Add some tests in a Massively Parallel Forecasting Architecture

[antoinecarme]

PyAF uses a parallel training process with 4 sub-processes by default , which is OK for a standard PC with about 8 cores. the number of these sub-processes is configurable.

pyaf/TS/Options.py

Line 121 in 2bee2a6

self.mNbCores = 8;

Need to see what happens and what can be improved when one has hunderds of Cores.

The Xeon-Phi Architecture, recently made EOL by Intel, is a good candidate for these tests. Each Xeon-Phi processor has at least 64 Xeon-like cores or 256 concurrent threads.

[antoinecarme]

setup a xeon-phi debian machine for the tests

New repository for debian-related config data :

https://github.com/antoinecarme/xeon-phi-data

[antoinecarme]

Hierarchical modeling is now parallelized differently. The number of cores needed is the number of nodes in the hierarchy, All individual node models are trained in parallel.

The same for forecasting. All individual node models are forecast in parallel.

[antoinecarme]

Closing.

Will be officially available in release 2.0