From c25c567d8c7358554605d785e62623b9fc7cda8e Mon Sep 17 00:00:00 2001 From: Arnaud Giersch Date: Tue, 27 May 2014 11:42:11 +0200 Subject: [PATCH] *a* homogeneous --- paper.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper.tex b/paper.tex index eb3cd18..da3fb81 100644 --- a/paper.tex +++ b/paper.tex @@ -136,7 +136,7 @@ the MPI program to choose the scaling factor. This algorithm has the ability to predict both energy consumption and execution time over all available scaling factors. The prediction achieved depends on some computing time information, gathered at the beginning of the runtime. We apply this algorithm to the NAS parallel benchmarks (NPB v3.3)~\cite{44}. Our experiments are executed using the simulator -SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183} over an homogeneous +SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183} over a homogeneous distributed memory architecture. Furthermore, we compare the proposed algorithm with Rauber and Rünger methods~\cite{3}. The comparison's results show that our algorithm gives better energy-time trade-off. @@ -682,7 +682,7 @@ trade-offs such as in BT and EP. In this paper, we have presented a new online scaling factor selection method that optimizes simultaneously the energy and performance of a distributed -application running on an homogeneous cluster. It uses the computation and +application running on a homogeneous cluster. It uses the computation and communication times measured at the first iteration to predict energy consumption and the performance of the parallel application at every available frequency. Then, it selects the scaling factor that gives the best trade-off -- 2.39.5