correct job sizes for bootstrapping on large machines

previously on large machines the analysis would use just few processes
for calculating the bootstrapped samples, now we make sure
there are at least as many jobs in the iterator as there are cores,
or all job sizes are equal 1 and there are as many as there are
specified bootstrapps

tlsfuzzer/analysis.py

@@ -698,7 +698,8 @@ def _bootstrap_differences(self, pair, reps=5000):
         _diffs = data.iloc[:, pair.index2] -\
             data.iloc[:, pair.index1]
 
-        job_size = os.cpu_count() * 10
+        job_count = os.cpu_count() * 4
+        job_size = max(reps // job_count, 1)
 
         keys = ("mean", "median", "trim_mean_05", "trim_mean_25", "trimean")
 
@@ -708,7 +709,7 @@ def _bootstrap_differences(self, pair, reps=5000):
                 as pool:
             cent_tend = pool.imap_unordered(
                 self._cent_tend_of_random_sample,
-                chain(repeat(job_size, reps//job_size), [reps % job_size]))
+                chain(repeat(job_size, reps // job_size), [reps % job_size]))
 
             for values in cent_tend:
                 # handle reps % job_size == 0