Started implemented parallelization along broadcasted dimensions

reproject/common.py

@@ -97,6 +97,23 @@ def _reproject_dispatcher(
     if reproject_func_kwargs is None:
         reproject_func_kwargs = {}
 
+    # Determine whether any broadcasting is taking place
+    broadcasting = wcs_in.low_level_wcs.pixel_n_dim < len(shape_out)
+
+    # Determine whether block size indicates we should parallelize over broadcasted dimension
+    broadcasted_parallelization = False
+    if broadcasting and block_size:
+        if len(block_size) == len(shape_out):
+            if (
+                block_size[-wcs_in.low_level_wcs.pixel_n_dim :]
+                == shape_out[-wcs_in.low_level_wcs.pixel_n_dim :]
+            ):
+                broadcasted_parallelization = True
+                block_size = (
+                    block_size[: -wcs_in.low_level_wcs.pixel_n_dim]
+                    + (-1,) * wcs_in.low_level_wcs.pixel_n_dim
+                )
+
     # We set up a global temporary directory since this will be used e.g. to
     # store memory mapped Numpy arrays and zarr arrays.
 
@@ -154,28 +171,10 @@ def _reproject_dispatcher(
 
         shape_in = array_in.shape
 
-        # When in parallel mode, we want to make sure we avoid having to copy the
-        # input array to all processes for each chunk, so instead we write out
-        # the input array to a Numpy memory map and load it in inside each process
-        # as a memory-mapped array. We need to be careful how this gets passed to
-        # reproject_single_block so we pass a variable that can be either a string
-        # or the array itself (for synchronous mode). If the input array is a dask
-        # array we should always write it out to a memmap even in synchronous mode
-        # otherwise map_blocks gets confused if it gets two dask arrays and tries
-        # to iterate over both.
-
-        if isinstance(array_in, da.core.Array) or parallel:
-            array_in_or_path = as_delayed_memmap_path(array_in, tmp_dir)
-        else:
-            # Here we could set array_in_or_path to array_in_path if it
-            # has been set previously, but in synchronous mode it is better to
-            # simply pass a reference to the memmap array itself to avoid having
-            # to load the memmap inside each reproject_single_block call.
-            array_in_or_path = array_in
-
         def reproject_single_block(a, array_or_path, block_info=None):
             if a.ndim == 0 or block_info is None or block_info == []:
                 return np.array([a, a])
+
             slices = [slice(*x) for x in block_info[None]["array-location"][-wcs_out.pixel_n_dim :]]
 
             if isinstance(wcs_out, BaseHighLevelWCS):
@@ -209,37 +208,70 @@ def reproject_single_block(a, array_or_path, block_info=None):
         # NOTE: the following array is just used to set up the iteration in map_blocks
         # but isn't actually used otherwise - this is deliberate.
 
-        if block_size:
-            if wcs_in.low_level_wcs.pixel_n_dim < len(shape_out):
-                if len(block_size) < len(shape_out):
-                    block_size = [-1] * (len(shape_out) - len(block_size)) + list(block_size)
-                else:
-                    for i in range(len(shape_out) - wcs_in.low_level_wcs.pixel_n_dim):
-                        if block_size[i] != -1 and block_size[i] != shape_out[i]:
-                            raise ValueError(
-                                "block shape for extra broadcasted dimensions should cover entire array along those dimensions"
-                            )
+        if broadcasted_parallelization:
             array_out_dask = da.empty(shape_out, chunks=block_size)
+            array_in = array_in.rechunk(block_size)
+
+            result = da.map_blocks(
+                reproject_single_block,
+                array_out_dask,
+                array_in,
+                dtype=float,
+                new_axis=0,
+                chunks=(2,) + array_out_dask.chunksize,
+            )
+
         else:
-            if wcs_in.low_level_wcs.pixel_n_dim < len(shape_out):
-                chunks = (-1,) * (len(shape_out) - wcs_in.low_level_wcs.pixel_n_dim)
-                chunks += ("auto",) * wcs_in.low_level_wcs.pixel_n_dim
-                rechunk_kwargs = {"chunks": chunks}
+            # When in parallel mode, we want to make sure we avoid having to copy the
+            # input array to all processes for each chunk, so instead we write out
+            # the input array to a Numpy memory map and load it in inside each process
+            # as a memory-mapped array. We need to be careful how this gets passed to
+            # reproject_single_block so we pass a variable that can be either a string
+            # or the array itself (for synchronous mode). If the input array is a dask
+            # array we should always write it out to a memmap even in synchronous mode
+            # otherwise map_blocks gets confused if it gets two dask arrays and tries
+            # to iterate over both.
+
+            if isinstance(array_in, da.core.Array) or parallel:
+                array_in_or_path = as_delayed_memmap_path(array_in, tmp_dir)
             else:
-                rechunk_kwargs = {}
-            array_out_dask = da.empty(shape_out)
-            array_out_dask = array_out_dask.rechunk(
-                block_size_limit=8 * 1024**2, **rechunk_kwargs
-            )
+                # Here we could set array_in_or_path to array_in_path if it
+                # has been set previously, but in synchronous mode it is better to
+                # simply pass a reference to the memmap array itself to avoid having
+                # to load the memmap inside each reproject_single_block call.
+                array_in_or_path = array_in
+
+            if block_size:
+                if broadcasting:
+                    if len(block_size) < len(shape_out):
+                        block_size = [-1] * (len(shape_out) - len(block_size)) + list(block_size)
+                    else:
+                        for i in range(len(shape_out) - wcs_in.low_level_wcs.pixel_n_dim):
+                            if block_size[i] != -1 and block_size[i] != shape_out[i]:
+                                raise ValueError(
+                                    "block shape for extra broadcasted dimensions should cover entire array along those dimensions"
+                                )
+                array_out_dask = da.empty(shape_out, chunks=block_size)
+            else:
+                if broadcasting:
+                    chunks = (-1,) * (len(shape_out) - wcs_in.low_level_wcs.pixel_n_dim)
+                    chunks += ("auto",) * wcs_in.low_level_wcs.pixel_n_dim
+                    rechunk_kwargs = {"chunks": chunks}
+                else:
+                    rechunk_kwargs = {}
+                array_out_dask = da.empty(shape_out)
+                array_out_dask = array_out_dask.rechunk(
+                    block_size_limit=8 * 1024**2, **rechunk_kwargs
+                )
 
-        result = da.map_blocks(
-            reproject_single_block,
-            array_out_dask,
-            array_in_or_path,
-            dtype=float,
-            new_axis=0,
-            chunks=(2,) + array_out_dask.chunksize,
-        )
+            result = da.map_blocks(
+                reproject_single_block,
+                array_out_dask,
+                array_in_or_path,
+                dtype=float,
+                new_axis=0,
+                chunks=(2,) + array_out_dask.chunksize,
+            )
 
         # Ensure that there are no more references to Numpy memmaps
         array_in = None