diff --git a/heat/sparse/_operations.py b/heat/sparse/_operations.py
index 1d38114955..94946a1dde 100644
--- a/heat/sparse/_operations.py
+++ b/heat/sparse/_operations.py
@@ -135,13 +135,10 @@ def __binary_op_csr(
     else:
         result = operation(t1.larray.to(promoted_type), t2.larray.to(promoted_type), **fn_kwargs)
 
+    output_gnnz = torch.tensor(result._nnz())
     if output_split is not None:
-        output_gnnz = torch.tensor(result._nnz())
         output_comm.Allreduce(MPI.IN_PLACE, output_gnnz, MPI.SUM)
         output_gnnz = output_gnnz.item()
-    else:
-        output_gnnz = torch.tensor(result._nnz())
-
     output_type = types.canonical_heat_type(result.dtype)
 
     if out is None:
diff --git a/heat/sparse/dcsr_matrix.py b/heat/sparse/dcsr_matrix.py
index 468ab84c27..38c0245ea9 100644
--- a/heat/sparse/dcsr_matrix.py
+++ b/heat/sparse/dcsr_matrix.py
@@ -337,3 +337,22 @@ def __repr__(self) -> str:
         if self.comm.rank != 0:
             return ""
         return print_string
+
+    def __str__(self) -> str:
+        """
+        Computes a string representation of the passed ``DCSR_matrix``.
+        """
+        size = self.__gshape
+        nnz = self.__gnnz
+
+        print_string = (
+            f"DCSR_matrix(indices={self.indices},\n"
+            f"            indptr={self.indptr},\n"
+            f"            data={self.data},\n"
+            f"            size={size}, nnz={nnz}, split={self.__split})"
+        )
+
+        if self.comm.rank != 0:
+            return ""
+
+        return print_string