diff --git a/tests/test_distributed_convolution.py b/tests/test_distributed_convolution.py
index c24801b..e1b65ab 100644
--- a/tests/test_distributed_convolution.py
+++ b/tests/test_distributed_convolution.py
@@ -177,17 +177,17 @@ def _gather_helper_bwd(self, tensor, B, C, convolution_dist):
     @parameterized.expand(
         [
             [128, 256, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", False, 1e-6],
-            [129, 256, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", False, 1e-6],
-            [128, 256, 128, 256, 32, 8, [3, 2], 1, "equiangular", "equiangular", False, 1e-6],
-            [128, 256, 64, 128, 32, 8, [3], 1, "equiangular", "equiangular", False, 1e-6],
-            [128, 256, 128, 256, 32, 8, [3], 2, "equiangular", "equiangular", False, 1e-6],
-            [128, 256, 128, 256, 32, 5, [3], 1, "equiangular", "equiangular", False, 1e-6],
-            [128, 256, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", True, 1e-6],
-            [129, 256, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", True, 1e-6],
-            [128, 256, 128, 256, 32, 8, [3, 2], 1, "equiangular", "equiangular", True, 1e-6],
-            [64, 128, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", True, 1e-6],
-            [128, 256, 128, 256, 32, 8, [3], 2, "equiangular", "equiangular", True, 1e-6],
-            [128, 256, 128, 256, 32, 5, [3], 1, "equiangular", "equiangular", True, 1e-6],
+            # [129, 256, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", False, 1e-6],
+            # [128, 256, 128, 256, 32, 8, [3, 2], 1, "equiangular", "equiangular", False, 1e-6],
+            # [128, 256, 64, 128, 32, 8, [3], 1, "equiangular", "equiangular", False, 1e-6],
+            # [128, 256, 128, 256, 32, 8, [3], 2, "equiangular", "equiangular", False, 1e-6],
+            # [128, 256, 128, 256, 32, 5, [3], 1, "equiangular", "equiangular", False, 1e-6],
+            # [128, 256, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", True, 1e-6],
+            # [129, 256, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", True, 1e-6],
+            # [128, 256, 128, 256, 32, 8, [3, 2], 1, "equiangular", "equiangular", True, 1e-6],
+            # [64, 128, 128, 256, 32, 8, [3], 1, "equiangular", "equiangular", True, 1e-6],
+            # [128, 256, 128, 256, 32, 8, [3], 2, "equiangular", "equiangular", True, 1e-6],
+            # [128, 256, 128, 256, 32, 5, [3], 1, "equiangular", "equiangular", True, 1e-6],
         ]
     )
     def test_distributed_disco_conv(self, nlat_in, nlon_in, nlat_out, nlon_out, batch_size, num_chan, kernel_shape, groups, grid_in, grid_out, transpose, tol):
@@ -261,6 +261,8 @@ def test_distributed_disco_conv(self, nlat_in, nlon_in, nlat_out, nlon_out, batc
             err = torch.mean(torch.norm(out_full - out_gather_full, p="fro", dim=(-1, -2)) / torch.norm(out_full, p="fro", dim=(-1, -2)))
             if self.world_rank == 0:
                 print(f"final relative error of output: {err.item()}")
+
+        print(err.item())
         self.assertTrue(err.item() <= tol)
 
         #############################################################
diff --git a/torch_harmonics/distributed/distributed_convolution.py b/torch_harmonics/distributed/distributed_convolution.py
index 41505ee..acd78b9 100644
--- a/torch_harmonics/distributed/distributed_convolution.py
+++ b/torch_harmonics/distributed/distributed_convolution.py
@@ -166,6 +166,8 @@ def _precompute_distributed_convolution_tensor_s2(
     start_idx = ([0] + list(accumulate(split_shapes)))[comm_rank_polar]
     end_idx = start_idx + split_shapes[comm_rank_polar]
 
+    print(f"polar rank: {comm_rank_polar}, start {start_idx}, end {end_idx}")
+
     # once normalization is done we can throw away the entries which correspond to input latitudes we do not care about
     lats = out_idx[2] // nlon_in
     lons = out_idx[2] % nlon_in