NCCL support for Stacked op/array & Solver with doc and tutorial update

Makefile

@@ -55,6 +55,13 @@ doc:
 	rm -rf source/tutorials && rm -rf build &&\
 	cd .. && sphinx-build -b html docs/source docs/build
 
+doc_nccl:
+	cp tutorials_nccl/* tutorials/
+	cd docs  && rm -rf source/api/generated && rm -rf source/gallery &&\
+	rm -rf source/tutorials && rm -rf source/tutorials && rm -rf build &&\
+	cd .. && sphinx-build -b html docs/source docs/build
+	rm tutorials/*_nccl.py
+
 docupdate:
 	cd docs && make html && cd ..
 
@@ -68,3 +75,7 @@ run_examples:
 # Run tutorials using mpi
 run_tutorials:
 	sh mpi_examples.sh tutorials $(NUM_PROCESSES)
+
+# Run tutorials using nccl 
+run_tutorials_nccl:
+	sh mpi_examples.sh tutorials_nccl $(NUM_PROCESSES)

docs/source/gpu.rst

@@ -131,26 +131,30 @@ In the following, we provide a list of modules (i.e., operators and solvers) whe
    * - modules
      - NCCL supported
    * - :class:`pylops_mpi.DistributedArray`
-     - / 
+     - ✅ 
    * - :class:`pylops_mpi.basicoperators.MPIVStack`
-     - Ongoing
+     - ✅ 
+   * - :class:`pylops_mpi.basicoperators.MPIVStack`
+     - ✅ 
    * - :class:`pylops_mpi.basicoperators.MPIHStack`
-     - Ongoing
+     - ✅ 
    * - :class:`pylops_mpi.basicoperators.MPIBlockDiag`
-     - Ongoing
+     - ✅ 
    * - :class:`pylops_mpi.basicoperators.MPIGradient`
-     - Ongoing
+     - ✅ 
    * - :class:`pylops_mpi.basicoperators.MPIFirstDerivative`
-     - Ongoing
+     - ✅ 
    * - :class:`pylops_mpi.basicoperators.MPISecondDerivative`
-     - Ongoing
+     - ✅ 
    * - :class:`pylops_mpi.basicoperators.MPILaplacian`
-     - Ongoing
+     - ✅ 
    * - :class:`pylops_mpi.optimization.basic.cg`
-     - Ongoing
+     - ✅ 
    * - :class:`pylops_mpi.optimization.basic.cgls`
-     - Ongoing
+     - ✅ 
+   * - :class:`pylops_mpi.signalprocessing.Fredhoml1`
+     - Planned ⏳
    * - ISTA Solver
-     - Planned 
+     - Planned ⏳
    * - Complex Numeric Data Type for NCCL 
-     - Planned 
+     - Planned ⏳ 

pylops_mpi/DistributedArray.py

@@ -646,9 +646,9 @@ def dot(self, dist_array):
         self._check_mask(dist_array)
         ncp = get_module(self.engine)
         # Convert to Partition.SCATTER if Partition.BROADCAST
-        x = DistributedArray.to_dist(x=self.local_array) \
+        x = DistributedArray.to_dist(x=self.local_array, base_comm=self.base_comm, base_comm_nccl=self.base_comm_nccl) \
             if self.partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST] else self
-        y = DistributedArray.to_dist(x=dist_array.local_array) \
+        y = DistributedArray.to_dist(x=dist_array.local_array, base_comm=self.base_comm, base_comm_nccl=self.base_comm_nccl) \
             if self.partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST] else dist_array
         # Flatten the local arrays and calculate dot product
         return self._allreduce_subcomm(ncp.dot(x.local_array.flatten(), y.local_array.flatten()))
@@ -701,6 +701,7 @@ def zeros_like(self):
         """
         arr = DistributedArray(global_shape=self.global_shape,
                                base_comm=self.base_comm,
+                               base_comm_nccl=self.base_comm_nccl,
                                partition=self.partition,
                                axis=self.axis,
                                local_shapes=self.local_shapes,
@@ -721,7 +722,7 @@ def norm(self, ord: Optional[int] = None,
             Axis along which vector norm needs to be computed. Defaults to ``-1``
         """
         # Convert to Partition.SCATTER if Partition.BROADCAST
-        x = DistributedArray.to_dist(x=self.local_array) \
+        x = DistributedArray.to_dist(x=self.local_array, base_comm=self.base_comm, base_comm_nccl=self.base_comm_nccl) \
             if self.partition in [Partition.BROADCAST, Partition.UNSAFE_BROADCAST] else self
         if axis == -1:
             # Flatten the local arrays and calculate norm
@@ -736,6 +737,7 @@ def conj(self):
         """
         conj = DistributedArray(global_shape=self.global_shape,
                                 base_comm=self.base_comm,
+                                base_comm_nccl=self.base_comm_nccl,
                                 partition=self.partition,
                                 axis=self.axis,
                                 local_shapes=self.local_shapes,
@@ -750,6 +752,7 @@ def copy(self):
         """
         arr = DistributedArray(global_shape=self.global_shape,
                                base_comm=self.base_comm,
+                               base_comm_nccl=self.base_comm_nccl,
                                partition=self.partition,
                                axis=self.axis,
                                local_shapes=self.local_shapes,
@@ -905,7 +908,8 @@ def asarray(self):
             Global Array gathered at all ranks
 
         """
-        return np.hstack([distarr.asarray().ravel() for distarr in self.distarrays])
+        ncp = get_module(self.distarrays[0].engine)
+        return ncp.hstack([distarr.asarray().ravel() for distarr in self.distarrays])
 
     def _check_stacked_size(self, stacked_array):
         """Check that arrays have consistent size

pylops_mpi/optimization/cls_basic.py

@@ -98,7 +98,8 @@ def setup(
 
         if show and self.rank == 0:
             if isinstance(x, StackedDistributedArray):
-                self._print_setup(np.iscomplexobj([x1.local_array for x1 in x.distarrays]))
+                is_complex = any(np.iscomplexobj(x1.local_array) for x1 in x.distarrays)
+                self._print_setup(is_complex)
             else:
                 self._print_setup(np.iscomplexobj(x.local_array))
         return x
@@ -354,7 +355,8 @@ def setup(self,
         # print setup
         if show and self.rank == 0:
             if isinstance(x, StackedDistributedArray):
-                self._print_setup(np.iscomplexobj([x1.local_array for x1 in x.distarrays]))
+                is_complex = any(np.iscomplexobj(x1.local_array) for x1 in x.distarrays)
+                self._print_setup(is_complex)
             else:
                 self._print_setup(np.iscomplexobj(x.local_array))
         return x