Various version bumps (#198)

* Various version bumps * Julia 1.6-1.7, CUDA.jl 3.4 * This requires the 'CUDA.Mem.DeviceBuffer' as a parameter type to CuArray to be fully resolved
exanauts · Aug 26, 2021 · 84a7fec · 84a7fec
1 parent 5b98666
commit 84a7fec
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Showing 6 changed files with 19 additions and 21 deletions.
diff --git a/.github/workflows/action.yml b/.github/workflows/action.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       matrix:
           os: [ubuntu-20.04]
-          julia-version: ['1.6']
+          julia-version: ['1.6', '1.7.0-beta3']
           julia-arch: [x64]
 
     steps:
@@ -36,7 +36,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-20.04]
-        julia-version: ['1.6']
+        julia-version: ['1.6', '1.7.0-beta3']
         julia-arch: [x64]
 
     steps:

diff --git a/Project.toml b/Project.toml
@@ -5,7 +5,6 @@ version = "0.5.0"
 
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-ExprTools = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
@@ -19,17 +18,16 @@ SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
 TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 
 [compat]
-CUDA = "~2.6, ~3.2, ~3.3"
-ExprTools = "= 0.1.3"
+CUDA = "3.4"
 FiniteDiff = "2.7"
 ForwardDiff = "0.10"
-KernelAbstractions = "0.6, 0.7"
+KernelAbstractions = "0.7"
 Krylov = "~0.7.3"
 LightGraphs = "1.3"
 Metis = "1"
 SparseDiffTools = "1"
 TimerOutputs = "0.5"
-julia = "^1.5"
+julia = "1.7"
 
 [extras]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"

diff --git a/src/LinearSolvers/LinearSolvers.jl b/src/LinearSolvers/LinearSolvers.jl
@@ -241,7 +241,7 @@ function DQGMRES(J::AbstractSparseMatrix;
     P=BlockJacobiPreconditioner(J), memory=4, verbose=false
 )
     n, m = size(J)
-    S = isa(J, CUSPARSE.CuSparseMatrixCSR) ? CuVector{Float64} : Vector{Float64}
+    S = isa(J, CUSPARSE.CuSparseMatrixCSR) ? CuArray{Float64, 1, CUDA.Mem.DeviceBuffer} : Vector{Float64}
     solver = Krylov.DqgmresSolver(n, m, memory, S)
     return DQGMRES(solver, P, memory, verbose)
 end
@@ -274,7 +274,7 @@ function KrylovBICGSTAB(J::AbstractSparseMatrix;
     P=BlockJacobiPreconditioner(J), verbose=0, rtol=1e-10, atol=1e-10
 )
     n, m = size(J)
-    S = isa(J, CUSPARSE.CuSparseMatrixCSR) ? CuVector{Float64} : Vector{Float64}
+    S = isa(J, CUSPARSE.CuSparseMatrixCSR) ? CuArray{Float64, 1, CUDA.Mem.DeviceBuffer} : Vector{Float64}
     solver = Krylov.BicgstabSolver(n, m, S)
     return KrylovBICGSTAB(solver, P, verbose, atol, rtol)
 end

diff --git a/src/LinearSolvers/preconditioners.jl b/src/LinearSolvers/preconditioners.jl
@@ -54,13 +54,13 @@ struct BlockJacobiPreconditioner{AT,GAT,VI,GVI,MT,GMT,MI,GMI,SMT} <: AbstractPre
             SMT = SparseMatrixCSC{Float64,Int64}
         elseif isa(device, GPU)
             AT  = Array{Float64,3}
-            GAT = CuArray{Float64,3}
+            GAT = CuArray{Float64, 3, CUDA.Mem.DeviceBuffer}
             VI  = Vector{Int64}
-            GVI = CuVector{Int64}
+            GVI = CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}
             MT  = Matrix{Float64}
-            GMT = CuMatrix{Float64}
+            GMT = CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}
             MI  = Matrix{Int64}
-            GMI = CuMatrix{Int64}
+            GMI = CuArray{Int64, 2, CUDA.Mem.DeviceBuffer}
             SMT = CUDA.CUSPARSE.CuSparseMatrixCSR{Float64}
             J = SparseMatrixCSC(J)
         else

diff --git a/src/Polar/polar.jl b/src/Polar/polar.jl
@@ -50,8 +50,8 @@ function PolarForm(pf::PS.PowerNetwork, device::KA.Device)
         M = SparseMatrixCSC
         AT = Array
     elseif isa(device, KA.GPU)
-        IT = CUDA.CuVector{Int64}
-        VT = CUDA.CuVector{Float64}
+        IT = CUDA.CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}
+        VT = CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}
         M = CUSPARSE.CuSparseMatrixCSR
         AT = CUDA.CuArray
     end

diff --git a/src/utils.jl b/src/utils.jl
@@ -55,14 +55,14 @@ mutable struct Spmat{VTI<:AbstractVector, VTF<:AbstractVector}
 end
 
 mutable struct BatchCuSparseMatrixCSR{Tv} <: CUSPARSE.AbstractCuSparseMatrix{Tv}
-    rowPtr::CUDA.CuVector{Cint}
-    colVal::CUDA.CuVector{Cint}
-    nzVal::CUDA.CuMatrix{Tv}
+    rowPtr::CUDA.CuArray{Cint, 1, CUDA.Mem.DeviceBuffer}
+    colVal::CUDA.CuArray{Cint, 1, CUDA.Mem.DeviceBuffer}
+    nzVal::CUDA.CuArray{Tv, 2, CUDA.Mem.DeviceBuffer}
     dims::NTuple{2,Int}
     nnz::Cint
     nbatch::Int
 
-    function BatchCuSparseMatrixCSR{Tv}(rowPtr::CUDA.CuVector{<:Integer}, colVal::CUDA.CuVector{<:Integer},
+    function BatchCuSparseMatrixCSR{Tv}(rowPtr::CUDA.CuArray{<:Integer, 1, CUDA.Mem.DeviceBuffer}, colVal::CUDA.CuArray{<:Integer, 1, CUDA.Mem.DeviceBuffer},
                                    nzVal::CUDA.CuMatrix, dims::NTuple{2,<:Integer}, nnzJ::Int, nbatch::Int) where Tv
         new(rowPtr, colVal, nzVal, dims, nnzJ, nbatch)
     end
@@ -119,8 +119,8 @@ end
 # Source code taken from:
 # https://github.com/JuliaGPU/CUDA.jl/blob/master/lib/cusolver/wrappers.jl#L78L111
 function csclsvqr!(A::CUSPARSE.CuSparseMatrixCSC{Float64},
-                    b::CUDA.CuVector{Float64},
-                    x::CUDA.CuVector{Float64},
+                    b::CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer},
+                    x::CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer},
                     tol::Float64,
                     reorder::Cint,
                     inda::Char)