Changeset 1551

Timestamp:

06/26/18 00:28:38 (6 years ago)

Author:

yushan

Message:

report updated

Location:

XIOS/dev/branch_openmp/Note

Files:

• domain.pdf (added)
• domain.svg (added)
• omp.pdf (added)
• omp.svg (added)
• rapport ESIWACE.aux (modified) (1 diff)
• rapport ESIWACE.bbl (modified) (1 diff)
• rapport ESIWACE.blg (modified) (1 diff)
• rapport ESIWACE.log (modified) (19 diffs)
• rapport ESIWACE.pdf (modified) (previous)
• rapport ESIWACE.tex (modified) (5 diffs)

XIOS/dev/branch_openmp/Note/rapport ESIWACE.aux

@@ -1 +1 @@
 \relax 
 \@writefile{toc}{\contentsline {section}{\numberline {1}Context}{1}}
-\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces On the left, each peak of computing power corresponds to the vallay of memory bandwidth which shows that the computing resources are alternating between calculates and IO. ON the right, both curves are smooth which means that the computing resources have a stable charge of work, either calculates or IO.}}{1}}
-\@writefile{toc}{\contentsline {section}{\numberline {2}Developpement of a thread-friendly MPI for XIOS}{2}}
-\@writefile{toc}{\contentsline {section}{\numberline {3}Performance of LMDZ using EP\_XIOS}{3}}
-\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Speedup obtained by using EP in LMDZ simulations.}}{4}}
-\@writefile{toc}{\contentsline {section}{\numberline {4}Perspectives of EP\_XIOS}{4}}
+\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces On the left, each peak of computing power corresponds to the valley of memory bandwidth which shows that the computing resources are alternating between calculates and I/O. ON the right, both curves are smooth which means that the computing resources have a stable charge of work, either calculates or I/O.}}{1}}
+\@writefile{toc}{\contentsline {section}{\numberline {2}Development of thread-friendly XIOS}{2}}
+\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Illustration of the domain decomposition used in LMDZ.}}{2}}
+\citation{Dinan:2013}
+\citation{Sridharan:2014}
+\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces }}{3}}
+\newlabel{fig:omp}{{3}{3}}
+\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces }}{3}}
+\newlabel{fig:scheme}{{4}{3}}
+\@writefile{toc}{\contentsline {section}{\numberline {3}Performance of LMDZ using EP\_XIOS}{5}}
+\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Speedup obtained by using EP in LMDZ simulations.}}{5}}
+\bibstyle{plain}
+\bibdata{reference}
+\bibcite{Dinan:2013}{1}
+\bibcite{Sridharan:2014}{2}
+\@writefile{toc}{\contentsline {section}{\numberline {4}Performance of EP\_XIOS}{6}}
+\@writefile{toc}{\contentsline {section}{\numberline {5}Perspectives of EP\_XIOS}{6}}

XIOS/dev/branch_openmp/Note/rapport ESIWACE.bbl

@@ +1 @@
+\begin{thebibliography}{1}
+
+\bibitem{Dinan:2013}
+J.~Dinan, Pavan Balaji, D.~Goodell, D.~Miller, M.~Snir, and Rajeev Thakur.
+\newblock Enabling mpi interoperability through flexible communication
+  endpoints.
+\newblock In {\em EuroMPI 2013}, Madrid, Spain, 2013.
+
+\bibitem{Sridharan:2014}
+S.~Sridharan, J.~Dinan, and D.~D. Kalamkar.
+\newblock Enabling efficient multithreaded mpi communication through a
+  library-based implementation of mpi endpoints.
+\newblock In {\em SC14: International Conference for High Performance
+  Computing, Networking, Storage and Analysis}, pages 487--498, Nov 2014.
+
+\end{thebibliography}

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XIOS/dev/branch_openmp/Note/rapport ESIWACE.log

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XIOS/dev/branch_openmp/Note/rapport ESIWACE.tex

@@ -9 +9 @@
 % Title Page
 
-\title{Developping XIOS with multithread : to accelerate the IO of climate models}
+\title{Developing XIOS with multi-thread : to accelerate the I/O of climate models}
 
 \author{}
@@ -20 +20 @@
 
 The simulation models of climate systems, running on a large number of computing resources can produce an important volume of data. At this 
-scale, the IO and the post-treatement of data becomes a bottle-neck for the performance. In order to manage efficiently the data flux 
-generated by the simulations, we use XIOS developped by the Institut Pierre Simon Laplace and Maison de la simulation.
+scale, the I/O and the post-treatment of data becomes a bottle-neck for the performance. In order to manage efficiently the data flux 
+generated by the simulations, we use XIOS developed by the Institut Pierre Simon Laplace and Maison de la simulation.
 
-XIOS, a libarary dedicated to intense calculates, allows us to easily and efficiently manage the parallel IO on the storage systems. XIOS 
+XIOS, a library dedicated to intense calculates, allows us to easily and efficiently manage the parallel I/O on the storage systems. XIOS 
 uses the client/server scheme in which computing resources (server) are reserved exclusively for IO in order to minimize their impact on 
-the performance of the climate models (client). The clients and servers are executed in parallel and communicate asynchronuously. In this 
-way, the IO peaks can be smoothed out as data fluxes are send to server constantly throughout the simulation and the time spent on data 
+the performance of the climate models (client). The clients and servers are executed in parallel and communicate asynchronously. In this 
+way, the I/O peaks can be smoothed out as data fluxes are send to server constantly throughout the simulation and the time spent on data 
 writing on the server side can be overlapped completely by calculates on the client side. 
 
@@ -32 +32 @@
 \includegraphics[scale=0.4]{Charge1.png}
 \includegraphics[scale=0.4]{Charge2.png}
-\caption{On the left, each peak of computing power corresponds to the vallay of memory bandwidth which shows that the computing resources 
-are alternating between calculates and IO. ON the right, both curves are smooth which means that the computing resources have a stable 
-charge of work, either calculates or IO.}
+\caption{On the left, each peak of computing power corresponds to the valley of memory bandwidth which shows that the computing resources 
+are alternating between calculates and I/O. ON the right, both curves are smooth which means that the computing resources have a stable 
+charge of work, either calculates or I/O.}
 \end{figure}
 
 
 XIOS works well with many climate simulation codes. For example, LMDZ\footnote{LMDZ is a general circulation model (or global climate model) 
-developped since the 70s at the "Laboratoire de Météorologie Dynamique", which includes various variants for the Earth and other planets 
+developed since the 70s at the "Laboratoire de Météorologie Dynamique", which includes various variants for the Earth and other planets 
 (Mars, Titan, Venus, Exoplanets). The 'Z' in LMDZ stands for "zoom" (and the 'LMD' is for  'Laboratoire de Météorologie Dynamique"). 
-\url{http://lmdz.lmd.jussieu.fr}}, NENO\footnote{Nucleus for European Modelling of the Ocean alias NEMO is a 
+\url{http://lmdz.lmd.jussieu.fr}}, NENO\footnote{Nucleus for European Modeling of the Ocean alias NEMO is a 
 state-of-the-art modelling framework of ocean related engines. \url{https://www.nemo-ocean.eu}}, ORCHIDEE\footnote{the land surface 
 model of the IPSL (Institut Pierre Simon Laplace) Earth System Model. \url{https://orchidee.ipsl.fr}}, and DYNAMICO\footnote{The DYNAMICO 
 project develops a new dynamical core for LMD-Z, the atmospheric general circulation model (GCM) part of IPSL-CM Earth System Model. 
-\url{http://www.lmd.polytechnique.fr/~dubos/DYNAMICO/}} all use XIOS as the output backend. M\'et\'eoFrance and MetOffice also choose XIOS 
-to manege the IO for their models.
+\url{http://www.lmd.polytechnique.fr/~dubos/DYNAMICO/}} all use XIOS as the output back end. M\'et\'eoFrance and MetOffice also choose XIOS 
+to manege the I/O for their models.
 
 
-\section{Developpement of thread-friendly XIOS}
+\section{Development of thread-friendly XIOS}
+
+Although XIOS copes well with many models, there is one potential optimization in XIOS which needs to be investigated: making XIOS thread-friendly.
+
+This topic comes along with the configuration of the climate models. Take LMDZ as example, it is designed with the 2-level parallelization scheme. To be more specific, LMDZ uses the domain decomposition method in which each sub-domain is associated with one MPI process. Inside of the sub-domain, the model also uses OpenMP derivatives to accelerate the computation. We can imagine that the sub-domain be divided into sub-sub-domain and is managed by threads. 
+
+\begin{figure}[h]
+\centering
+\includegraphics[scale=0.5]{domain.pdf}
+\caption{Illustration of the domain decomposition used in LMDZ.}
+\end{figure}
+
+As we know, each sub-domain, or in another word, each MPI process is a XIOS client. The data exchange between client and XIOS servers is handled by MPI communications. In order to write an output field, all threads must gather the data to the master thread who acts as MPI process in order to call MPI routines. There are two disadvantages about this method : first, we have to spend time on gathering information to the master thread which not only increases the memory use, but also implies an OpenMP barrier; second, while the master thread calls MPI routine, other threads are in the idle state thus a waster of computing resources. What we want obtain with the thread-friendly XIOS is that all threads can act like MPI processes. They can call directly the MPI routine thus no waste in memory nor in computing resources as shown in Figure \ref{fig:omp}.
+
+\begin{figure}[h!]
+\centering
+\includegraphics[scale=0.6]{omp.pdf}
+\caption{}
+\label{fig:omp}
+\end{figure}
+
+There are two ways to make XIOS thread-friendly. First of all, change the structure of XIOS which demands a lot of modification is the XIOS library. Knowing that XIOS is about 100 000 lines of code, this method will be very time consuming. What's more, the modification will be local to XIOS. If we want to optimize an other code to be thread-friendly, we have to redo the modifications. The second choice is to add an extra interface to MPI in order to manage the threads. When a thread want to call an MPI routine inside XIOS, it will first pass the interface, in which the communication information will be analyzed before the MPI routine is invoked. With this method, we only need to modify a very small part of XIOS in order to make it work. What is more interesting is that the interface we created can be adjusted to suit other MPI based libraries.
 
 
-XIOS is a library dedicated to IO management of climate code. It has a client-server pattern in which clients are in charge of computations 
-and servers manage the reading and writing of files. The communication between clients and servers are handled by MPI.
-However, some of the climate models (\textit{e.g.} LMDZ) nowadays use an hybrid programming policy. Within a shared memory node, OpenMP 
-directives are used to manage message exchanges. In such configuration, XIOS can not take full advantages of the computing resources to 
-maximize the performance. This is because XIOS can only work with MPI processes. Before each call of XIOS routines, threads of one MPI 
-process must gather their information to the master thread who works as an MPI process. After the call, the master thread distributes the 
-updated information among its slave threads. As result, all slave threads have to wait while the master thread calls the XIOS routines. 
-This introduce extra synchronization into the model and leads to not optimized performance. Aware of this situation, we need to develop a 
-new version of XIOS (EP\_XIOS) which can work with threads, or in other words, can consider threads as they were processes. To do so, we 
-introduce the MPI endpoints.  
+In this project, we choose to implement the interface to handle the threads. To do so, we introduce the MPI\_endpoint which is a concept proposed in the last MPI Forum and several papers has already discussed the importance of such idea and have introduced the framework of the MPI\_endpoint \cite{Dinan:2013}\cite{Sridharan:2014}. The concept of an endpoint is shown by Figure \ref{fig:scheme}. Threads of an MPI process is associated with a unique rank (global endpoint rank) and an endpoint communicator. They also have a local rank (rank inside the MPI process) which is very similar to the \verb|OMP_thread_num| rank. 
+
+\begin{figure}[h!]
+\begin{center}
+\includegraphics[scale=0.4]{scheme.png} 
+\end{center}
+\caption{}
+\label{fig:scheme}
+\end{figure}
+
+%XIOS is a library dedicated to IO management of climate code. It has a client-server pattern in which clients are in charge of computations and servers manage the reading and writing of files. The communication between clients and servers are handled by MPI. However, some of the climate models (\textit{e.g.} LMDZ) nowadays use an hybrid programming policy. Within a shared memory node, OpenMP directives are used to manage message exchanges. In such configuration, XIOS can not take full advantages of the computing resources to maximize the performance. This is because XIOS can only work with MPI processes. Before each call of XIOS routines, threads of one MPI process must gather their information to the master thread who works as an MPI process. After the call, the master thread distributes the updated information among its slave threads. As result, all slave threads have to wait while the master thread calls the XIOS routines. This introduce extra synchronization into the model and leads to not optimized performance. Aware of this situation, we need to develop a new version of XIOS (EP\_XIOS) which can work with threads, or in other words, can consider threads as they were processes. To do so, we introduce the MPI endpoints.  
 
 
-The MPI endpoints (EP) is a layer on top of an existing MPI Implementation. All MPI function, or in our work the functions used in XIOS, 
-will be reimplemented in order to cope with OpenMP threads. The idea is that, in the MPI endpoints environment, each OpenMP thread will be 
+The MPI\_endpoints interface we implemented lies on top of an existing MPI Implementation. It consists of wrappers to all MPI functions used in XIOS. 
+
+will be re-implemented in order to cope with OpenMP threads. The idea is that, in the MPI endpoints environment, each OpenMP thread will be 
 associated with a unique rank and with an endpoint communicator. This rank (EP rank) will replace the role of the classic MPI rank and will 
 be used in MPI communications. In order to successfully execute an MPI communication, for example \verb|MPI_Send|, we know already which 
@@ -73 +96 @@
 
 In XIOS, we used the ``probe'' technique to search for arrived messages and then performing the receive action. The principle is 
-that sender processes execute the send operations as usual. However, to minimise the time spent on waiting incoming messages, the receiver 
+that sender processes execute the send operations as usual. However, to minimize the time spent on waiting incoming messages, the receiver 
 processe performs in the first place the \verb|MPI_Probe| function to check if a message destinated to it has been published. If yes, the 
 process execute in the second place the \verb|MPI_Recv| to receive the message. In this situation, if we introduce the threads, problems 
@@ -135 +158 @@
 decrease in time of 25\%. Even the 25\% may seems to be small, it is still a gain in performance with existing computing resources.
 
+\section{Performance of EP\_XIOS}
+
+workfloz\_cmip6 
+light output
+24*8+2
+30s - 52s
+32 days
+histmth with daily output
+
 \section{Perspectives of EP\_XIOS}
 
+
+\bibliographystyle{plain}
+\bibliography{reference}
+
 \end{document}