\newcommand{\VAR}[1]{\textit{#1}}
+\newcommand{\besteffort}{\emph{best effort}}
+\newcommand{\makhoul}{\emph{Makhoul}}
+
\begin{document}
\begin{frontmatter}
\author{Arnaud Giersch\corref{cor}}
\ead{arnaud.giersch@femto-st.fr}
-\address{FEMTO-ST, University of Franche-Comté\\
- 19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France}
+\address{%
+ Institut FEMTO-ST (UMR 6174),
+ Université de Franche-Comté (UFC),
+ Centre National de la Recherche Scientifique (CNRS),
+ École Nationale Supérieure de Mécanique et des Microtechniques (ENSMM),
+ Université de Technologie de Belfort Montbéliard (UTBM)\\
+ 19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France}
\cortext[cor]{Corresponding author.}
the most well known algorithm for which the convergence proof is given. From a
practical point of view, when a node wants to balance a part of its load to
some of its neighbors, the strategy is not described. In this paper, we
- propose a strategy called \emph{best effort} which tries to balance the load
+ propose a strategy called \besteffort{} which tries to balance the load
of a node to all its less loaded neighbors while ensuring that all the nodes
concerned by the load balancing phase have the same amount of load. Moreover,
asynchronous iterative algorithms in which an asynchronous load balancing
ensure the convergence, there is no indication or strategy to really implement
the load distribution. In other word, a node can send a part of its load to one
or many of its neighbors while all the convergence conditions are
-followed. Consequently, we propose a new strategy called \emph{best effort}
+followed. Consequently, we propose a new strategy called \besteffort{}
that tries to balance the load of a node to all its less loaded neighbors while
ensuring that all the nodes concerned by the load balancing phase have the same
amount of load. Moreover, when real asynchronous applications are considered,
\label{sec.besteffort}
In this section we describe a new load-balancing strategy that we call
-\emph{best effort}. First, we explain the general idea behind this strategy,
+\besteffort{}. First, we explain the general idea behind this strategy,
and then we describe some variants of this basic strategy.
\subsection{Basic strategy}
-The general idea behind the \emph{best effort} strategy is that each processor,
+The general idea behind the \besteffort{} strategy is that each processor,
that detects it has more load than some of its neighbors, sends some load to the
most of its less loaded neighbors, doing its best to reach the equilibrium
between those neighbors and himself.
Another load balancing strategy, working under the same conditions, was
previously developed by Bahi, Giersch, and Makhoul in
\cite{bahi+giersch+makhoul.2008.scalable}. In order to assess the performances
-of the new \emph{best effort}, we naturally chose to compare it to this anterior
+of the new \besteffort{}, we naturally chose to compare it to this anterior
work. More precisely, we will use the algorithm~2 from
\cite{bahi+giersch+makhoul.2008.scalable} and, in the following, we will
reference it under the name of Makhoul's.
\subsubsection{Load balancing strategies}
Several load balancing strategies were compared. We ran the experiments with
-the \emph{Best effort}, and with the \emph{Makhoul} strategies. \emph{Best
+the \besteffort{}, and with the \makhoul{} strategies. \emph{Best
effort} was tested with parameter $k = 1$, $k = 2$, and $k = 4$. Secondly,
each strategy was run in its two variants: with, and without the management of
\emph{virtual load}. Finally, we tested each configuration with \emph{real},
To summarize the different load balancing strategies, we have:
\begin{description}
-\item[\textbf{strategies:}] \emph{Makhoul}, or \emph{Best effort} with $k\in
+\item[\textbf{strategies:}] \makhoul{}, or \besteffort{} with $k\in
\{1,2,4\}$
\item[\textbf{variants:}] with, or without virtual load
\item[\textbf{domain:}] real load, or integer load
\FIXME{annoncer le plan de la suite}
-\subsubsection{The \emph{best effort} strategy}
+\subsubsection{The \besteffort{} strategy with the load initially on only one
+ node}
+
+Before looking at the different variations, we'll 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
+are respectively labeled ``b'' and ``a''.
+
+twice faster on lines
+almost equivalent on torus
+worse on hcubes
+
+-> interconnection
+
+plus c'est connecté, moins bon est BE car à vouloir trop bien équilibrer
+localement, le processeurs se perturbent mutuellement. Du coup, makhoul qui
+équilibre moins bien localement est moins perturbé par ces interférences.
-Looking at the graph on figure~\ref{fig.results1}, we can see that the
-\emph{best effort} strategy is not too bad.
+\subsubsection{With the virtual load extension with the load initially on only
+ one node}
-\FIXME{donner les premières conclusions}
-\FIXME{comparer be/makhoul -> be tient la route (parler du cas réel uniquement)}
+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{With the virtual load extension}
+\subsubsection{The \besteffort{} strategy with an initial random load
+ distribution, and larger platforms}
-\FIXME{valider l'extension virtual load -> c'est 'achement bien}
+Mêmes conclusions pour line et hcube.
+Sur tore, BE se fait exploser quand les comms coutent cher.
+
+\FIXME{virer les 1024 ?}
+
+\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.
\subsubsection{The $k$ parameter}
-\FIXME{proposer le -k -> ça peut aider dans certains cas}
+Dans le cas où les comms coutent cher et ou BE se fait avoir, on peut ameliorer
+les perfs avec le param k.
-\subsubsection{With an initial random distribution, and larger platforms}
+\subsubsection{With integer load, 1 ou N}
-\FIXME{dire quoi ici ?}
+Cas normal, ligne -> converge pas (effet d'escalier).
+Avec vload, ça converge.
-\subsubsection{With integer load}
+Dans les autres cas, résultats similaires au cas réel: redire que vload est
+intéressant.
-\FIXME{conclure avec la version entière -> on n'a pas l'effet d'escalier !}
+\FIXME{virer la metrique volume de comms}
-\FIXME{what about the amount of data?}
+\FIXME{ajouter une courbe ou on voit l'évolution de la charge en fonction du
+ temps : avec et sans vload}
-\FIXME{On constate quoi (vérifier avec les chiffres)?
-\begin{itemize}
-\item cluster ou grid, entier ou réel, ne font pas de grosses différences
-\item bookkeeping? améliore souvent les choses, parfois au prix d'un retard au démarrage
-\item makhoul? se fait battre sur les grosses plateformes
-\item taille de plateforme?
-\item ratio comp/comm?
-\item option $k$? peut-être intéressant sur des plateformes fortement interconnectées (hypercube)
-\item volume de comm? souvent, besteffort/plain en fait plus. pourquoi?
-\item répartition initiale de la charge ?
-\item integer mode sur topo. line n'a jamais fini en plain? vérifier si ce n'est
- pas à cause de l'effet d'escalier que bk est capable de gommer.
-\end{itemize}}
+% \begin{itemize}
+% \item cluster ou grid, entier ou réel, ne font pas de grosses différences
+% \item bookkeeping? améliore souvent les choses, parfois au prix d'un retard au démarrage
+% \item makhoul? se fait battre sur les grosses plateformes
+% \item taille de plateforme?
+% \item ratio comp/comm?
+% \item option $k$? peut-être intéressant sur des plateformes fortement interconnectées (hypercube)
+% \item volume de comm? souvent, besteffort/plain en fait plus. pourquoi?
+% \item répartition initiale de la charge ?
+% \item integer mode sur topo. line n'a jamais fini en plain? vérifier si ce n'est
+% pas à cause de l'effet d'escalier que bk est capable de gommer.
+% \end{itemize}}
% On veut montrer quoi ? :
