Merge pull request #116 from JordiManyer/psparse-with-local-indices

Using LocalIndices with psparse! constructors

Author: fverdugo

CHANGELOG.md

@@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added 
 
 - Added an MPI ibarrier-based (supposedly scalable) algorithm to find rcv neighbours in a sparse all-to-all communication graph given the snd neighbors. We left the previous non-scalable algorithm as default (based on gather-scatter) until we have experimental evidence on the relative performance and scalability of the former with respect to the latter and for which core ranges.
+- Added a new kwarg `discover_cols=true` to the `psparse!` constructor, which allows the user to skip column index discovery.
 
 ### Fixed
 

src/p_sparse_matrix.jl

@@ -471,23 +471,25 @@ function LinearAlgebra.mul!(c::PVector,a::PSparseMatrix,b::PVector,α::Number,β
 end
 
 """
-    psparse!([f,]I,J,V,row_partition,col_partition;discover_rows=true) -> Task
+    psparse!([f,]I,J,V,row_partition,col_partition;discover_rows=true,discover_cols=true) -> Task
 
 Crate an instance of [`PSparseMatrix`](@ref) by setting arbitrary entries
 from each of the underlying parts. It returns a task that produces the
 instance of [`PSparseMatrix`](@ref) allowing latency hiding while performing
 the communications needed in its setup.
 """
-function psparse!(f,I,J,V,row_partition,col_partition;discover_rows=true)
+function psparse!(f,I,J,V,row_partition,col_partition;discover_rows=true,discover_cols=true)
     if discover_rows
         I_owner = find_owner(row_partition,I)
         row_partition = map(union_ghost,row_partition,I,I_owner)
     end
     t = assemble_coo!(I,J,V,row_partition)
     @async begin
         wait(t)
-        J_owner = find_owner(col_partition,J)
-        col_partition = map(union_ghost,col_partition,J,J_owner)
+        if discover_cols
+            J_owner = find_owner(col_partition,J)
+            col_partition = map(union_ghost,col_partition,J,J_owner)
+        end
         map(to_local!,I,row_partition)
         map(to_local!,J,col_partition)
         matrix_partition = map(f,I,J,V,row_partition,col_partition)