Add text about CUDA support

diff --git a/9.1/paper.bib b/9.1/paper.bib
index 3a2878a2e28e1e5e208ff74644bf70ce2cad1f45..01dba7594548fb908806e2bc73ff16c4853a5973 100644 (file)
--- a/9.1/paper.bib
+++ b/9.1/paper.bib
@@ -831,4 +831,15 @@ year = {2009}
   author = {R. M. Kynch and P. D. Ledger},
   title = {Resolving the sign conflict problem for hp{\textendash}hexahedral {N}{\'{e}}d{\'{e}}lec elements with application to eddy current problems},
   journal = {Computers {\&} Structures}
-}
\ No newline at end of file
+}
+
+@proceedings{ljungkvist2017,
+  title={{M}atrix-{F}ree {F}inite-{E}lement {C}omputations on {G}raphics
+    {P}rocessors with {A}daptively {R}efined {U}nstructured {M}eshes},
+  author={Karl Ljungkvist},
+  year={2017},
+  address={Virginia Beach, VA, USA},
+  month={April 23-26},
+  publisher={Proceedings of the 25th High Performance Computing Symposium,
+    SpringSim-HPC}, 
+}

diff --git a/9.1/paper.tex b/9.1/paper.tex
index ef4524c82f2e6108072a3dcfa9a627de2089d4cd..8122a6778f424d6d30e0ab402302d61bf8beef93 100644 (file)
--- a/9.1/paper.tex
+++ b/9.1/paper.tex
@@ -359,12 +359,32 @@ and their performance in a separate publication.
 \subsection{GPU support via CUDA}
 \label{subsec:gpu}
 
-The GPU support was significantly extended for the current release. On the one hand, the MPI-parallel vector class
-\texttt{LinearAlgebra::distributed::Vector} has gained a second template
-argument \texttt{MemorySpace} which can either be \texttt{Host} or
-\texttt{CUDA}. In the latter case, the data resides in the GPU memory. Also,
-the ghost exchange can be performed completely via CUDA in case a compatible
-MPI library is found (as is usual on large GPU-based supercomputers).
+The GPU support was significantly extended for the current release:
+\begin{itemize}
+  \item \texttt{LinearAlgebra::distributed::Vector}: the MPI-parallel vector
+    class has gained a second template argument \texttt{MemorySpace} which can
+    either be \texttt{Host} or \texttt{CUDA}. In the latter case, the data
+    resides in the GPU memory. By default, the template parameter is
+    \texttt{Host} and the behavior is unchanged compared to previous versions. 
+    When using CUDA, the ghost exchange can be performed either by first copy
+    the relevant data to the host, performing a MPI communication, and finally
+    move the data to the device or, if CUDA-aware MPI is available, by
+    performing the MPI directly from GPU to GPU.
+  \item Constrained degrees of freedom: the matrix-free framework now now
+    supports constraints degrees of freedom. The implementation is based on
+    \cite{ljungkvist2017}. With this addition, not only the user can impose Dirichlet
+    boundary conditions but the matrix-free framework can be used on adapted
+    meshes. The only restriction is that for two-dimensional meshes, the degree
+    of finite elements must be odd.  There is no such restriction in three
+    dimensions.
+  \item MPI matrix-free: using \texttt{LinearAlgebra::distributed::Vector}, the
+    matrix-free framework can scale to multiple multiple GPUS by taking
+    advantage of MPI. Each MPI process can only use GPU and therefore, if
+    multiple GPUs are available in one node, it is necessary to have as many
+    ranks as GPUs. Using Nvidia Multi-Process Device (MPS), it is also possible
+    for multiple processes to use the same GPU. This can be advantageous if the
+    amount of work on one rank is not sufficient to fully utilize GPU.
+\end{itemize}
 
 Furthermore, a new tutorial program \texttt{step-64} has been added that demonstrates the usage of matrix-free methods on Nvidia GPUs. GPUs are advantageous for these kind of operations because of their superior hardware characteristics, in particular a higher memory bandwidth than server CPUs within a given power envelope. The matrix-free GPU components integrated in \dealii have been analyzed against CPUs in \cite{KronbichlerLjungkvist2019}, where the application to geometric multigrid solvers is discussed.