi-threaded (using OpenMP pragmas). To get the best
performance it's usually necessary to experiment. Use a computation
only program (like james1c) and set different values for
OMP_NUM_THREADS. For example:
```bash
export OMP_NUM_THREADS=10 && time ./build/sim/james1c ./configs/c1.json >/dev/null
```
Or use the findfastest.sh script in misc/.
On an Intel i9 7980XE with 18 cores, 13 seems to be fastest, but
there's not much difference for the range 6 cores to 18 cores.
On an AMD Threadripper 2990WX with 32 cores, about 20 seems to be
fastest. This CPU is slower for these reaction diffusion equations
than the 7980XE. (2 seconds for the Intel, 3 seconds for the AMD).