L1 Gauss Seidel preconditioner #191
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| ######################################### | ||
| ## CUDA L1 Gauss Seidel Preconditioner ## | ||
| ######################################### | ||
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| # PIRACY ALERT: this code exhibit piratic nature because both `adapt` and its arguments are foreign objects. | ||
| # Therefore, `adapt` behavior is going to be different depending on whether `Thunderbolt` and `CuThunderboltExt` are loaded or not. | ||
| # Reference: https://juliatesting.github.io/Aqua.jl/stable/piracies/ | ||
| # Note: the problem is with `AbstractSparseMatrix` as the default behavior of `adapt` is to return the same object, whatever the backend is. | ||
| # Adapt.adapt(::CUDABackend, A::CUSPARSE.AbstractCuSparseMatrix) = A | ||
| # Adapt.adapt(::CUDABackend,A::AbstractSparseMatrix) = A |> cu | ||
| # Adapt.adapt(::CUDABackend, x::Vector) = x |> cu # not needed | ||
| # Adapt.adapt(::CUDABackend, x::CuVector) = x # not needed | ||
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| # TODO: remove this function if back compatibility is not needed | ||
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| Preconditioners.convert_to_backend(::CUDABackend, A::AbstractSparseMatrix) = adapt(CUDABackend(), A) | ||
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| # For some reason, these properties are not automatically defined for Device Arrays, | ||
| # TODO: remove the following code when https://github.com/JuliaGPU/CUDA.jl/pull/2738 is merged | ||
| #SparseArrays.rowvals(A::CUSPARSE.CuSparseDeviceMatrixCSC{Tv,Ti,1}) where {Tv,Ti} = A.rowVal | ||
| #SparseArrays.getcolptr(A::CUSPARSE.CuSparseDeviceMatrixCSC{Tv,Ti,1}) where {Tv,Ti} = A.colPtr | ||
| #SparseArrays.getnzval(A::CUSPARSE.CuSparseDeviceMatrixCSC{Tv,Ti,1}) where {Tv,Ti} = A.nzVal | ||
| #SparseMatricesCSR.getnzval(A::CUSPARSE.CuSparseDeviceMatrixCSR{Tv,Ti,1}) where {Tv,Ti} = A.nzVal | ||
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| # PIRACY ALERT: the following code is commented out to avoid piracy | ||
| # SparseMatricesCSR.colvals(A::CUSPARSE.CuSparseDeviceMatrixCSR{Tv,Ti,1}) where {Tv,Ti} = A.colVal | ||
| # SparseMatricesCSR.getrowptr(A::CUSPARSE.CuSparseDeviceMatrixCSR{Tv,Ti,1}) where {Tv,Ti} = A.rowPtr | ||
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| # workaround for the issue with SparseMatricesCSR | ||
| # TODO: find a more robust solution to dispatch the correct function | ||
| Preconditioners.colvals(A::CUSPARSE.CuSparseDeviceMatrixCSR{Tv,Ti,1}) where {Tv,Ti} = A.colVal | ||
| Preconditioners.getrowptr(A::CUSPARSE.CuSparseDeviceMatrixCSR{Tv,Ti,1}) where {Tv,Ti} = A.rowPtr | ||
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| Preconditioners.sparsemat_format_type(::CUSPARSE.CuSparseDeviceMatrixCSC{Tv,Ti,1}) where {Tv,Ti} = CSCFormat | ||
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| Preconditioners.sparsemat_format_type(::CUSPARSE.CuSparseDeviceMatrixCSR{Tv,Ti,1}) where {Tv,Ti} = CSRFormat | ||
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| # remove the following code once PR (https://github.com/JuliaGPU/CUDA.jl/pull/2720) is merged ## | ||
| CUDA.CUSPARSE.CuSparseMatrixCSR{T}(Mat::SparseMatrixCSR) where {T} = | ||
| CUDA.CUSPARSE.CuSparseMatrixCSR{T}(CuVector{Cint}(Mat.rowptr), CuVector{Cint}(Mat.colval), | ||
| CuVector{T}(Mat.nzval), size(Mat)) | ||
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| CUSPARSE.CuSparseMatrixCSC{T}(Mat::SparseMatrixCSR) where {T} = | ||
| CUSPARSE.CuSparseMatrixCSC{T}(CUSPARSE.CuSparseMatrixCSR(Mat)) | ||
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| SparseMatricesCSR.SparseMatrixCSR(A::CUSPARSE.CuSparseMatrixCSR) = | ||
| SparseMatrixCSR(CUSPARSE.SparseMatrixCSC(A)) # no direct conversion (gpu_CSR -> cpu_CSC -> cpu_CSR) | ||
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| Adapt.adapt_storage(::Type{CuArray}, xs::SparseMatrixCSR) = | ||
| CUSPARSE.CuSparseMatrixCSR(xs) | ||
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| Adapt.adapt_storage(::Type{CuArray{T}}, xs::SparseMatrixCSR) where {T} = | ||
| CUSPARSE.CuSparseMatrixCSR{T}(xs) | ||
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| Adapt.adapt_storage(::Type{Array}, mat::CUSPARSE.CuSparseMatrixCSR) = | ||
| SparseMatrixCSR(mat) | ||
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| module Preconditioners | ||
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| using SparseArrays, SparseMatricesCSR | ||
| using LinearSolve | ||
| import LinearSolve: \ | ||
| using Adapt | ||
| using UnPack | ||
| import KernelAbstractions: Backend, @kernel, @index, @ndrange, @groupsize, @print, functional, | ||
| CPU,synchronize | ||
| import SparseArrays: getcolptr,getnzval | ||
| import SparseMatricesCSR: getnzval | ||
| import LinearAlgebra: Symmetric | ||
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| ## Generic Code # | ||
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| # CSR and CSC are exact the same in symmetric matrices,so we need to hold symmetry info | ||
| # in order to be exploited in cases in which one format has better access pattern than the other. | ||
| abstract type AbstractMatrixSymmetry end | ||
| struct SymmetricMatrix <: AbstractMatrixSymmetry end | ||
| struct NonSymmetricMatrix <: AbstractMatrixSymmetry end | ||
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| abstract type AbstractMatrixFormat end | ||
| struct CSRFormat <: AbstractMatrixFormat end | ||
| struct CSCFormat <: AbstractMatrixFormat end | ||
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| # Why using these traits? | ||
| # Since we are targeting multiple backends, but unfortunately, all the sparse matrix CSC/CSR on all | ||
| # backends don't share the same supertype (e.g. AbstractSparseMatrixCSC/AbstractSparseMatrixCSR) | ||
| # e.g. CUSPARSE.CuSparseDeviceMatrixCSC <:SparseArrays.AbstractSparseMatrixCSC → false | ||
| # So we need to define our own traits to identify the format of the sparse matrix | ||
| sparsemat_format_type(::SparseMatrixCSC) = CSCFormat | ||
| sparsemat_format_type(::SparseMatrixCSR) = CSRFormat | ||
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| #TODO: remove once https://github.com/JuliaGPU/CUDA.jl/pull/2740 is merged | ||
| convert_to_backend(backend::Backend, A::AbstractSparseMatrix) = | ||
| adapt(backend, A) # fallback value, specific backends are to be extended in their corresponding extensions. | ||
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| # Why? because we want to circumvent piracy when extending these functions for device backend (e.g. CuSparseDeviceMatrixCSR) | ||
| # TODO: find a more robust solution to dispatch the correct function | ||
| colvals(A::SparseMatrixCSR) = SparseMatricesCSR.colvals(A) | ||
| getrowptr(A::SparseMatrixCSR) = SparseMatricesCSR.getrowptr(A) | ||
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| include("l1_gauss_seidel.jl") | ||
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| export L1GSPrecBuilder | ||
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| end | ||
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