Force use of fork in multiprocessing

From Tomasz Balawajder:
"Since we are using a Java service to launch the Python process, its behavior differs from running the script directly on the cluster.

By default, Dask uses fork() to create worker processes. However, when running under the JVM, the start method defaults to spawn, which does not share memory between processes. This caused the slowdown and unexpected behavior.

I’ve forced Python to use fork() in the configuration, and now the application completes in the same time as when executed with sbatch."

src/seismic_hazard_forecasting.py

@@ -69,6 +69,7 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
     from matplotlib.contour import ContourSet
     import xml.etree.ElementTree as ET
     import json
+    import multiprocessing as mp
 
     logger = getDefaultLogger('igfash')
 
@@ -448,9 +449,10 @@ verbose: {verbose}")
 
         start = timer()
 
-        use_pp = False
+        use_pp = True
 
         if use_pp:         # use dask parallel computing
+            mp.set_start_method("fork", force=True)
             pbar = ProgressBar()
             pbar.register()
             iter = indices