From: Arnaud Giersch Date: Thu, 10 Jan 2013 22:04:57 +0000 (+0100) Subject: A few updates. X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/loba-papers.git/commitdiff_plain/1221c00d8b41a6532556dd303cef9666a8fab93c?ds=inline A few updates. --- diff --git a/supercomp11/biblio.bib b/supercomp11/biblio.bib index 70a6f87..5e721a1 100644 --- a/supercomp11/biblio.bib +++ b/supercomp11/biblio.bib @@ -1,3 +1,20 @@ +@Article{10.1109/TPDS.2005.2, + author = {Jacques M. Bahi and Sylvain Contassot-Vivier and + Raphael Couturier and Flavien Vernier}, + title = {A Decentralized Convergence Detection Algorithm for + Asynchronous Parallel Iterative Algorithms}, + journal = {IEEE Transactions on Parallel and Distributed + Systems}, + volume = {16}, + number = {1}, + issn = {1045-9219}, + year = {2005}, + pages = {4-13}, + doi = {10.1109/TPDS.2005.2}, + publisher = {IEEE Computer Society}, + address = {Los Alamitos, CA, USA}, +} + @InProceedings{bahi+giersch+makhoul.2008.scalable, author = {Jacques M. Bahi and Arnaud Giersch and Abdallah Makhoul}, diff --git a/supercomp11/supercomp11.tex b/supercomp11/supercomp11.tex index 01ca4f7..056e185 100644 --- a/supercomp11/supercomp11.tex +++ b/supercomp11/supercomp11.tex @@ -489,8 +489,18 @@ To summarize the different load balancing strategies, we have: % This gives us as many as $4\times 2\times 2 = 16$ different strategies. +\paragraph{End of the simulation} -\paragraph{Configurations} +The simulations were run until the load was nearly balanced among the +participating nodes. More precisely the simulation stops when each node holds +an amount of load at less than 1\% of the load average, during an arbitrary +number of computing iterations (2000 in our case). + +Note that this convergence detection was implemented in a centralized manner. +This is easy to do within the simulator, but it's obviously not realistic. In +a real application we would have chosen a decentralized convergence detection algorithm, like the one described in \cite{10.1109/TPDS.2005.2}. + +\paragraph{Platforms} In order to show the behavior of the different strategies in different settings, we simulated the executions on two sorts of platforms. These two @@ -511,6 +521,8 @@ bandwidth was fixed to 2.25~GB/s, with a latency of 500~$\mu$s. Then we derived each sort of platform with four different number of computing nodes: 16, 64, 256, and 1024 nodes. +\paragraph{Configurations} + The distributed processes of the application were then logically organized along three possible topologies: a line, a torus or an hypercube. We ran tests where the total load was initially on an only node (at one end for the line topology), @@ -552,12 +564,16 @@ time. \paragraph{Metrics} +In order to evaluate and compare the different load balancing strategies, we +choose to measure the following metrics: + \begin{description} -\item[\textbf{average idle time}] -\item[\textbf{average convergence date}] -\item[\textbf{maximum convergence date}] -\item[\textbf{data transfer amount}] relative to the total data amount +\item[\textbf{average idle time:}] +\item[\textbf{average convergence date:}] +\item[\textbf{maximum convergence date:}] +\item[\textbf{data transfer amount:}] relative to the total data amount \end{description} +\FIXME{dire à chaque fois ce que ça représente, et motiver le choix} \subsection{Validation of our approaches} \label{Results}