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updating examples/gpu and doc

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pasquadambra
2021-06-08 15:15:44 +02:00
parent 9b9dfbd198
commit 10e9c53e54
3 changed files with 25 additions and 23 deletions
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docs/amg4psblas_1.0-guide.pdf
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docs/src/gettingstarted.tex
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@@ -584,15 +584,18 @@ Krylov method. At the end of the code, we close the GPU environment
\caption{setup of a GPU-enabled test program part three.\label{fig:gpu-ex3}}
\end{listing}
It is very important to employ solvers that are suited
to the GPU, i.e. solvers that do NOT employ triangular
system solve kernels. Solvers that satisfy this constraint include:
It is very important to employ smoothers and coarsest solvers that are suited
to the GPU, i.e. methods that do NOT employ triangular
system solve kernels. Methods that satisfy this constraint include:
\begin{itemize}
\item \verb|JACOBI|
\item \verb|BJAC| with the following methods on the local blocks:
\begin{itemize}
\item \verb|INVK|
\item \verb|INVT|
\item \verb|AINV|
\end{itemize}
\end{itemize}
and their $\ell_1$ variants.
%%% Local Variables:
examples/gpu/amg_dexample_gpu.f90
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@@ -39,23 +39,18 @@
!
! This sample program solves a linear system obtained by discretizing a
! PDE with Dirichlet BCs. The solver is CG, coupled with one of the
! following multi-level preconditioner, as explained in Section 4.1 of
! following multi-level preconditioner, as explained in Section 4.2 of
! the AMG4PSBLAS User's and Reference Guide:
!
! - choice = 1, the default multi-level preconditioner solver, i.e.,
! V-cycle with decoupled smoothed aggregation, 1 hybrid forward/backward
! GS sweep as pre/post-smoother and UMFPACK as coarsest-level
! solver (Sec. 4.1, Listing 1)
! - choice = 1, a V-cycle with decoupled smoothed aggregation, 4 Jacobi
! sweeps as pre/post-smoother and 8 Jacobi sweeps as coarsest-level
! solver with replicated coarsest matrix
!
! - choice = 2, a V-cycle preconditioner with 1 block-Jacobi sweep
! (with ILU(0) on the blocks) as pre- and post-smoother, and 8 block-Jacobi
! sweeps (with ILU(0) on the blocks) as coarsest-level solver (Sec. 4.1, Listing 2)
!
! - choice = 3, W-cycle preconditioner based on the coupled aggregation relying
! on matching, with maximum size of aggregates equal to 8 and smoothed prolongators,
! 2 hybrid forward/backward GS sweeps as pre/post-smoother, a distributed coarsest
! matrix, and preconditioned Flexible Conjugate Gradient as coarsest-level solver
! (Sec. 4.1, Listing 3)
! - choice = 2, a W-cycle based on the coupled aggregation relying on matching,
! with maximum size of aggregates equal to 8 and smoothed prolongators,
! 2 sweeps of Block-Jacobi ipre/post-smoother using approximate inverse INVK and
! 4 sweeps of Block-Jacobi with INVK as coarsest-level solver on distributed
! coarsest matrix
!
! The matrix and the rhs are read from files (if an rhs is not available, the
! unit rhs is set).
@@ -183,8 +178,9 @@ program amg_dexample_gpu
case(1)
! initialize a V-cycle preconditioner with 4 Jacobi sweep
! and 8 Jacobi sweeps as coarsest-level solver
! initialize a V-cycle preconditioner, relying on decoupled smoothed aggregation
! with 4 Jacobi sweeps as pre/post-smoother
! and 8 Jacobi sweeps as coarsest-level solver on replicated coarsest matrix
call P%init(ctxt,'ML',info)
call P%set('SMOOTHER_TYPE','JACOBI',info)
@@ -195,19 +191,22 @@ program amg_dexample_gpu
case(2)
! initialize a V-cycle preconditioner based on the coupled aggregation relying on matching,
! initialize a W-cycle preconditioner based on the coupled aggregation relying on matching,
! with maximum size of aggregates equal to 8 and smoothed prolongators,
! Block-Jacobi smoother using approximate inverse INVK and
! and 4 sweeps of INVK on he coarsest level
! 2 sweeps of Block-Jacobi pre/post-smoother using approximate inverse INVK and
! 4 sweeps of Block-Jacobi with INVK on the coarsest level distributed matrix
call P%init(ctxt,'ML',info)
call P%set('PAR_AGGR_ALG','COUPLED',info)
call P%set('AGGR_TYPE','MATCHBOXP',info)
call P%set('AGGR_SIZE',8,info)
call P%set('ML_CYCLE','WCYCLE',info)
call P%set('SMOOTHER_TYPE','BJAC',info)
call P%set('SMOOTHER_SWEEPS',2,info)
call P%set('SUB_SOLVE','INVK',info)
call P%set('COARSE_SOLVE','INVK',info)
call P%set('COARSE_SOLVE','BJAC',info)
call P%set('COARSE_SUBSOLVE','INVK',info)
call P%set('COARSE_SWEEPS',4,info)
call P%set('COARSE_MAT','DIST',info)
kmethod = 'CG'