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Source code for pylops_mpi.DistributedArray

@@ -478,7 +482,7 @@

Source code for pylops_mpi.DistributedArray

     local_shape : :obj:`tuple`
         Shape of the local array.
     """
-    if partition == Partition.BROADCAST:
+    if partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST]:
         local_shape = global_shape
     # Split the array
     else:
@@ -502,10 +506,13 @@ 

Source code for pylops_mpi.DistributedArray

     .. warning:: When setting the partition of the DistributedArray to
         :obj:`pylops_mpi.Partition.BROADCAST`, it is crucial to be aware
         that any attempts to make arrays different from rank to rank will be
-        overwritten by the actions of rank 0. This means that if you modify
-        the DistributedArray on a specific rank, and are using broadcast to
-        synchronize the arrays across all ranks, the modifications made by other
-        ranks will be discarded and overwritten with the value at rank 0.
+        overwritten by the actions of rank 0. This is accomplished internally
+        by broadcasting the content of rank 0 every time a modification of
+        the array is attempted. Such a behaviour does however incur a cost
+        as communication may be not needed if the user ensures not to modify
+        the content of the array in different ranks in a different way. To
+        avoid broadcasting, one can use :obj:`pylops_mpi.Partition.UNSAFE_BROADCAST`
+        instead.
 
     Parameters
     ----------
@@ -515,11 +522,14 @@ 

Source code for pylops_mpi.DistributedArray

         MPI Communicator over which array is distributed.
         Defaults to ``mpi4py.MPI.COMM_WORLD``.
     partition : :obj:`Partition`, optional
-        Broadcast or Scatter the array. Defaults to ``Partition.SCATTER``.
+        Broadcast, UnsafeBroadcast, or Scatter the array. Defaults to ``Partition.SCATTER``.
     axis : :obj:`int`, optional
         Axis along which distribution occurs. Defaults to ``0``.
     local_shapes : :obj:`list`, optional
         List of tuples or integers representing local shapes at each rank.
+    mask : :obj:`list`, optional
+        Mask defining subsets of ranks to consider when performing 'global'
+        operations on the distributed array such as dot product or norm.
     engine : :obj:`str`, optional
         Engine used to store array (``numpy`` or ``cupy``)
     dtype : :obj:`str`, optional
@@ -530,6 +540,7 @@ 

Source code for pylops_mpi.DistributedArray

                  base_comm: Optional[MPI.Comm] = MPI.COMM_WORLD,
                  partition: Partition = Partition.SCATTER, axis: int = 0,
                  local_shapes: Optional[List[Union[Tuple, Integral]]] = None,
+                 mask: Optional[List[Integral]] = None,
                  engine: Optional[str] = "numpy",
                  dtype: Optional[DTypeLike] = np.float64):
         if isinstance(global_shape, Integral):
@@ -538,13 +549,15 @@ 

Source code for pylops_mpi.DistributedArray

             raise IndexError(f"Axis {axis} out of range for DistributedArray "
                              f"of shape {global_shape}")
         if partition not in Partition:
-            raise ValueError(f"Should be either {Partition.BROADCAST} "
-                             f"or {Partition.SCATTER}")
+            raise ValueError(f"Should be either {Partition.BROADCAST}, "
+                             f"{Partition.UNSAFE_BROADCAST} or {Partition.SCATTER}")
         self.dtype = dtype
         self._global_shape = _value_or_sized_to_tuple(global_shape)
         self._base_comm = base_comm
         self._partition = partition
         self._axis = axis
+        self._mask = mask
+        self._sub_comm = base_comm if mask is None else base_comm.Split(color=mask[base_comm.rank], key=base_comm.rank)
 
         local_shapes = local_shapes if local_shapes is None else [_value_or_sized_to_tuple(local_shape) for local_shape in local_shapes]
         self._check_local_shapes(local_shapes)
@@ -562,6 +575,9 @@ 

Source code for pylops_mpi.DistributedArray

         `Partition.SCATTER` - Local Arrays are assigned their
         unique values.
 
+        `Partition.UNSAFE_SCATTER` - Local Arrays are assigned their
+        unique values.
+
         `Partition.BROADCAST` - The value at rank-0 is broadcasted
         and is assigned to all the ranks.
 
@@ -608,6 +624,16 @@ 

Source code for pylops_mpi.DistributedArray

         """
         return self._local_shape
 
+    @property
+    def mask(self):
+        """Mask of the Distributed array
+
+        Returns
+        -------
+        engine : :obj:`list`
+        """
+        return self._mask
+
     @property
     def engine(self):
         """Engine of the Distributed array
@@ -689,6 +715,16 @@ 

Source code for pylops_mpi.DistributedArray

         """
         return self.base_comm.allgather(self.local_shape)
 
+    @property
+    def sub_comm(self):
+        """MPI Sub-Communicator
+
+        Returns
+        -------
+        sub_comm : :obj:`MPI.Comm`
+        """
+        return self._sub_comm
+
     def asarray(self):
         """Global view of the array
 
@@ -700,7 +736,7 @@ 

Source code for pylops_mpi.DistributedArray

             Global Array gathered at all ranks
         """
         # Since the global array was replicated at all ranks
-        if self.partition == Partition.BROADCAST:
+        if self.partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST]:
             # Get only self.local_array.
             return self.local_array
         # Gather all the local arrays and apply concatenation.
@@ -712,7 +748,8 @@ 

Source code for pylops_mpi.DistributedArray

                 base_comm: MPI.Comm = MPI.COMM_WORLD,
                 partition: Partition = Partition.SCATTER,
                 axis: int = 0,
-                local_shapes: Optional[List[Tuple]] = None):
+                local_shapes: Optional[List[Tuple]] = None,
+                mask: Optional[List[Integral]] = None):
         """Convert A Global Array to a Distributed Array
 
         Parameters
@@ -727,9 +764,12 @@ 

Source code for pylops_mpi.DistributedArray

             Axis of Distribution
         local_shapes : :obj:`list`, optional
             Local Shapes at each rank.
+        mask : :obj:`list`, optional
+            Mask defining subsets of ranks to consider when performing 'global'
+            operations on the distributed array such as dot product or norm.
 
         Returns
-        ----------
+        -------
         dist_array : :obj:`DistributedArray`
             Distributed Array of the Global Array
         """
@@ -738,9 +778,10 @@ 

Source code for pylops_mpi.DistributedArray

                                       partition=partition,
                                       axis=axis,
                                       local_shapes=local_shapes,
+                                      mask=mask,
                                       engine=get_module_name(get_array_module(x)),
                                       dtype=x.dtype)
-        if partition == Partition.BROADCAST:
+        if partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST]:
             dist_array[:] = x
         else:
             slices = [slice(None)] * x.ndim
@@ -759,7 +800,7 @@ 

Source code for pylops_mpi.DistributedArray

                 raise ValueError(f"Length of local shapes is not equal to number of processes; "
                                  f"{len(local_shapes)} != {self.size}")
             # Check if local shape == global shape
-            if self.partition is Partition.BROADCAST and local_shapes[self.rank] != self.global_shape:
+            if self.partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST] and local_shapes[self.rank] != self.global_shape:
                 raise ValueError(f"Local shape is not equal to global shape at rank = {self.rank};"
                                  f"{local_shapes[self.rank]} != {self.global_shape}")
             elif self.partition is Partition.SCATTER:
@@ -779,6 +820,12 @@ 

Source code for pylops_mpi.DistributedArray

             raise ValueError(f"Local Array Shape Mismatch - "
                              f"{self.local_shape} != {dist_array.local_shape}")
 
+    def _check_mask(self, dist_array):
+        """Check mask of the Array
+        """
+        if not np.array_equal(self.mask, dist_array.mask):
+            raise ValueError("Mask of both the arrays must be same")
+
     def _allreduce(self, send_buf, recv_buf=None, op: MPI.Op = MPI.SUM):
         """MPI Allreduce operation
         """
@@ -788,12 +835,22 @@ 

Source code for pylops_mpi.DistributedArray

         self.base_comm.Allreduce(send_buf, recv_buf, op)
         return recv_buf
 
+    def _allreduce_subcomm(self, send_buf, recv_buf=None, op: MPI.Op = MPI.SUM):
+        """MPI Allreduce operation with subcommunicator
+        """
+        if recv_buf is None:
+            return self.sub_comm.allreduce(send_buf, op)
+        # For MIN and MAX which require recv_buf
+        self.sub_comm.Allreduce(send_buf, recv_buf, op)
+        return recv_buf
+
     def __neg__(self):
         arr = DistributedArray(global_shape=self.global_shape,
                                base_comm=self.base_comm,
                                partition=self.partition,
                                axis=self.axis,
                                local_shapes=self.local_shapes,
+                               mask=self.mask,
                                engine=self.engine,
                                dtype=self.dtype)
         arr[:] = -self.local_array
@@ -821,11 +878,13 @@ 

Source code for pylops_mpi.DistributedArray

         """Distributed Addition of arrays
         """
         self._check_partition_shape(dist_array)
+        self._check_mask(dist_array)
         SumArray = DistributedArray(global_shape=self.global_shape,
                                     base_comm=self.base_comm,
                                     dtype=self.dtype,
                                     partition=self.partition,
                                     local_shapes=self.local_shapes,
+                                    mask=self.mask,
                                     engine=self.engine,
                                     axis=self.axis)
         SumArray[:] = self.local_array + dist_array.local_array
@@ -835,6 +894,7 @@ 

Source code for pylops_mpi.DistributedArray

         """Distributed In-place Addition of arrays
         """
         self._check_partition_shape(dist_array)
+        self._check_mask(dist_array)
         self[:] = self.local_array + dist_array.local_array
         return self
 
@@ -843,12 +903,14 @@ 

Source code for pylops_mpi.DistributedArray

         """
         if isinstance(dist_array, DistributedArray):
             self._check_partition_shape(dist_array)
+            self._check_mask(dist_array)
 
         ProductArray = DistributedArray(global_shape=self.global_shape,
                                         base_comm=self.base_comm,
                                         dtype=self.dtype,
                                         partition=self.partition,
                                         local_shapes=self.local_shapes,
+                                        mask=self.mask,
                                         engine=self.engine,
                                         axis=self.axis)
         if isinstance(dist_array, DistributedArray):
@@ -863,13 +925,15 @@ 

Source code for pylops_mpi.DistributedArray

         """Distributed Dot Product
         """
         self._check_partition_shape(dist_array)
+        self._check_mask(dist_array)
+
         # Convert to Partition.SCATTER if Partition.BROADCAST
         x = DistributedArray.to_dist(x=self.local_array) \
-            if self.partition is Partition.BROADCAST else self
+            if self.partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST] else self
         y = DistributedArray.to_dist(x=dist_array.local_array) \
-            if self.partition is Partition.BROADCAST else dist_array
+            if self.partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST] else dist_array
         # Flatten the local arrays and calculate dot product
-        return self._allreduce(np.dot(x.local_array.flatten(), y.local_array.flatten()))
+        return self._allreduce_subcomm(np.dot(x.local_array.flatten(), y.local_array.flatten()))
 
     def _compute_vector_norm(self, local_array: NDArray,
                              axis: int, ord: Optional[int] = None):
@@ -896,23 +960,36 @@ 

Source code for pylops_mpi.DistributedArray

             raise ValueError(f"norm-{ord} not possible for vectors")
         elif ord == 0:
             # Count non-zero then sum reduction
-            recv_buf = self._allreduce(np.count_nonzero(local_array, axis=axis).astype(np.float64))
+            recv_buf = self._allreduce_subcomm(np.count_nonzero(local_array, axis=axis).astype(np.float64))
         elif ord == np.inf:
             # Calculate max followed by max reduction
-            recv_buf = self._allreduce(np.max(np.abs(local_array), axis=axis).astype(np.float64),
-                                       recv_buf, op=MPI.MAX)
+            recv_buf = self._allreduce_subcomm(np.max(np.abs(local_array), axis=axis).astype(np.float64),
+                                               recv_buf, op=MPI.MAX)
             recv_buf = np.squeeze(recv_buf, axis=axis)
         elif ord == -np.inf:
             # Calculate min followed by min reduction
-            recv_buf = self._allreduce(np.min(np.abs(local_array), axis=axis).astype(np.float64),
-                                       recv_buf, op=MPI.MIN)
+            recv_buf = self._allreduce_subcomm(np.min(np.abs(local_array), axis=axis).astype(np.float64),
+                                               recv_buf, op=MPI.MIN)
             recv_buf = np.squeeze(recv_buf, axis=axis)
 
         else:
-            recv_buf = self._allreduce(np.sum(np.abs(np.float_power(local_array, ord)), axis=axis))
+            recv_buf = self._allreduce_subcomm(np.sum(np.abs(np.float_power(local_array, ord)), axis=axis))
             recv_buf = np.power(recv_buf, 1. / ord)
         return recv_buf
 
+    def zeros_like(self):
+        """Creates a copy of the DistributedArray filled with zeros
+        """
+        arr = DistributedArray(global_shape=self.global_shape,
+                               base_comm=self.base_comm,
+                               partition=self.partition,
+                               axis=self.axis,
+                               local_shapes=self.local_shapes,
+                               engine=self.engine,
+                               dtype=self.dtype)
+        arr[:] = 0.
+        return arr
+
     def norm(self, ord: Optional[int] = None,
              axis: int = -1):
         """Distributed numpy.linalg.norm method
@@ -926,7 +1003,7 @@ 

Source code for pylops_mpi.DistributedArray

         """
         # Convert to Partition.SCATTER if Partition.BROADCAST
         x = DistributedArray.to_dist(x=self.local_array) \
-            if self.partition is Partition.BROADCAST else self
+            if self.partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST] else self
         if axis == -1:
             # Flatten the local arrays and calculate norm
             return x._compute_vector_norm(x.local_array.flatten(), axis=0, ord=ord)
@@ -943,6 +1020,7 @@ 

Source code for pylops_mpi.DistributedArray

                                 partition=self.partition,
                                 axis=self.axis,
                                 local_shapes=self.local_shapes,
+                                mask=self.mask,
                                 engine=self.engine,
                                 dtype=self.dtype)
         conj[:] = self.local_array.conj()
@@ -956,6 +1034,7 @@ 

Source code for pylops_mpi.DistributedArray

                                partition=self.partition,
                                axis=self.axis,
                                local_shapes=self.local_shapes,
+                               mask=self.mask,
                                engine=self.engine,
                                dtype=self.dtype)
         arr[:] = self.local_array
@@ -978,6 +1057,7 @@ 

Source code for pylops_mpi.DistributedArray

         local_shapes = [(np.prod(local_shape, axis=-1), ) for local_shape in self.local_shapes]
         arr = DistributedArray(global_shape=np.prod(self.global_shape),
                                local_shapes=local_shapes,
+                               mask=self.mask,
                                partition=self.partition,
                                engine=self.engine,
                                dtype=self.dtype)
diff --git a/_modules/pylops_mpi/basicoperators/BlockDiag.html b/_modules/pylops_mpi/basicoperators/BlockDiag.html
index 62c3dfc4..1e339438 100644
--- a/_modules/pylops_mpi/basicoperators/BlockDiag.html
+++ b/_modules/pylops_mpi/basicoperators/BlockDiag.html
@@ -433,7 +433,8 @@ 

Source code for pylops_mpi.basicoperators.BlockDiag

import numpy as np from scipy.sparse.linalg._interface import _get_dtype from mpi4py import MPI -from typing import Optional, Sequence +from typing import Optional, Sequence, List +from numbers import Integral from pylops import LinearOperator from pylops.utils import DTypeLike @@ -462,6 +463,9 @@

Source code for pylops_mpi.basicoperators.BlockDiag

One or more :class:`pylops.LinearOperator` to be stacked. base_comm : :obj:`mpi4py.MPI.Comm`, optional Base MPI Communicator. Defaults to ``mpi4py.MPI.COMM_WORLD``. + mask : :obj:`list`, optional + Mask defining subsets of ranks to consider when performing 'global' operations on + the distributed array such as dot product or norm. dtype : :obj:`str`, optional Type of elements in input array. @@ -529,8 +533,10 @@

Source code for pylops_mpi.basicoperators.BlockDiag

def __init__(self, ops: Sequence[LinearOperator], base_comm: MPI.Comm = MPI.COMM_WORLD, + mask: Optional[List[Integral]] = None, dtype: Optional[DTypeLike] = None): self.ops = ops + self.mask = mask mops = np.zeros(len(self.ops), dtype=np.int64) nops = np.zeros(len(self.ops), dtype=np.int64) for iop, oper in enumerate(self.ops): @@ -550,7 +556,7 @@

Source code for pylops_mpi.basicoperators.BlockDiag

def _matvec(self, x: DistributedArray) -> DistributedArray: ncp = get_module(x.engine) y = DistributedArray(global_shape=self.shape[0], local_shapes=self.local_shapes_n, - engine=x.engine, dtype=self.dtype) + mask=self.mask, engine=x.engine, dtype=self.dtype) y1 = [] for iop, oper in enumerate(self.ops): y1.append(oper.matvec(x.local_array[self.mmops[iop]: @@ -562,7 +568,7 @@

Source code for pylops_mpi.basicoperators.BlockDiag

def _rmatvec(self, x: DistributedArray) -> DistributedArray: ncp = get_module(x.engine) y = DistributedArray(global_shape=self.shape[1], local_shapes=self.local_shapes_m, - engine=x.engine, dtype=self.dtype) + mask=self.mask, engine=x.engine, dtype=self.dtype) y1 = [] for iop, oper in enumerate(self.ops): y1.append(oper.rmatvec(x.local_array[self.nnops[iop]: diff --git a/_modules/pylops_mpi/waveeqprocessing/MDC.html b/_modules/pylops_mpi/waveeqprocessing/MDC.html index 34a4a1d5..57b25e5f 100644 --- a/_modules/pylops_mpi/waveeqprocessing/MDC.html +++ b/_modules/pylops_mpi/waveeqprocessing/MDC.html @@ -452,7 +452,7 @@

Source code for pylops_mpi.waveeqprocessing.MDC

< Used to be able to provide operators from different libraries to MDC. It operates in the same way as public method - (PoststackLinearModelling) but has additional input parameters allowing + (MPIMDC) but has additional input parameters allowing passing a different operator and additional arguments to be passed to such operator. @@ -515,8 +515,10 @@

Source code for pylops_mpi.waveeqprocessing.MDC

< base_comm: MPI.Comm = MPI.COMM_WORLD): r"""Multi-dimensional convolution. - Apply multi-dimensional convolution between two datasets. Model and data - should be provided after flattening 2- or 3-dimensional arrays of size + Apply multi-dimensional convolution between two datasets in a distributed + fashion, with ``G`` distributed over ranks across the frequency axis. + Model and data are broadcasted and should be provided after flattening + 2- or 3-dimensional arrays of size :math:`[n_t \times n_r (\times n_{vs})]` and :math:`[n_t \times n_s (\times n_{vs})]` (or :math:`2*n_t-1` for ``twosided=True``), respectively. @@ -525,7 +527,7 @@

Source code for pylops_mpi.waveeqprocessing.MDC

< ---------- G : :obj:`numpy.ndarray` Multi-dimensional convolution kernel in frequency domain of size - :math:`[n_{fmax} \times n_s \times n_r]` + :math:`[n_{f,rank} \times n_s \times n_r]` nt : :obj:`int` Number of samples along time axis for model and data (note that this must be equal to ``2*n_t-1`` when working with ``twosided=True``. diff --git a/_sources/api/generated/pylops_mpi.DistributedArray.rst.txt b/_sources/api/generated/pylops_mpi.DistributedArray.rst.txt index 79f4c779..d1ba19c3 100644 --- a/_sources/api/generated/pylops_mpi.DistributedArray.rst.txt +++ b/_sources/api/generated/pylops_mpi.DistributedArray.rst.txt @@ -21,6 +21,7 @@ ~DistributedArray.norm ~DistributedArray.ravel ~DistributedArray.to_dist + ~DistributedArray.zeros_like diff --git a/_sources/gallery/plot_cgls.rst.txt b/_sources/gallery/plot_cgls.rst.txt index adf6b41d..b8851b25 100644 --- a/_sources/gallery/plot_cgls.rst.txt +++ b/_sources/gallery/plot_cgls.rst.txt @@ -170,7 +170,7 @@ function of the DistributedArray as shown below. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.173 seconds) + **Total running time of the script:** (0 minutes 0.181 seconds) .. _sphx_glr_download_gallery_plot_cgls.py: diff --git a/_sources/gallery/plot_derivative.rst.txt b/_sources/gallery/plot_derivative.rst.txt index 3c0dedb4..ee3ca973 100644 --- a/_sources/gallery/plot_derivative.rst.txt +++ b/_sources/gallery/plot_derivative.rst.txt @@ -262,7 +262,7 @@ dimensions and concatenates them. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 1.311 seconds) + **Total running time of the script:** (0 minutes 1.360 seconds) .. _sphx_glr_download_gallery_plot_derivative.py: diff --git a/_sources/gallery/plot_distributed_array.rst.txt b/_sources/gallery/plot_distributed_array.rst.txt index bc6725a0..cdc8c1f1 100644 --- a/_sources/gallery/plot_distributed_array.rst.txt +++ b/_sources/gallery/plot_distributed_array.rst.txt @@ -24,7 +24,7 @@ This example shows how to use the :py:class:`pylops_mpi.DistributedArray`. This class provides a way to distribute arrays across multiple processes in a parallel computing environment. -.. GENERATED FROM PYTHON SOURCE LINES 8-22 +.. GENERATED FROM PYTHON SOURCE LINES 8-27 .. code-block:: Python @@ -39,6 +39,11 @@ a parallel computing environment. plt.close("all") np.random.seed(42) + # MPI parameters + size = MPI.COMM_WORLD.Get_size() # number of nodes + rank = MPI.COMM_WORLD.Get_rank() # rank of current node + + # Defining the global shape of the distributed array global_shape = (10, 5) @@ -49,13 +54,13 @@ a parallel computing environment. -.. GENERATED FROM PYTHON SOURCE LINES 23-26 +.. GENERATED FROM PYTHON SOURCE LINES 28-31 -Let's start by defining the -class with the input parameters ``global_shape``, -``partition``, and ``axis``. Here's an example implementation of the class with ``axis=0``. +Let's start by defining the class with the input parameters ``global_shape``, +``partition``, and ``axis``. Here's an example implementation of the class +with ``axis=0``. -.. GENERATED FROM PYTHON SOURCE LINES 26-34 +.. GENERATED FROM PYTHON SOURCE LINES 31-39 .. code-block:: Python @@ -79,12 +84,12 @@ class with the input parameters ``global_shape``, -.. GENERATED FROM PYTHON SOURCE LINES 35-37 +.. GENERATED FROM PYTHON SOURCE LINES 40-42 Below is an implementation to show how the global array is distributed along the second axis. -.. GENERATED FROM PYTHON SOURCE LINES 37-45 +.. GENERATED FROM PYTHON SOURCE LINES 42-50 .. code-block:: Python @@ -108,7 +113,7 @@ the second axis. -.. GENERATED FROM PYTHON SOURCE LINES 46-51 +.. GENERATED FROM PYTHON SOURCE LINES 51-56 You also have the option of directly including the ``local_shapes`` as a parameter to the :py:class:`pylops_mpi.DistributedArray`. This will enable the assignment @@ -116,7 +121,7 @@ of shapes to local arrays on each rank. However, it's essential to ensure that the number of processes matches the length of ``local_shapes``, and that the combined local shapes should align with the ``global_shape`` along the desired ``axis``. -.. GENERATED FROM PYTHON SOURCE LINES 51-59 +.. GENERATED FROM PYTHON SOURCE LINES 56-64 .. code-block:: Python @@ -140,14 +145,14 @@ combined local shapes should align with the ``global_shape`` along the desired ` -.. GENERATED FROM PYTHON SOURCE LINES 60-64 +.. GENERATED FROM PYTHON SOURCE LINES 65-69 To convert a random NumPy array into a ``pylops_mpi.DistributedArray``, you can use the ``to_dist`` classmethod. This method allows you to distribute the array across multiple processes for parallel computation. Below is an example implementation depicting the same. -.. GENERATED FROM PYTHON SOURCE LINES 64-74 +.. GENERATED FROM PYTHON SOURCE LINES 69-79 .. code-block:: Python @@ -185,12 +190,15 @@ Below is an example implementation depicting the same. -.. GENERATED FROM PYTHON SOURCE LINES 75-77 +.. GENERATED FROM PYTHON SOURCE LINES 80-85 + +Let's move now to consider various operations that one can perform on +:py:class:`pylops_mpi.DistributedArray` objects. **Scaling** - Each process operates on its local portion of the array and scales the corresponding elements by a given scalar. -.. GENERATED FROM PYTHON SOURCE LINES 77-80 +.. GENERATED FROM PYTHON SOURCE LINES 85-88 .. code-block:: Python @@ -209,12 +217,12 @@ the array and scales the corresponding elements by a given scalar. -.. GENERATED FROM PYTHON SOURCE LINES 81-83 +.. GENERATED FROM PYTHON SOURCE LINES 89-91 **Element-wise Addition** - Each process operates on its local portion of the array and adds the corresponding elements together. -.. GENERATED FROM PYTHON SOURCE LINES 83-86 +.. GENERATED FROM PYTHON SOURCE LINES 91-94 .. code-block:: Python @@ -233,13 +241,13 @@ the array and adds the corresponding elements together. -.. GENERATED FROM PYTHON SOURCE LINES 87-90 +.. GENERATED FROM PYTHON SOURCE LINES 95-98 **Element-wise In-place Addition** - Similar to the previous one but the addition is performed directly on one of the addends without creating a new distributed array. -.. GENERATED FROM PYTHON SOURCE LINES 90-93 +.. GENERATED FROM PYTHON SOURCE LINES 98-101 .. code-block:: Python @@ -258,12 +266,12 @@ distributed array. -.. GENERATED FROM PYTHON SOURCE LINES 94-96 +.. GENERATED FROM PYTHON SOURCE LINES 102-104 **Element-wise Subtraction** - Each process operates on its local portion of the array and subtracts the corresponding elements together. -.. GENERATED FROM PYTHON SOURCE LINES 96-99 +.. GENERATED FROM PYTHON SOURCE LINES 104-107 .. code-block:: Python @@ -282,12 +290,12 @@ of the array and subtracts the corresponding elements together. -.. GENERATED FROM PYTHON SOURCE LINES 100-102 +.. GENERATED FROM PYTHON SOURCE LINES 108-110 **Element-wise Multiplication** - Each process operates on its local portion of the array and multiplies the corresponding elements together. -.. GENERATED FROM PYTHON SOURCE LINES 102-104 +.. GENERATED FROM PYTHON SOURCE LINES 110-113 .. code-block:: Python @@ -296,6 +304,7 @@ of the array and multiplies the corresponding elements together. + .. image-sg:: /gallery/images/sphx_glr_plot_distributed_array_010.png :alt: Multiplication, Rank-0 :srcset: /gallery/images/sphx_glr_plot_distributed_array_010.png @@ -305,10 +314,165 @@ of the array and multiplies the corresponding elements together. +.. GENERATED FROM PYTHON SOURCE LINES 114-124 + +Finally, let's look at the case where parallelism could be applied over +multiple axes - and more specifically one belonging to the model/data and one +to the operator. This kind of "2D"-parallelism requires repeating parts of +the model/data over groups of ranks. However, when global operations such as +``dot`` or ``norm`` are applied on a ``pylops_mpi.DistributedArray`` of +this kind, we need to ensure that the repeated portions to do all contribute +to the computation. This can be achieved via the ``mask`` input parameter: +a list of size equal to the number of ranks, whose elements contain the index +of the subgroup/subcommunicator (with partial arrays in different groups +are identical to each other). + +.. GENERATED FROM PYTHON SOURCE LINES 124-156 + +.. code-block:: Python + + + # Defining the local and global shape of the distributed array + local_shape = 5 + global_shape = local_shape * size + + # Create mask + nsub = 2 + subsize = max(1, size // nsub) + mask = np.repeat(np.arange(size // subsize), subsize) + if rank == 0: + print("1D masked arrays") + print(f"Mask: {mask}") + + # Create and fill the distributed array + x = pylops_mpi.DistributedArray(global_shape=global_shape, + partition=Partition.SCATTER, + mask=mask) + x[:] = (MPI.COMM_WORLD.Get_rank() % subsize + 1.) * np.ones(local_shape) + xloc = x.asarray() + + # Dot product + dot = x.dot(x) + dotloc = np.dot(xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)], + xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)]) + print(f"Dot check (Rank {rank}): {np.allclose(dot, dotloc)}") + + # Norm + norm = x.norm(ord=2) + normloc = np.linalg.norm(xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)], + ord=2) + print(f"Norm check (Rank {rank}): {np.allclose(norm, normloc)}") + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + 1D masked arrays + Mask: [0] + Dot check (Rank 0): True + Norm check (Rank 0): True + + + + +.. GENERATED FROM PYTHON SOURCE LINES 157-158 + +And with 2d-arrays distributed over axis=1 + +.. GENERATED FROM PYTHON SOURCE LINES 158-181 + +.. code-block:: Python + + extra_dim_shape = 2 + if rank == 0: + print("2D masked arrays (over axis=1)") + + # Create and fill the distributed array + x = pylops_mpi.DistributedArray(global_shape=(extra_dim_shape, global_shape), + partition=Partition.SCATTER, + axis=1, mask=mask) + x[:] = (MPI.COMM_WORLD.Get_rank() % subsize + 1.) * np.ones((extra_dim_shape, local_shape)) + xloc = x.asarray() + + # Dot product + dot = x.dot(x) + dotloc = np.dot(xloc[:, local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)].ravel(), + xloc[:, local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)].ravel()) + print(f"Dot check (Rank {rank}): {np.allclose(dot, dotloc)}") + + # Norm + norm = x.norm(ord=2, axis=1) + normloc = np.linalg.norm(xloc[:, local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)], + ord=2, axis=1) + print(f"Norm check (Rank {rank}): {np.allclose(norm, normloc)}") + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + 2D masked arrays (over axis=1) + Dot check (Rank 0): True + Norm check (Rank 0): True + + + + +.. GENERATED FROM PYTHON SOURCE LINES 182-183 + +And finally with 2d-arrays distributed over axis=0 + +.. GENERATED FROM PYTHON SOURCE LINES 183-204 + +.. code-block:: Python + + if rank == 0: + print("2D masked arrays (over axis=0)") + + # Create and fill the distributed array + x = pylops_mpi.DistributedArray(global_shape=(global_shape, extra_dim_shape), + partition=Partition.SCATTER, + axis=0, mask=mask) + x[:] = (MPI.COMM_WORLD.Get_rank() % subsize + 1.) * np.ones((local_shape, extra_dim_shape)) + xloc = x.asarray() + + # Dot product + dot = x.dot(x) + dotloc = np.dot(xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)].ravel(), + xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)].ravel()) + print(f"Dot check (Rank {rank}): {np.allclose(dot, dotloc)}") + + # Norm + norm = x.norm(ord=2, axis=0) + normloc = np.linalg.norm(xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)], + ord=2, axis=0) + print(f"Norm check (Rank {rank}): {np.allclose(norm, normloc)}") + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + 2D masked arrays (over axis=0) + Dot check (Rank 0): True + Norm check (Rank 0): True + + + + .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 1.494 seconds) + **Total running time of the script:** (0 minutes 1.537 seconds) .. _sphx_glr_download_gallery_plot_distributed_array.py: diff --git a/_sources/gallery/plot_mpilinop.rst.txt b/_sources/gallery/plot_mpilinop.rst.txt index 9f697c3e..0bf561f5 100644 --- a/_sources/gallery/plot_mpilinop.rst.txt +++ b/_sources/gallery/plot_mpilinop.rst.txt @@ -214,7 +214,7 @@ to ensure that the operators align appropriately with their corresponding .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.344 seconds) + **Total running time of the script:** (0 minutes 0.357 seconds) .. _sphx_glr_download_gallery_plot_mpilinop.py: diff --git a/_sources/gallery/plot_stacked_array.rst.txt b/_sources/gallery/plot_stacked_array.rst.txt index a2f1ead5..60fe6bd9 100644 --- a/_sources/gallery/plot_stacked_array.rst.txt +++ b/_sources/gallery/plot_stacked_array.rst.txt @@ -403,7 +403,7 @@ of distributed arrays .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.016 seconds) + **Total running time of the script:** (0 minutes 0.017 seconds) .. _sphx_glr_download_gallery_plot_stacked_array.py: diff --git a/_sources/gallery/plot_stacking.rst.txt b/_sources/gallery/plot_stacking.rst.txt index 851360aa..c096a919 100644 --- a/_sources/gallery/plot_stacking.rst.txt +++ b/_sources/gallery/plot_stacking.rst.txt @@ -279,7 +279,7 @@ corresponding vector :math:`x`. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.429 seconds) + **Total running time of the script:** (0 minutes 0.456 seconds) .. _sphx_glr_download_gallery_plot_stacking.py: diff --git a/_sources/gallery/sg_execution_times.rst.txt b/_sources/gallery/sg_execution_times.rst.txt index 3e7cb805..d11d12b6 100644 --- a/_sources/gallery/sg_execution_times.rst.txt +++ b/_sources/gallery/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:04.249** total execution time for 7 files **from gallery**: +**00:04.391** total execution time for 7 files **from gallery**: .. container:: @@ -33,23 +33,23 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_gallery_plot_distributed_array.py` (``plot_distributed_array.py``) - - 00:01.494 + - 00:01.537 - 0.0 * - :ref:`sphx_glr_gallery_plot_derivative.py` (``plot_derivative.py``) - - 00:01.311 + - 00:01.360 - 0.0 * - :ref:`sphx_glr_gallery_plot_mdc.py` (``plot_mdc.py``) - 00:00.482 - 0.0 * - :ref:`sphx_glr_gallery_plot_stacking.py` (``plot_stacking.py``) - - 00:00.429 + - 00:00.456 - 0.0 * - :ref:`sphx_glr_gallery_plot_mpilinop.py` (``plot_mpilinop.py``) - - 00:00.344 + - 00:00.357 - 0.0 * - :ref:`sphx_glr_gallery_plot_cgls.py` (``plot_cgls.py``) - - 00:00.173 + - 00:00.181 - 0.0 * - :ref:`sphx_glr_gallery_plot_stacked_array.py` (``plot_stacked_array.py``) - - 00:00.016 + - 00:00.017 - 0.0 diff --git a/_sources/sg_execution_times.rst.txt b/_sources/sg_execution_times.rst.txt index 29b79164..61d831b5 100644 --- a/_sources/sg_execution_times.rst.txt +++ b/_sources/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**02:27.526** total execution time for 10 files **from all galleries**: +**02:28.821** total execution time for 10 files **from all galleries**: .. container:: @@ -33,32 +33,32 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_poststack.py` (``../../tutorials/poststack.py``) - - 02:18.159 + - 02:19.126 - 0.0 * - :ref:`sphx_glr_tutorials_lsm.py` (``../../tutorials/lsm.py``) - - 00:03.108 + - 00:03.245 - 0.0 * - :ref:`sphx_glr_tutorials_mdd.py` (``../../tutorials/mdd.py``) - - 00:02.010 + - 00:02.058 - 0.0 * - :ref:`sphx_glr_gallery_plot_distributed_array.py` (``../../examples/plot_distributed_array.py``) - - 00:01.494 + - 00:01.537 - 0.0 * - :ref:`sphx_glr_gallery_plot_derivative.py` (``../../examples/plot_derivative.py``) - - 00:01.311 + - 00:01.360 - 0.0 * - :ref:`sphx_glr_gallery_plot_mdc.py` (``../../examples/plot_mdc.py``) - 00:00.482 - 0.0 * - :ref:`sphx_glr_gallery_plot_stacking.py` (``../../examples/plot_stacking.py``) - - 00:00.429 + - 00:00.456 - 0.0 * - :ref:`sphx_glr_gallery_plot_mpilinop.py` (``../../examples/plot_mpilinop.py``) - - 00:00.344 + - 00:00.357 - 0.0 * - :ref:`sphx_glr_gallery_plot_cgls.py` (``../../examples/plot_cgls.py``) - - 00:00.173 + - 00:00.181 - 0.0 * - :ref:`sphx_glr_gallery_plot_stacked_array.py` (``../../examples/plot_stacked_array.py``) - - 00:00.016 + - 00:00.017 - 0.0 diff --git a/_sources/tutorials/lsm.rst.txt b/_sources/tutorials/lsm.rst.txt index d6597ea7..7865d109 100644 --- a/_sources/tutorials/lsm.rst.txt +++ b/_sources/tutorials/lsm.rst.txt @@ -304,7 +304,7 @@ solver. 99 -1.2546e-02 2.8528e+00 2.8528e+00 100 -1.2389e-02 2.8342e+00 2.8342e+00 - Iterations = 100 Total time (s) = 1.17 + Iterations = 100 Total time (s) = 1.24 ----------------------------------------------------------------- @@ -385,7 +385,7 @@ solver. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.108 seconds) + **Total running time of the script:** (0 minutes 3.245 seconds) .. _sphx_glr_download_tutorials_lsm.py: diff --git a/_sources/tutorials/mdd.rst.txt b/_sources/tutorials/mdd.rst.txt index e5ee3454..81196026 100644 --- a/_sources/tutorials/mdd.rst.txt +++ b/_sources/tutorials/mdd.rst.txt @@ -367,7 +367,7 @@ back for our input model 49 0.00e+00+0.00e+00j 3.7578e-01 3.7578e-01 50 0.00e+00+0.00e+00j 3.6788e-01 3.6788e-01 - Iterations = 50 Total time (s) = 1.15 + Iterations = 50 Total time (s) = 1.18 ----------------------------------------------------------------- @@ -377,7 +377,7 @@ back for our input model .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.010 seconds) + **Total running time of the script:** (0 minutes 2.058 seconds) .. _sphx_glr_download_tutorials_mdd.py: diff --git a/_sources/tutorials/poststack.rst.txt b/_sources/tutorials/poststack.rst.txt index aac91446..4c0b41eb 100644 --- a/_sources/tutorials/poststack.rst.txt +++ b/_sources/tutorials/poststack.rst.txt @@ -284,7 +284,7 @@ normal equations, and :math:`\mathbf{d}^{Norm}` is the data of the normal equati 99 6.1544e-01 6.5026e-01 6.5026e-01 100 6.1561e-01 6.4212e-01 6.4212e-01 - Iterations = 100 Total time (s) = 29.21 + Iterations = 100 Total time (s) = 29.55 ----------------------------------------------------------------- @@ -349,7 +349,7 @@ normal equations, and :math:`\mathbf{d}^{Norm}` is the data of the normal equati 99 6.3033e-01 1.9903e+02 100 6.3026e-01 1.9777e+02 - Iterations = 100 Total time (s) = 44.36 + Iterations = 100 Total time (s) = 44.68 ------------------------------------------------------- @@ -481,7 +481,7 @@ Finally, we display the modeling and inversion results .. rst-class:: sphx-glr-timing - **Total running time of the script:** (2 minutes 18.159 seconds) + **Total running time of the script:** (2 minutes 19.126 seconds) .. _sphx_glr_download_tutorials_poststack.py: diff --git a/_sources/tutorials/sg_execution_times.rst.txt b/_sources/tutorials/sg_execution_times.rst.txt index 9961055b..dbb95e5d 100644 --- a/_sources/tutorials/sg_execution_times.rst.txt +++ b/_sources/tutorials/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**02:23.277** total execution time for 3 files **from tutorials**: +**02:24.429** total execution time for 3 files **from tutorials**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_poststack.py` (``poststack.py``) - - 02:18.159 + - 02:19.126 - 0.0 * - :ref:`sphx_glr_tutorials_lsm.py` (``lsm.py``) - - 00:03.108 + - 00:03.245 - 0.0 * - :ref:`sphx_glr_tutorials_mdd.py` (``mdd.py``) - - 00:02.010 + - 00:02.058 - 0.0 diff --git a/api/generated/pylops_mpi.DistributedArray.html b/api/generated/pylops_mpi.DistributedArray.html index 798a97ea..cab13871 100644 --- a/api/generated/pylops_mpi.DistributedArray.html +++ b/api/generated/pylops_mpi.DistributedArray.html @@ -490,7 +490,7 @@

pylops_mpi.DistributedArray#

-class pylops_mpi.DistributedArray(global_shape, base_comm=<mpi4py.MPI.Intracomm object>, partition=Partition.SCATTER, axis=0, local_shapes=None, engine='numpy', dtype=<class 'numpy.float64'>)[source]#
+class pylops_mpi.DistributedArray(global_shape, base_comm=<mpi4py.MPI.Intracomm object>, partition=Partition.SCATTER, axis=0, local_shapes=None, mask=None, engine='numpy', dtype=<class 'numpy.float64'>)[source]#

Distributed Numpy Arrays

Multidimensional NumPy-like distributed arrays. It brings NumPy arrays to high-performance computing.

@@ -499,10 +499,13 @@

pylops_mpi.DistributedArraypylops_mpi.Partition.BROADCAST, it is crucial to be aware that any attempts to make arrays different from rank to rank will be -overwritten by the actions of rank 0. This means that if you modify -the DistributedArray on a specific rank, and are using broadcast to -synchronize the arrays across all ranks, the modifications made by other -ranks will be discarded and overwritten with the value at rank 0.

+overwritten by the actions of rank 0. This is accomplished internally +by broadcasting the content of rank 0 every time a modification of +the array is attempted. Such a behaviour does however incur a cost +as communication may be not needed if the user ensures not to modify +the content of the array in different ranks in a different way. To +avoid broadcasting, one can use pylops_mpi.Partition.UNSAFE_BROADCAST +instead.

Parameters:
@@ -512,12 +515,15 @@

pylops_mpi.DistributedArraympi4py.MPI.Comm, optional

MPI Communicator over which array is distributed. Defaults to mpi4py.MPI.COMM_WORLD.

-
partitionPartition, optional

Broadcast or Scatter the array. Defaults to Partition.SCATTER.

+
partitionPartition, optional

Broadcast, UnsafeBroadcast, or Scatter the array. Defaults to Partition.SCATTER.

axisint, optional

Axis along which distribution occurs. Defaults to 0.

local_shapeslist, optional

List of tuples or integers representing local shapes at each rank.

+
masklist, optional

Mask defining subsets of ranks to consider when performing ‘global’ +operations on the distributed array such as dot product or norm.

+
enginestr, optional

Engine used to store array (numpy or cupy)

dtypestr, optional

Type of elements in input array. Defaults to numpy.float64.

@@ -564,6 +570,9 @@

pylops_mpi.DistributedArray

to_dist(x[, base_comm, partition, axis, ...])

Convert A Global Array to a Distributed Array

+

zeros_like()

+

Creates a copy of the DistributedArray filled with zeros

+

diff --git a/api/generated/pylops_mpi.Partition.html b/api/generated/pylops_mpi.Partition.html index 4b280567..cbe97fd3 100644 --- a/api/generated/pylops_mpi.Partition.html +++ b/api/generated/pylops_mpi.Partition.html @@ -494,7 +494,10 @@

pylops_mpi.Partition

Enum class

Distributing data among different processes.

    -
  • BROADCAST: Distributes data to all processes.

  • +
  • BROADCAST: Distributes data to all processes +(ensuring that data is kept consistent across processes)

  • +
  • UNSAFE_BROADCAST: Distributes data to all processes +(without ensuring that data is kept consistent across processes)

  • SCATTER: Distributes unique portions to each process.

Methods

diff --git a/api/generated/pylops_mpi.basicoperators.MPIBlockDiag.html b/api/generated/pylops_mpi.basicoperators.MPIBlockDiag.html index 5b765017..d6662eb1 100644 --- a/api/generated/pylops_mpi.basicoperators.MPIBlockDiag.html +++ b/api/generated/pylops_mpi.basicoperators.MPIBlockDiag.html @@ -492,7 +492,7 @@

pylops_mpi.basicoperators.MPIBlockDiag#

-class pylops_mpi.basicoperators.MPIBlockDiag(ops, base_comm=<mpi4py.MPI.Intracomm object>, dtype=None)[source]#
+class pylops_mpi.basicoperators.MPIBlockDiag(ops, base_comm=<mpi4py.MPI.Intracomm object>, mask=None, dtype=None)[source]#

MPI Block-diagonal operator.

Create a block-diagonal operator from a set of linear operators using MPI. Each rank must initialize this operator by providing one or more linear operators @@ -506,6 +506,9 @@

pylops_mpi.basicoperators.MPIBlockDiagmpi4py.MPI.Comm, optional

Base MPI Communicator. Defaults to mpi4py.MPI.COMM_WORLD.

+
masklist, optional

Mask defining subsets of ranks to consider when performing ‘global’ operations on +the distributed array such as dot product or norm.

+
dtypestr, optional

Type of elements in input array.

@@ -569,7 +572,7 @@

pylops_mpi.basicoperators.MPIBlockDiagMethods

- + diff --git a/api/generated/pylops_mpi.waveeqprocessing.MPIMDC.html b/api/generated/pylops_mpi.waveeqprocessing.MPIMDC.html index 2ffb7c22..f2800746 100644 --- a/api/generated/pylops_mpi.waveeqprocessing.MPIMDC.html +++ b/api/generated/pylops_mpi.waveeqprocessing.MPIMDC.html @@ -494,8 +494,10 @@

pylops_mpi.waveeqprocessing.MPIMDC pylops_mpi.waveeqprocessing.MPIMDC(G, nt, nv, nfreq, dt=1.0, dr=1.0, twosided=True, fftengine='numpy', saveGt=True, conj=False, usematmul=False, prescaled=False, base_comm=<mpi4py.MPI.Intracomm object>)[source]#

Multi-dimensional convolution.

-

Apply multi-dimensional convolution between two datasets. Model and data -should be provided after flattening 2- or 3-dimensional arrays of size +

Apply multi-dimensional convolution between two datasets in a distributed +fashion, with G distributed over ranks across the frequency axis. +Model and data are broadcasted and should be provided after flattening +2- or 3-dimensional arrays of size \([n_t \times n_r (\times n_{vs})]\) and \([n_t \times n_s (\times n_{vs})]\) (or \(2*n_t-1\) for twosided=True), respectively.

@@ -503,7 +505,7 @@

pylops_mpi.waveeqprocessing.MPIMDCParameters:
Gnumpy.ndarray

Multi-dimensional convolution kernel in frequency domain of size -\([n_{fmax} \times n_s \times n_r]\)

+\([n_{f,rank} \times n_s \times n_r]\)

ntint

Number of samples along time axis for model and data (note that this must be equal to 2*n_t-1 when working with twosided=True.

diff --git a/api/index.html b/api/index.html index ec3fb8dd..22379b5a 100644 --- a/api/index.html +++ b/api/index.html @@ -529,7 +529,7 @@

Templates#

__init__(ops[, base_comm, dtype])

__init__(ops[, base_comm, mask, dtype])

adjoint()

- + diff --git a/gallery/plot_cgls.html b/gallery/plot_cgls.html index a7100ebf..2bc34235 100644 --- a/gallery/plot_cgls.html +++ b/gallery/plot_cgls.html @@ -544,7 +544,7 @@ 0.99999999 1. 0.99999996] -

Total running time of the script: (0 minutes 0.173 seconds)

+

Total running time of the script: (0 minutes 0.181 seconds)

-Gradient, x, y - 1st direction, xadj, y - 2nd direction

Total running time of the script: (0 minutes 1.311 seconds)

+Gradient, x, y - 1st direction, xadj, y - 2nd direction

Total running time of the script: (0 minutes 1.360 seconds)

-

Let’s start by defining the -class with the input parameters global_shape, -partition, and axis. Here’s an example implementation of the class with axis=0.

-
arr = pylops_mpi.DistributedArray(global_shape=global_shape,
+

Let’s start by defining the class with the input parameters global_shape, +partition, and axis. Here’s an example implementation of the class +with axis=0.

+
arr = pylops_mpi.DistributedArray(global_shape=global_shape,
                                   partition=pylops_mpi.Partition.SCATTER,
                                   axis=0)
 # Filling the local arrays
-arr[:] = np.arange(arr.local_shape[0] * arr.local_shape[1] * arr.rank,
-                   arr.local_shape[0] * arr.local_shape[1] * (arr.rank + 1)).reshape(arr.local_shape)
+arr[:] = np.arange(arr.local_shape[0] * arr.local_shape[1] * arr.rank,
+                   arr.local_shape[0] * arr.local_shape[1] * (arr.rank + 1)).reshape(arr.local_shape)
 pylops_mpi.plot_distributed_array(arr)
 
Original Array, Distributed over axis 0

Below is an implementation to show how the global array is distributed along the second axis.

-
arr = pylops_mpi.DistributedArray(global_shape=global_shape,
+
arr = pylops_mpi.DistributedArray(global_shape=global_shape,
                                   partition=pylops_mpi.Partition.SCATTER,
                                   axis=1)
 # Filling the local arrays
-arr[:] = np.arange(arr.local_shape[0] * arr.local_shape[1] * arr.rank,
-                   arr.local_shape[0] * arr.local_shape[1] * (arr.rank + 1)).reshape(arr.local_shape)
+arr[:] = np.arange(arr.local_shape[0] * arr.local_shape[1] * arr.rank,
+                   arr.local_shape[0] * arr.local_shape[1] * (arr.rank + 1)).reshape(arr.local_shape)
 pylops_mpi.plot_distributed_array(arr)
 
@@ -500,12 +505,12 @@ of shapes to local arrays on each rank. However, it’s essential to ensure that the number of processes matches the length of local_shapes, and that the combined local shapes should align with the global_shape along the desired axis.

-
local_shape = local_split(global_shape, MPI.COMM_WORLD, Partition.SCATTER, 0)
+
local_shape = local_split(global_shape, MPI.COMM_WORLD, Partition.SCATTER, 0)
 # Assigning local_shapes(List of tuples)
-local_shapes = MPI.COMM_WORLD.allgather(local_shape)
-arr = pylops_mpi.DistributedArray(global_shape=global_shape, local_shapes=local_shapes, axis=0)
-arr[:] = np.arange(arr.local_shape[0] * arr.local_shape[1] * arr.rank,
-                   arr.local_shape[0] * arr.local_shape[1] * (arr.rank + 1)).reshape(arr.local_shape)
+local_shapes = MPI.COMM_WORLD.allgather(local_shape)
+arr = pylops_mpi.DistributedArray(global_shape=global_shape, local_shapes=local_shapes, axis=0)
+arr[:] = np.arange(arr.local_shape[0] * arr.local_shape[1] * arr.rank,
+                   arr.local_shape[0] * arr.local_shape[1] * (arr.rank + 1)).reshape(arr.local_shape)
 pylops_mpi.plot_distributed_array(arr)
 
@@ -513,12 +518,12 @@ you can use the to_dist classmethod. This method allows you to distribute the array across multiple processes for parallel computation. Below is an example implementation depicting the same.

-
n = global_shape[0] * global_shape[1]
+
n = global_shape[0] * global_shape[1]
 # Array to be distributed
 array = np.arange(n) / float(n)
-arr1 = pylops_mpi.DistributedArray.to_dist(x=array.reshape(global_shape), axis=1)
+arr1 = pylops_mpi.DistributedArray.to_dist(x=array.reshape(global_shape), axis=1)
 array = array / 2.0
-arr2 = pylops_mpi.DistributedArray.to_dist(x=array.reshape(global_shape), axis=1)
+arr2 = pylops_mpi.DistributedArray.to_dist(x=array.reshape(global_shape), axis=1)
 # plot local arrays
 pylops_mpi.plot_local_arrays(arr1, "Distributed Array - 1", vmin=0, vmax=1)
 pylops_mpi.plot_local_arrays(arr2, "Distributed Array - 2", vmin=0, vmax=1)
@@ -528,6 +533,8 @@
 
  • Distributed Array - 1, Rank-0
  • Distributed Array - 2, Rank-0
  • +

    Let’s move now to consider various operations that one can perform on +pylops_mpi.DistributedArray objects.

    Scaling - Each process operates on its local portion of the array and scales the corresponding elements by a given scalar.

    scale_arr = .5 * arr1
    @@ -559,7 +566,114 @@
     pylops_mpi.plot_local_arrays(mult_arr, "Multiplication", vmin=0, vmax=1)
     
    -Multiplication, Rank-0

    Total running time of the script: (0 minutes 1.494 seconds)

    +Multiplication, Rank-0

    Finally, let’s look at the case where parallelism could be applied over +multiple axes - and more specifically one belonging to the model/data and one +to the operator. This kind of “2D”-parallelism requires repeating parts of +the model/data over groups of ranks. However, when global operations such as +dot or norm are applied on a pylops_mpi.DistributedArray of +this kind, we need to ensure that the repeated portions to do all contribute +to the computation. This can be achieved via the mask input parameter: +a list of size equal to the number of ranks, whose elements contain the index +of the subgroup/subcommunicator (with partial arrays in different groups +are identical to each other).

    +
    # Defining the local and global shape of the distributed array
    +local_shape = 5
    +global_shape = local_shape * size
    +
    +# Create mask
    +nsub = 2
    +subsize = max(1, size // nsub)
    +mask = np.repeat(np.arange(size // subsize), subsize)
    +if rank == 0:
    +    print("1D masked arrays")
    +    print(f"Mask: {mask}")
    +
    +# Create and fill the distributed array
    +x = pylops_mpi.DistributedArray(global_shape=global_shape,
    +                                partition=Partition.SCATTER,
    +                                mask=mask)
    +x[:] = (MPI.COMM_WORLD.Get_rank() % subsize + 1.) * np.ones(local_shape)
    +xloc = x.asarray()
    +
    +# Dot product
    +dot = x.dot(x)
    +dotloc = np.dot(xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)],
    +                xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)])
    +print(f"Dot check (Rank {rank}): {np.allclose(dot, dotloc)}")
    +
    +# Norm
    +norm = x.norm(ord=2)
    +normloc = np.linalg.norm(xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)],
    +                         ord=2)
    +print(f"Norm check (Rank {rank}): {np.allclose(norm, normloc)}")
    +
    +
    +
    1D masked arrays
    +Mask: [0]
    +Dot check (Rank 0): True
    +Norm check (Rank 0): True
    +
    +
    +

    And with 2d-arrays distributed over axis=1

    +
    extra_dim_shape = 2
    +if rank == 0:
    +    print("2D masked arrays (over axis=1)")
    +
    +# Create and fill the distributed array
    +x = pylops_mpi.DistributedArray(global_shape=(extra_dim_shape, global_shape),
    +                                partition=Partition.SCATTER,
    +                                axis=1, mask=mask)
    +x[:] = (MPI.COMM_WORLD.Get_rank() % subsize + 1.) * np.ones((extra_dim_shape, local_shape))
    +xloc = x.asarray()
    +
    +# Dot product
    +dot = x.dot(x)
    +dotloc = np.dot(xloc[:, local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)].ravel(),
    +                xloc[:, local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)].ravel())
    +print(f"Dot check (Rank {rank}): {np.allclose(dot, dotloc)}")
    +
    +# Norm
    +norm = x.norm(ord=2, axis=1)
    +normloc = np.linalg.norm(xloc[:, local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)],
    +                         ord=2, axis=1)
    +print(f"Norm check (Rank {rank}): {np.allclose(norm, normloc)}")
    +
    +
    +
    2D masked arrays (over axis=1)
    +Dot check (Rank 0): True
    +Norm check (Rank 0): True
    +
    +
    +

    And finally with 2d-arrays distributed over axis=0

    +
    if rank == 0:
    +    print("2D masked arrays (over axis=0)")
    +
    +# Create and fill the distributed array
    +x = pylops_mpi.DistributedArray(global_shape=(global_shape, extra_dim_shape),
    +                                partition=Partition.SCATTER,
    +                                axis=0, mask=mask)
    +x[:] = (MPI.COMM_WORLD.Get_rank() % subsize + 1.) * np.ones((local_shape, extra_dim_shape))
    +xloc = x.asarray()
    +
    +# Dot product
    +dot = x.dot(x)
    +dotloc = np.dot(xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)].ravel(),
    +                xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)].ravel())
    +print(f"Dot check (Rank {rank}): {np.allclose(dot, dotloc)}")
    +
    +# Norm
    +norm = x.norm(ord=2, axis=0)
    +normloc = np.linalg.norm(xloc[local_shape * subsize * (rank // subsize):local_shape * subsize * (rank // subsize + 1)],
    +                         ord=2, axis=0)
    +print(f"Norm check (Rank {rank}): {np.allclose(norm, normloc)}")
    +
    +
    +
    2D masked arrays (over axis=0)
    +Dot check (Rank 0): True
    +Norm check (Rank 0): True
    +
    +
    +

    Total running time of the script: (0 minutes 1.537 seconds)

    -Adjoint, $x$, $y$

    Total running time of the script: (0 minutes 0.344 seconds)

    +Adjoint, $x$, $y$

    Total running time of the script: (0 minutes 0.357 seconds)

    -Block Diagonal, $x$, $y$

    Total running time of the script: (0 minutes 0.429 seconds)

    +Block Diagonal, $x$, $y$

    Total running time of the script: (0 minutes 0.456 seconds)

    MPIBlockDiag(ops[, base_comm, dtype])

    MPIBlockDiag(ops[, base_comm, mask, dtype])

    MPI Block-diagonal operator.

    MPIStackedBlockDiag(ops[, base_comm, dtype])