mpi: Drop redundant check and defs

devito/data/data.py

@@ -37,11 +37,11 @@ class Data(np.ndarray):
     -----
     NumPy array subclassing is described at: ::
 
-        https://docs.scipy.org/doc/numpy-1.13.0/user/basics.subclassing.html
+        https://numpy.org/doc/stable/user/basics.subclassing.html
 
     Any view or copy created from ``self``, for instance via a slice operation
     or a universal function ("ufunc" in NumPy jargon), will still be of type
-    Data.
+    `Data`.
     """
 
     def __new__(cls, shape, dtype, decomposition=None, modulo=None, allocator=ALLOC_FLAT,
@@ -224,7 +224,7 @@ def __getitem__(self, glb_idx, comm_type, gather_rank=None):
                     glb_shape = self._distributor.glb_shape
                 retval = np.zeros(glb_shape, dtype=self.dtype.type)
                 start, stop, step = 0, 0, 1
-                for i, s in enumerate(sendcounts):
+                for i, _ in enumerate(sendcounts):
                     if i > 0:
                         start += sendcounts[i-1]
                     stop += sendcounts[i]

devito/mpi/distributed.py

@@ -2,7 +2,7 @@
 from ctypes import c_int, c_void_p, sizeof
 from itertools import groupby, product
 from math import ceil, pow
-from sympy import root, primefactors
+from sympy import primefactors
 
 import atexit
 
@@ -618,26 +618,22 @@ class CustomTopology(tuple):
     def __new__(cls, items, input_comm):
         # Keep track of nstars and already defined decompositions
         nstars = len([i for i in items if i == '*'])
-        alloc_procs = np.prod([i for i in items if i != '*'])
-        remprocs = int(input_comm.size // alloc_procs)
 
         # If no stars exist we are ready
         if nstars == 0:
             processed = items
-        # If all inputs are stars, and nstars root exists slice as evenly as possible
-        elif nstars == len(items) and root(remprocs, nstars).is_Integer:
-            dd = root(remprocs, nstars)
-            processed = as_tuple([int(dd) for i in range(nstars)])
-        # Process nstars > 0
         else:
+            # Init decomposition list
             processed = [1] * len(items)
 
-            # Get star and ints positions
+            # Get star and integer indices
             int_pos = [i for i, item in enumerate(items) if isinstance(item, int)]
             int_vals = [item for item in items if isinstance(item, int)]
             star_pos = [i for i, item in enumerate(items) if not isinstance(item, int)]
 
             # Decompose the processes remaining for allocation to prime factors
+            alloc_procs = np.prod([i for i in items if i != '*'])
+            remprocs = int(input_comm.size // alloc_procs)
             prime_factors = primefactors(remprocs)
 
             star_i = -1