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\title{Developping XIOS with multithread : to accelerate the IO of climate models}

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\maketitle

\section{background}

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.

XIOS, a libarary dedicated to intense calculates, allows us to easily and efficiently manage the parallel IO 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).

Cette bibliothèque, dédiée au calcul intensif, permet de gérer efficacement et simplement les
entrée/sortie parallèles des données sur les systèmes de stockage. Dans cette nouvelle
approche, orientée client/serveur, des cœurs de calcul sont exclusivement dédiés aux I/O de
façon à minimiser leur impact sur le temps de calcul des modèles. L’utilisation des
communications asynchrones entre les modèles (clients) et les serveurs I/O permet de lisser
les pics I/O en envoyant un flux de données constant au système de fichiers tout au long de la
simulation, recouvrant ainsi totalement les écritures par du calcul.


The aim of this project ESIWACE is to develop a multithreaded version of XIOS, a library dedicated to IO manegement of climate code.
The current XIOS code lies on a single level of parallelization using MPI. However, many climate models are now disigned with two-level 
parallelization through MPI and OpenMP. The difference of parallelization between the climate models and XIOS can lead to performance lost 
because XIOS can not cope with threads. This fact 


The resulting multithreaded XIOS is desinged to cope with climate models which use a two-level parallelization (MPI/Openmp) scheme.
The principle model we work with is the LMDZ code developped at Laboratoire de Météorologie Dynamique. This model has 



\section{Developpement of a thread-friendly MPI for XIOS}

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 
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 
endpoints to be the receiver but not sufficient. We also need to know which MPI process should be involved in such communication. To 
identify the MPI rank, we added a ``map'' in the EP communicator in which the relation of all EP and MPI ranks can be easily obtained.


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 
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 
occur. The reason why the ``probe'' method is not suitable is that messages destinated to one certain process can be probed by any of 
its threads. Thus the message can be received by the wrong thread which gives errors.

To solve this problem, we introduce the ``matching-probe'' technique. The idea of the method is that each process is equiped with a local 
incoming message queue. All incoming message will be probed, sorted, and then stored in this queue according to their destination rank. 
Every time we call an MPI function, we firstly call the \verb|MPI_Mprobe| function to get the handle to 
the incoming message. Then, we identify the destination thread rank and store the message handle inside the local queue of the target 
thread. After this, we perform the usual ``probe'' technique upon the local incoming message queue. In this way, we can assure the messages 
to be received by the right thread.

Another issue remains in this technique: how to identify the receiver's rank? The solution is to use the tag argument. In the MPI 
environment, a tag is an integer ranging from 0 to $2^{31}$. We can explore the large range of the tag to store in it information about the 
source and destination thread ranks. We choose to limite the first 15 bits for the tag used in the classic MPI communication, the next 8 
bits to the sender's thread rank, and the last 8 bits to the receiver's thread rank. In such way, with an extra analysis of the EP tag, we 
can identify the ranks of the sender and the receiver in any P2P communication. As results, we a thread probes a message, it knows 
exactly in which local queue should store the probed message. 


With the global rank map, tag extension, and the matching-probe techniques, we are able to use any P2P communication in the endpoint 
environment. For the collective communications, we perform a step-by-step execution and no special technique is required. The most 
representative functions is the collective communications are \verb|MPI_Gather| and \verb|MPI_Bcast|. A step-by-step execution consists of 
3 steps (not necessarily in this order): arrangement of the source data, execution of the MPI function by all 
master/root threads, distribution or arrangement of the data among threads. 

For example, if we want to perform a broadcast operation, 2 steps are needed. Firstly, the root thread, along with the master threads of 
other processes, perform the classic \verb|MPI_Bcast| operation. Secondly, the root thread, and the master threads send data to threads 
sharing the same process via local memory transfer. In another example for illustrating the \verb|MPI_Gather| function, we also need 2 
steps. First of all, data is gathered from slave threads to the master thread or the root thread. Next, the master thread and the root 
thread execute the \verb|MPI_Gather| operation of complete the communication. Other collective calls such as \verb|MPI_Scan|, 
\verb|MPI_Reduce|, \verb|MPI_Scatter| \textit{etc} follow the same principle of step-by-step execution.


\section{Performance of LMDZ using EP\_XIOS}

With the new version of XIOS, we are now capable of taking full advantages of the computing resources allocated by a simulation model when 
calling XIOS functions. All threads, can participate in XIOS as if they are MPI processes. We have tested the EP\_XIOS in LMDZ and the 
performance results are very encouraging.

In our tests, we used 12 client processor with 8 threads each (96 XIOS clients in total), and one single-thread server processor. We have 2 
output densities. The light output gives mainly 2 dimensional fields while the heavy output records more 3D fields. We also have differente 
simulation duration settings: 1 day, 5 days, 15 days, and 31 days. 

\begin{figure}[h]
 \centering
 \includegraphics[scale = 0.6]{LMDZ_perf.png}
 \caption{Speedup obtained by using EP in LMDZ simulations.}
\end{figure}

In this figure, we show the speedup which is computed by $\displaystyle{\frac{time_{XIOS}}{time_{EP\_XIOS}}}$. The blue bars 
represent speedup of the XIOS file output and the red bars the speedup of LMDZ: calculates + XIOS file output. In all experimens, 
we can observe a speedup which represents a gain in performance. One important conclusion we can get from this result is that, more dense 
the output is, more efficient is the EP\_XIOS. With 8 threads per process, we can reach a speedup in XIOS upto 6, and a speedup of 1.5 in 
LMDZ which represents a decrease of the total execution time to 68\% ($\approx 1/1.5$). This observation confirmes steadily the importance 
of using EP in XIOS.  

The reason why LMDZ does not show much speedup, is because the model is calcutation dominant: time spent on calculation is much longer than 
that on the file output. For example, if 30\% of the execution time is spent on the output, then with a speepup of 6, we can obtain a 
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{Perspectives of EP\_XIOS}

\end{document}