From fa3bc7818831f1d61bf711b84884a615d522a081 Mon Sep 17 00:00:00 2001 From: Arnaud Giersch Date: Mon, 17 Mar 2014 10:03:35 +0100 Subject: [PATCH 1/1] Remove spaces before colons. --- paper.tex | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/paper.tex b/paper.tex index 5e80312..dc963b5 100644 --- a/paper.tex +++ b/paper.tex @@ -200,7 +200,7 @@ The energy consumption by the processor consists of two power metrics: the dynamic and the static power. This general power formulation is used by many researchers~\cite{9,3,15,26}. The dynamic power of the CMOS processors $P_{dyn}$ is related to the switching activity $\alpha$, load capacitance $C_L$, -the supply voltage $V$ and operational frequency $f$ respectively as follow : +the supply voltage $V$ and operational frequency $f$ respectively as follow: \begin{equation} \label{eq:pd} P_\textit{dyn} = \alpha \cdot C_L \cdot V^2 \cdot f @@ -342,7 +342,7 @@ In our cluster there are 18 available frequency states for each processor from 2.5 GHz to 800 MHz, there is 100 MHz difference between two successive frequencies. For more details on the characteristics of the platform refer to table~(\ref{table:platform}). This lead to 18 run states for each program. We -use seven MPI programs of the NAS parallel benchmarks : CG, MG, EP, FT, BT, LU +use seven MPI programs of the NAS parallel benchmarks: CG, MG, EP, FT, BT, LU and SP. The average normalized errors between the predicted execution time and the real time (SimGrid time) for all programs is between 0.0032 to 0.0133. AS an example, we are present the execution times of the NAS benchmarks as in the @@ -360,7 +360,7 @@ it is linear see~\cite{17}. The relation between the energy and the performance is not straightforward. Moreover, they are not measured using the same metric. For solving this problem, we normalize the energy by calculating the ratio between the consumed energy with scaled frequency and the consumed energy -without scaled frequency : +without scaled frequency: \begin{multline} \label{eq:enorm} E_\textit{Norm} = \frac{ E_\textit{Reduced}}{E_\textit{Original}} \\ @@ -370,7 +370,7 @@ without scaled frequency : P_\textit{dyn} \cdot \left(T_1+\sum_{i=2}^{N}\frac{T_i^3}{T_1^2}\right) + P_\textit{static} \cdot T_1 \cdot N } \end{multline} -By the same way we can normalize the performance as follows : +By the same way we can normalize the performance as follows: \begin{equation} \label{eq:pnorm} P_\textit{Norm} = \frac{T_\textit{New}}{T_\textit{Old}} @@ -391,7 +391,7 @@ paper we are present a method to find the optimal scaling factor \emph S for optimize both energy and performance simultaneously without adding big overheads. Our solution for this problem is to make the optimization process have the same direction. Therefore, we inverse the equation of normalize -performance as follows : +performance as follows: \begin{equation} \label{eq:pnorm_en} P^{-1}_\textit{Norm} = \frac{ T_\textit{Old}}{ T_\textit{New}} @@ -495,7 +495,7 @@ in the MPI program. After obtaining the optimal scale factor from the EPSA algorithm. The program calculates the new frequency $F_i$ for each task proportionally to its time value $T_i$. By substitution of the EQ~(\ref{eq:s}) in the EQ~(\ref{eq:si}), we -can calculate the new frequency $F_i$ as follows : +can calculate the new frequency $F_i$ as follows: \begin{equation} \label{eq:fi} F_i = \frac{F_\textit{max} \cdot T_i}{S_\textit{optimal} \cdot T_\textit{max}} -- 2.39.5