Update SimGrid ref.

[loba-papers.git] / loba-besteffort / loba-besteffort.tex

diff --git a/loba-besteffort/loba-besteffort.tex b/loba-besteffort/loba-besteffort.tex
index e966c402212b5dcb376ca9744b12cc5b71073e20..c387ad6e62bee5e66175f1220104360944bb12e4 100644 (file)
--- a/loba-besteffort/loba-besteffort.tex
+++ b/loba-besteffort/loba-besteffort.tex
@@ -87,7 +87,8 @@
 Load  balancing algorithms  are  extensively used  in  parallel and  distributed
 applications in  order to  reduce the  execution times. They  can be  applied in
 different scientific  fields from high  performance computation to  micro sensor
 Load  balancing algorithms  are  extensively used  in  parallel and  distributed
 applications in  order to  reduce the  execution times. They  can be  applied in
 different scientific  fields from high  performance computation to  micro sensor

-networks.   They are  iterative by  nature.  In  literature many  kinds  of load
+networks.   They are  iterative by  nature.\FIXME{really?}
+In  literature many  kinds  of load

 balancing  algorithms  have been  studied.   They  can  be classified  according
 different  criteria:   centralized  or  decentralized,  in   static  or  dynamic
 environment,  with  homogeneous  or  heterogeneous load,  using  synchronous  or
 balancing  algorithms  have been  studied.   They  can  be classified  according
 different  criteria:   centralized  or  decentralized,  in   static  or  dynamic
 environment,  with  homogeneous  or  heterogeneous load,  using  synchronous  or


@@ -115,11 +116,11 @@ ensuring that all the nodes concerned  by the load balancing phase have the same
 amount of  load.  Moreover, when real asynchronous  applications are considered,
 using  asynchronous   load  balancing   algorithms  can  reduce   the  execution
 times. Most of the times, it is simpler to distinguish load information messages
 amount of  load.  Moreover, when real asynchronous  applications are considered,
 using  asynchronous   load  balancing   algorithms  can  reduce   the  execution
 times. Most of the times, it is simpler to distinguish load information messages

-from  data  migration  messages.  Former  ones  allows  a  node to  inform  its
+from  data  migration  messages.  Former  ones  allow  a  node to  inform  its

 neighbors of its  current load. These messages are very small,  they can be sent
 neighbors of its  current load. These messages are very small,  they can be sent

-quite often.  For example, if an  computing iteration takes  a significant times
+quite often.  For example, if a computing iteration takes  a significant times

 (ranging from seconds to minutes), it is possible to send a new load information
 (ranging from seconds to minutes), it is possible to send a new load information

-message at each  neighbor at each iteration. Latter  messages contains data that
+message to each  neighbor at each iteration. Latter  messages contain data that

 migrates from one node to another one. Depending on the application, it may have
 sense or not  that nodes try to balance  a part of their load  at each computing
 iteration. But the time to transfer a load message from a node to another one is
 migrates from one node to another one. Depending on the application, it may have
 sense or not  that nodes try to balance  a part of their load  at each computing
 iteration. But the time to transfer a load message from a node to another one is


@@ -129,14 +130,12 @@ it can take this information into account  and it can consider that its new load
 is larger.   Consequently, it can  send a part  of it real  load to some  of its
 neighbors if required. We call this trick the \emph{virtual load} mechanism.
 
 is larger.   Consequently, it can  send a part  of it real  load to some  of its
 neighbors if required. We call this trick the \emph{virtual load} mechanism.
 

-
-
-So, in  this work, we propose a  new strategy for improving  the distribution of
-the  load  and  a  simple  but  efficient trick  that  also  improves  the  load
-balancing. Moreover, we have conducted  many simulations with SimGrid in order to
-validate our improvements are really efficient. Our simulations consider that in
-order  to send a  message, a  latency delays  the sending  and according  to the
-network  performance and  the message  size, the  time of  the reception  of the
+So, in this work, we propose a new strategy to improve the distribution of the
+load and a simple but efficient trick that also improves the load
+balancing. Moreover, we have conducted many simulations with SimGrid in order to
+validate that our improvements are really efficient. Our simulations consider
+that in order to send a message, a latency delays the sending and according to
+the network performance and the message size, the time of the reception of the

 message also varies.
 
 In the following of this paper, Section~\ref{sec.bt-algo} describes the
 message also varies.
 
 In the following of this paper, Section~\ref{sec.bt-algo} describes the


@@ -159,8 +158,8 @@ In  order  prove  the  convergence  of  asynchronous  iterative  load  balancing
 Bertsekas         and        Tsitsiklis         proposed         a        model
 in~\cite{bertsekas+tsitsiklis.1997.parallel}.   Here we  recall  some notations.
 Consider  that  $N={1,...,n}$  processors   are  connected  through  a  network.
 Bertsekas         and        Tsitsiklis         proposed         a        model
 in~\cite{bertsekas+tsitsiklis.1997.parallel}.   Here we  recall  some notations.
 Consider  that  $N={1,...,n}$  processors   are  connected  through  a  network.

-Communication links  are represented by  a connected undirected  graph $G=(N,V)$
-where $V$ is the set of links connecting different processors. In this work, we
+Communication links  are represented by  a connected undirected  graph $G=(N,A)$
+where $A$ is the set of links connecting different processors. In this work, we

 consider that  processors are  homogeneous for sake  of simplicity. It  is quite
 easy to tackle the  heterogeneous case~\cite{ElsMonPre02}. Load of processor $i$
 at  time $t$  is  represented  by $x_i(t)\geq  0$.   Let $V(i)$  be  the set  of
 consider that  processors are  homogeneous for sake  of simplicity. It  is quite
 easy to tackle the  heterogeneous case~\cite{ElsMonPre02}. Load of processor $i$
 at  time $t$  is  represented  by $x_i(t)\geq  0$.   Let $V(i)$  be  the set  of


@@ -193,15 +192,15 @@ Nevertheless,  we  think that  this  condition may  lead  to  deadlocks in  some
 cases. For example, if we consider  only three processors and that processor $1$
 is linked to processor $2$ which is  also linked to processor $3$ (i.e. a simple
 chain which 3 processors). Now consider we have the following values at time $t$:
 cases. For example, if we consider  only three processors and that processor $1$
 is linked to processor $2$ which is  also linked to processor $3$ (i.e. a simple
 chain which 3 processors). Now consider we have the following values at time $t$:

-\begin{eqnarray*}
-x_1(t)=10   \\
-x_2(t)=100   \\
-x_3(t)=99.99\\
- x_3^2(t)=99.99\\
-\end{eqnarray*}
+\begin{align*}
+  x_1(t)   &= 10    \\
+  x_2(t)   &= 100   \\
+  x_3(t)   &= 99.99 \\
+  x_3^2(t) &= 99.99 \\
+\end{align*}

 In this case, processor $2$ can either sends load to processor $1$ or processor
 $3$.  If it sends load to processor $1$ it will not satisfy condition
 In this case, processor $2$ can either sends load to processor $1$ or processor
 $3$.  If it sends load to processor $1$ it will not satisfy condition

-(\ref{eq.ping-pong}) because after the sending it will be less loaded that
+\eqref{eq.ping-pong} because after the sending it will be less loaded that

 $x_3^2(t)$.  So we consider that the \emph{ping-pong} condition is probably to
 strong. Currently, we did not try to make another convergence proof without this
 condition or with a weaker condition.
 $x_3^2(t)$.  So we consider that the \emph{ping-pong} condition is probably to
 strong. Currently, we did not try to make another convergence proof without this
 condition or with a weaker condition.


@@ -307,7 +306,7 @@ a load balancing decision, it starts by sorting its neighbors by increasing
 order of their load.  Then, it computes the difference between its own load, and
 the load of each of its neighbors.  Finally, taking the neighbors following the
 order defined before, the amount of load to send $s_{ij}$ is computed as
 order of their load.  Then, it computes the difference between its own load, and
 the load of each of its neighbors.  Finally, taking the neighbors following the
 order defined before, the amount of load to send $s_{ij}$ is computed as

-$1/(N+1)$ of the load difference, with $N$ being the number of neighbors.  This
+$1/(n+1)$ of the load difference, with $n$ being the number of neighbors.  This

 process continues as long as the node is more loaded than the considered
 neighbor.
 
 process continues as long as the node is more loaded than the considered
 neighbor.
 


@@ -318,8 +317,8 @@ neighbor.
 In this section,  we present the concept of \emph{virtual load}.  In order to
 use this concept, load balancing messages must be sent using two different kinds
 of  messages:  load information  messages  and  load  balancing messages.   More
 In this section,  we present the concept of \emph{virtual load}.  In order to
 use this concept, load balancing messages must be sent using two different kinds
 of  messages:  load information  messages  and  load  balancing messages.   More

-precisely, a node  wanting to send a part  of its load to one  of its neighbors,
-can first send  a load information message containing the load  it will send and
+precisely, a node wanting to send a part of its load to one of its neighbors
+can first send a load information message containing the load it will send, and

 then it can send the load  balancing message containing data  to be transferred.
 Load information  message are really  short, consequently they will  be received
 very quickly.  In opposition, load  balancing messages are often bigger and thus
 then it can send the load  balancing message containing data  to be transferred.
 Load information  message are really  short, consequently they will  be received
 very quickly.  In opposition, load  balancing messages are often bigger and thus


@@ -336,7 +335,7 @@ load  to  some of  its  neighbors  without waiting  the  reception  of the  load
 balancing message.
 
 Doing  this, we  can  expect a  faster  convergence since  nodes  have a  faster
 balancing message.
 
 Doing  this, we  can  expect a  faster  convergence since  nodes  have a  faster

-information of the load they will receive, so they can take in into account.
+information of the load they will receive, so they can take it into account.

 
 \FIXME{Est ce qu'on donne l'algo avec virtual load?}
 
 
 \FIXME{Est ce qu'on donne l'algo avec virtual load?}
 


@@ -347,7 +346,7 @@ information of the load they will receive, so they can take in into account.
 
 In order to test and validate our approaches, we wrote a simulator
 using the SimGrid
 
 In order to test and validate our approaches, we wrote a simulator
 using the SimGrid

-framework~\cite{casanova+legrand+quinson.2008.simgrid}.  This
+framework~\cite{simgrid.web,casanova+giersch+legrand+al.2014.simgrid}.  This

 simulator, which consists of about 2,700 lines of C++, allows to run
 the different load-balancing strategies under various parameters, such
 as the initial distribution of load, the interconnection topology, the
 simulator, which consists of about 2,700 lines of C++, allows to run
 the different load-balancing strategies under various parameters, such
 as the initial distribution of load, the interconnection topology, the


@@ -383,7 +382,7 @@ For the sake of simplicity, a few details were voluntary omitted from
 these descriptions.  For an exhaustive presentation, we refer to the
 actual source code that was used for the experiments%
 \footnote{As mentioned before, our simulator relies on the SimGrid
 these descriptions.  For an exhaustive presentation, we refer to the
 actual source code that was used for the experiments%
 \footnote{As mentioned before, our simulator relies on the SimGrid

-  framework~\cite{casanova+legrand+quinson.2008.simgrid}.  For the
+  framework~\cite{casanova+giersch+legrand+al.2014.simgrid}.  For the

   experiments, we used a pre-release of SimGrid 3.7 (Git commit
   67d62fca5bdee96f590c942b50021cdde5ce0c07, available from
   \url{https://gforge.inria.fr/scm/?group_id=12})}, and which is
   experiments, we used a pre-release of SimGrid 3.7 (Git commit
   67d62fca5bdee96f590c942b50021cdde5ce0c07, available from
   \url{https://gforge.inria.fr/scm/?group_id=12})}, and which is


@@ -561,7 +560,7 @@ algorithms currently do not handle heterogeneous computing resources, the
 processor speeds were normalized, and we arbitrarily chose to fix them to
 1~GFlop/s.
 
 processor speeds were normalized, and we arbitrarily chose to fix them to
 1~GFlop/s.
 

-Then we derived each sort of platform with four different number of computing
+Then we derived each kind of platform with four different numbers of computing

 nodes: 16, 64, 256, and 1024 nodes.
 
 \subsubsection{Configurations}
 nodes: 16, 64, 256, and 1024 nodes.
 
 \subsubsection{Configurations}


@@ -724,70 +723,103 @@ allocated time, or because we simply decided not to run it.
 
 \FIXME{annoncer le plan de la suite}
 
 
 \FIXME{annoncer le plan de la suite}
 

-\subsubsection{The \besteffort{} strategy with the load initially on only one
-  node}
+\subsubsection{The \besteffort{} and  \makhoul{} strategies without virtual load}

 
 Before looking  at the different variations,  we will first show  that the plain
 \besteffort{}  strategy  is valuable,  and  may be  as  good  as the  \makhoul{}
 
 Before looking  at the different variations,  we will first show  that the plain
 \besteffort{}  strategy  is valuable,  and  may be  as  good  as the  \makhoul{}

-strategy.  On  the graphs  from the figure~\ref{fig.results1},  these strategies
-(with virtual load feature) are respectively labeled ``b'' and ``a''.
+strategy.  On  Figures~\ref{fig.results1} and~\ref{fig.resultsN},
+these strategies are respectively labeled ``b'' and ``a''.

 
 We  can  see  that  the  relative  performance of  these  strategies  is  mainly
 influenced by  the application topology.  It  is for the line  topology that the
 difference is the  more important.  In this case,  the \besteffort{} strategy is
 
 We  can  see  that  the  relative  performance of  these  strategies  is  mainly
 influenced by  the application topology.  It  is for the line  topology that the
 difference is the  more important.  In this case,  the \besteffort{} strategy is

-nearly twice as  fast as the \makhoul{} strategy.  This can  be explained by the
-fact that the \besteffort{} strategy tries to distribute the load faitly between
+nearly faster than the \makhoul{} strategy.  This can  be explained by the
+fact that the \besteffort{} strategy tries to distribute the load fairly between

 all the nodes  and with the line topology,  it is easy to load  balance the load
 fairly.
 
 On the contrary, for the hypercube topology, the \besteffort{} strategy performs
 worse than the \makhoul{} strategy. In this case, the \makhoul{} strategy which
 all the nodes  and with the line topology,  it is easy to load  balance the load
 fairly.
 
 On the contrary, for the hypercube topology, the \besteffort{} strategy performs
 worse than the \makhoul{} strategy. In this case, the \makhoul{} strategy which

-tries to give more load to few neighbors reaches the equilibrum faster.
+tries to give more load to few neighbors reaches the equilibrium faster.

 
 For the torus  topology, for which the  number of links is between  the line and
 the hypercube, the \makhoul{} strategy  is slightly better but the difference is
 
 For the torus  topology, for which the  number of links is between  the line and
 the hypercube, the \makhoul{} strategy  is slightly better but the difference is

-more nuanced.
+more nuanced when the initial load is  only on one node. The only case where the
+\makhoul{} strategy is really faster than the \besteffort{} strategy is with the
+random initial distribution when the communication are slow.

 
 Globally   the  number  of   interconnection  is   very  important.    The  more
 
 Globally   the  number  of   interconnection  is   very  important.    The  more

-interconnection links there are, the  faster the \makhoul{} strategy is because
-it distributes quickly significant amount of load even if this is unfair between
+the interconnection links are, the  faster the \makhoul{} strategy is because
+it distributes quickly significant amount of load, even if this is unfair, between

 all the  neighbors.  In opposition,  the \besteffort{} strategy  distributes the
 load fairly so this strategy is better for low connected strategy.
 
 
 all the  neighbors.  In opposition,  the \besteffort{} strategy  distributes the
 load fairly so this strategy is better for low connected strategy.
 
 

-\subsubsection{With the virtual load extension with the load initially on only
-  one node}
+\subsubsection{Virtual load}
+
+The influence of virtual load is most of the time really significant compared to
+the  same configuration  without  it. Sometimes  it  has no  effect  but {\bf  A
+  VERIFIER} it has never a negative effect on the load balancing we tested.
+
+On Figure~\ref{fig.results1}, when the load is  initially on one node, it can be
+noticed that the  average idle times are generally longer  with the virtual load
+than without  it. This  can be explained  by the  fact that, with  virtual load,
+processors  will exchange all  the load  they need  to exchange  as soon  as the
+virtual load has been balanced  between all the processors. So consequently they
+cannot  compute  at  the  beginning.  This is  especially  noticeable  when  the
+communication are slow (on the left part of Figure ~\ref{fig.results1}.

 
 

-Dans ce cas légère amélioration de la cvg. max.  Temps moyen de cvg. amélioré,
-mais plus de temps passé en idle, surtout quand les comms coutent cher.
+%Dans ce cas  légère amélioration de la cvg. max.  Temps  moyen de cvg. amélioré,
+%mais plus de temps passé en idle, surtout quand les comms coutent cher.

 
 

-\subsubsection{The \besteffort{} strategy with an initial random load
-  distribution, and larger platforms}
+%\subsubsection{The \besteffort{} strategy with an initial random load
+%  distribution, and larger platforms}

 
 

-Mêmes conclusions pour line et hcube.
-Sur tore, BE se fait exploser quand les comms coutent cher.
+%In 
+%Mêmes conclusions pour line et hcube.
+%Sur tore, BE se fait exploser quand les comms coutent cher.

 
 

-\FIXME{virer les 1024 ?}
+%\FIXME{virer les 1024 ?}

 
 

-\subsubsection{With the virtual load extension with an initial random load
-  distribution}
+%\subsubsection{With the virtual load extension with an initial random load
+%  distribution}

 
 

-Soit c'est équivalent, soit on gagne -> surtout quand les comms coutent cher et
-qu'il y a beaucoup de voisins.
+%Soit c'est équivalent, soit on gagne -> surtout quand les comms coutent cher et
+%qu'il y a beaucoup de voisins.

 
 \subsubsection{The $k$ parameter}
 \label{results-k}
 
 
 \subsubsection{The $k$ parameter}
 \label{results-k}
 

-Dans le cas où les comms coutent cher et ou BE se fait avoir, on peut ameliorer
-les perfs avec le param k.
+As  explained  previously when  the  communication  are  slow the  \besteffort{}
+strategy is efficient. This is due to the fact that it tries to balance the load
+fairly and consequently  a significant amount of the  load is transfered between
+processors.  In this situation, it is possible to reduce the convergence time by
+using  the leveler  parameter  (parameter  $k$).  The  advantage  of using  this
+solution is particularly efficient when the initial load is randomly distributed
+on  the nodes with  torus and  hypercube topology  and slow  communication. When
+virtual load  mechanism is used,  the effect of  this parameter is  also visible
+with the same condition.
+
+
+
+\subsubsection{With integer load}
+
+We also performed  some experiments with integer load instead  of load with real
+value.  In  this case, the  results have globally  the same behavior.   The most
+intereting  result, from  our point  of view,  is that  the virtual  mode allows
+processors in a line topology to converge to the uniform load balancing. Without
+the virtual  load, most  of the time,  processors converge  to what we  call the
+``stairway effect'', that  is to say that  there is only a difference  of one in
+the load of each processor and its neighbors (for example with 10 processors, we
+obtain 10 9 8 7 6 6 7 8 9 10 instead of 8 8 8 8 8 8 8 8 8 8).

 
 

-\subsubsection{With integer load, 1 ou N}
+%Cas normal, ligne -> converge pas (effet d'escalier).
+%Avec vload, ça converge.

 
 

-Cas normal, ligne -> converge pas (effet d'escalier).
-Avec vload, ça converge.
+%Dans les autres cas, résultats similaires au cas réel: redire que vload est
+%intéressant.

 
 

-Dans les autres cas, résultats similaires au cas réel: redire que vload est
-intéressant.
+\FIXME{ajouter une courbe avec l'équilibrage en entier}

 
 \FIXME{virer la metrique volume de comms}
 
 
 \FIXME{virer la metrique volume de comms}