Docs: Remove TBB, updated C++14 (#1114)

Update the docs landing page and add pip install from git.

Fixes #1118

doc/conf.py

@@ -4,7 +4,7 @@
 html_static_path = ['static']
 
 def setup(app):
-    app.add_stylesheet('custom.css')
+    app.add_css_file('custom.css')
     app.add_object_type('generic', 'gen', 'pair: %s; generic')
 
 extensions = ['sphinx.ext.todo', 'sphinx.ext.mathjax']

doc/index.rst

@@ -4,55 +4,24 @@ Arbor
 .. image:: https://travis-ci.org/arbor-sim/arbor.svg?branch=master
     :target: https://travis-ci.org/arbor-sim/arbor
 
-What is Arbor?
---------------
-Arbor is a high-performance library for computational neuroscience simulations, being developed in work package 7.5.4 of the `Human Brain Project <//www.humanbrainproject.eu>`_.
+Arbor is a high-performance library for computational neuroscience simulations
+with morphologically-detailed cells, from single cell models to very large networks.
 
-The development team is based in:
-
-* `Swiss National Supercomputing Center <//www.cscs.ch>`_ (CSCS)
-* `Jülich Supercomputing Centre <//www.fz-juelich.de/ias/jsc/EN/>`_ (JSC)
+The development team is from HPC centers, aiming to help neuroscientists
+effectively use contemporary and future HPC systems to meet their simulation needs.
 
 Arbor is designed from the ground up for **many core**  architectures:
 
-    * Written in C++11 and CUDA;
+    * Written in modern C++ and CUDA;
     * Distributed parallelism using MPI;
     * Multithreading with TBB and C++11 threads;
     * **Open source** and **open development**;
     * Sound development practices: **unit testing**, **continuous Integration**,
       and **validation**.
 
-Features
---------
-
-We are actively developing `Arbor <https://github.com/arbor-sim/arbor>`_, improving performance and adding features.
-Some key features include:
-
-    * Optimized back end for CUDA
-    * Optimized vector back ends for Intel (KNL, AVX, AVX2) and Arm (ARMv8-A NEON) intrinsics.
-    * Asynchronous spike exchange that overlaps compute and communication.
-    * Efficient sampling of voltage and current on all back ends.
-    * Efficient implementation of all features on GPU.
-    * Reporting of memory and energy consumption (when available on platform).
-    * An API for addition of new cell types, e.g. LIF and Poisson spike generators.
-    * Validation tests against numeric/analytic models and NEURON.
-
 Citing Arbor
 ------------
 
-.. |DOI-v0.1| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1459679.svg
-     :target: https://doi.org/10.5281/zenodo.1459679
-
-.. |DOI-v0.2| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2583709.svg
-    :target: https://doi.org/10.5281/zenodo.2583709
-
-Specific versions of Arbor can be cited via Zenodo:
-
-   * v0.2:  |DOI-v0.2|
-   * v0.1:  |DOI-v0.1|
-
-The following BibTeX can be used to cite Arbor:
-
 .. code-block:: latex
 
     @INPROCEEDINGS{

doc/python.rst

@@ -15,7 +15,7 @@ The easiest way to get Arbor is with
 
     pip3 install arbor
 
-It is also possible to use Setuptools directly on a local copy of the source code:
+Every point release it pushed to the Python Package Index. If you wish to install another version, it is also possible to use Setuptools directly on a local copy of the source code, or instruct `pip` to install directly from our git repository:
 
 .. code-block:: bash
 
@@ -23,6 +23,12 @@ It is also possible to use Setuptools directly on a local copy of the source cod
     git clone https://github.com/arbor-sim/arbor.git --recursive
     python3 install ./arbor/setup.py
 
+    # tell pip to build and install from master
+    pip install git+https://github.com/arbor-sim/arbor.git
+
+.. note::
+    You will need to have some development packages installed in order to build Arbor this way. For Debian/Ubuntu: `sudo apt install build-essential python-dev`, Fedora/Red Hat/CentOS: `sudo yum install @development-tools python-devel`.
+
 .. note::
     Arbor's Setuptools process simplifies installation for common configurations
     on laptops and workstations by calling CMake under the hood.