extract TiledFactorization module

Project.toml

@@ -8,13 +8,9 @@ DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 GraphViz = "f526b714-d49f-11e8-06ff-31ed36ee7ee0"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
-LoopVectorization = "bdcacae8-1622-11e9-2a5c-532679323890"
-Octavian = "6fd5a793-0b7e-452c-907f-f8bfe9c57db4"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
-RecursiveFactorization = "f2c3362d-daeb-58d1-803e-2bc74f2840b4"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 ThreadPools = "b189fb0b-2eb5-4ed4-bc0c-d34c51242431"
-TriangularSolve = "d5829a12-d9aa-46ab-831f-fb7c9ab06edf"
 
 [compat]
 julia = "^1"

docs/src/references.md

@@ -1,6 +1,5 @@
 # [References](@id references-section)
 
 ```@autodocs
-Modules =   [DataFlowTasks,
-            DataFlowTasks.TiledFactorization]
+Modules =   [DataFlowTasks]
 ```

src/DataFlowTasks.jl

@@ -43,9 +43,6 @@ export
     @dasync,
     @dspawn
 
-# a module showcasing some usecases. To be removed eventually...
-include("TiledFactorization/TiledFactorization.jl")
-
 function __init__()
     # default scheduler
     capacity  = 50

test/Project.toml

@@ -1,6 +1,4 @@
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-LoopVectorization = "bdcacae8-1622-11e9-2a5c-532679323890"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-TriangularSolve = "d5829a12-d9aa-46ab-831f-fb7c9ab06edf"

test/juliascheduler_test.jl

@@ -2,7 +2,6 @@ using Test
 using DataFlowTasks
 using DataFlowTasks: R,W,RW
 using LinearAlgebra
-using DataFlowTasks.TiledFactorization
 
 include(joinpath(DataFlowTasks.PROJECT_ROOT,"test","testutils.jl"))
 
@@ -39,22 +38,4 @@ DataFlowTasks.setscheduler!(sch)
         @test abs(t1-t2) < 1e-2
     end
 
-    @testset "Tiled cholesky factorization" begin
-        m  = 100
-        bsize = div(m,5)
-        # create an SPD matrix
-        A = rand(m,m)
-        A = (A + adjoint(A))/2
-        A = A + m*I
-        F = TiledFactorization.cholesky(A)
-        @test F.L*F.U ≈ A
-    end
-
-    @testset "Tiled lu factorization" begin
-        m  = 100
-        bsize = div(m,5)
-        A = rand(m,m)
-        F = TiledFactorization.lu(A)
-        @test F.L*F.U ≈ A
-    end
 end

test/logging_dag_test.jl

@@ -0,0 +1,47 @@
+using Test
+using DataFlowTasks
+using DataFlowTasks: Logger, parse!, PlotFinished, PlotRunnable, TiledFactorization
+using LinearAlgebra
+using Logging
+using Plots
+
+GC.gc()
+
+sch = DataFlowTasks.JuliaScheduler(1000)
+# sch = DataFlowTasks.PriorityScheduler(200,true)
+DataFlowTasks.setscheduler!(sch)
+
+include(joinpath(DataFlowTasks.PROJECT_ROOT,"test","testutils.jl"))
+
+m = 4000
+A = rand(m,m)
+A = (A+A') + 2*size(A,1)*I
+
+TiledFactorization.TILESIZE[] = 256
+io     = open("forkjoin.log","w+")
+logger = Logger(io)
+TiledFactorization.cholesky!(copy(A)) # run once to compile
+DataFlowTasks.TASKCOUNTER[] = 0
+with_logger(logger) do
+    DataFlowTasks.reset_timer!(logger)
+    F = TiledFactorization.cholesky!(A)
+end
+parse!(logger)
+
+# plot(PlotRunnable(),logger)
+# plot(PlotFinished(),logger)
+@info DataFlowTasks.TASKCOUNTER[]
+plot(logger)
+
+# tl = logger.tasklogs
+# chol  = filter(t-> occursin("chol",t.task_label),tl)
+# schur = filter(t-> occursin("schur",t.task_label),tl)
+# ldiv = filter(t-> occursin("ldiv",t.task_label),tl)
+
+# t_chol = map(t -> (t.time_finish-t.time_start)/1e9,chol)
+# t_ldiv = map(t -> (t.time_finish-t.time_start)/1e9,ldiv)
+# t_schur = map(t -> (t.time_finish-t.time_start)/1e9,schur)
+# histogram(t_chol,label="cholesky",xlabel="time (s)",alpha=0.8)
+# histogram!(t_ldiv,label="ldiv",xlabel="time (s)",alpha=0.5)
+# histogram!(t_schur,label="schur",xlabel="time (s)",alpha=0.5)
+# plot(p1,p2,p3,layout=(1,3))