===================================================================
RCS file: /home/cvs/OpenXM/doc/issac2000/homogeneous-network.tex,v
retrieving revision 1.12
retrieving revision 1.13
diff -u -p -r1.12 -r1.13
--- OpenXM/doc/issac2000/homogeneous-network.tex	2000/01/17 08:06:15	1.12
+++ OpenXM/doc/issac2000/homogeneous-network.tex	2000/01/17 08:50:56	1.13
@@ -1,4 +1,4 @@
-% $OpenXM: OpenXM/doc/issac2000/homogeneous-network.tex,v 1.11 2000/01/17 07:15:52 noro Exp $
+% $OpenXM: OpenXM/doc/issac2000/homogeneous-network.tex,v 1.12 2000/01/17 08:06:15 noro Exp $
 
 \subsection{Distributed computation with homogeneous servers}
 \label{section:homog}
@@ -34,9 +34,9 @@ Figure \ref{speedup}
 shows the speedup factor under the above distributed computation
 on Risa/Asir. For each $n$, two polynomials of degree $n$
 with 3000bit coefficients are generated and the product is computed.
-The machine is Fujitsu AP3000,
-a cluster of Sun connected with a high speed network and MPI over the
-network is used to implement OpenXM.
+The machine is FUJITSU AP3000,
+a cluster of Sun workstations connected with a high speed network 
+and MPI over the network is used to implement OpenXM.
 \begin{figure}[htbp]
 \epsfxsize=8.5cm
 \epsffile{speedup.ps}
@@ -56,7 +56,7 @@ the speedup is satisfactory if the degree is large and
 is not large, say, up to 10 under the above environment.
 If OpenXM provides operations for the broadcast and the reduction
 such as {\tt MPI\_Bcast} and {\tt MPI\_Reduce} respectively, the cost of 
-sending $f_1$, $f_2$ and gathering $F_j$ may be reduced to $O(log_2L)$
+sending $f_1$, $f_2$ and gathering $F_j$ may be reduced to $O(\log_2L)$
 and we can expect better results in such a case.
 
 \subsubsection{Competitive distributed computation by various strategies}