Changeset 3185

Timestamp:

Jun 20, 2006, 1:54:04 PM (18 years ago)

Author:

linda

Message:

Updated shallow water equation to broadcast timestep after flux
calculation

Location:

inundation/parallel

Files:

• documentation/code/RunParallelSwMerimbulaMetis.py (modified) (1 diff)
• documentation/results.tex (modified) (3 diffs)
• parallel_shallow_water.py (modified) (5 diffs)

inundation/parallel/documentation/code/RunParallelSwMerimbulaMetis.py

@@ -85 +85 @@
     # Read in the test files
 
-    filename = 'merimbula_10785_1.tsh'
+    filename = 'parallel/merimbula_10785_1.tsh'
 
     # Build the whole mesh

inundation/parallel/documentation/results.tex

@@ -14 +14 @@
 \section{Advection, Rectangular Domain}
 
-The first example looked at the rectangular domain example given in Section \ref{sec:codeRPA}, excecpt that we changed the finaltime time to 1.0 (\code{domain.evolve(yieldstep = 0.1, finaltime = 1.0)}).
+The first example looked at the rectangular domain example given in Section \ref{subsec:codeRPA}, excecpt that we changed the finaltime time to 1.0 (\code{domain.evolve(yieldstep = 0.1, finaltime = 1.0)}).
 
 For this particular example we can control the mesh size by changing the parameters \code{N} and \code{M} given in the following section of code taken from
- Section \ref{sec:codeRPA}.
+ Section \ref{subsec:codeRPA}.
 
 \begin{verbatim}
@@ -89 +89 @@
 the Merimbula test problem. Inother words, we ran the code given in Section
 \ref{subsec:codeRPMM}, except the final time was reduced to 10000
-(\code{\label{subsec:codeRPMM}). The results are given in Table \ref{tbl:rpm}.
+(\code{finaltime = 10000}). The results are given in Table \ref{tbl:rpm}.
 These are good efficiency results, especially considering the structure of the
-Merimbula mesh. Note that since we are solving an advection problem the amount
-of calculation done on each triangle is relatively low, when we more to other
-problems that involve more calculations we would expect the computation to
-communication ratio to increase and thus get an increase in efficiency.
+Merimbula mesh. 
+%Note that since we are solving an advection problem the amount of calculation
+%done on each triangle is relatively low, when we more to other problems that
+%involve more calculations we would expect the computation to communication ratio to increase and thus get an increase in efficiency.
 
 \begin{table} 
 \caption{Parallel Efficiency Results for the Advection Problem on the
-  Merimbula Mesh {\tt N} = 160, {\tt M} = 160.\label{tbl:rpm}}
+  Merimbula Mesh.\label{tbl:rpm}}
 \begin{center}
 \begin{tabular}{|c|c c|}\hline
@@ -109 +109 @@
 \end{center}
 \end{table}
+
+\section{Shallow Water, Merimbula Mesh}
+
+The final example we looked at is the shallow water equation on the
+Merimbula mesh. We used the code listed in Section \ref{subsec:codeRPSMM}. The
+results are listed in Table \ref{tbl:rpsm}. The efficiency results are not as
+good as initally expected so we profiled the code and found that
+the problem is with the \code{update_boundary} routine in the {\tt domain.py}
+file. On one processor the \code{update_boundary} routine accounts for about
+72\% of the total computation time and unfortunately it is difficult to
+parallelise this routine. When metis subpartitions the mesh it is possible
+that one processor will only get a few boundary edges (some may not get any)
+while another processor may contain a relatively large number of boundary
+edges. The profiler indicated that when running the problem on 8 processors,
+Processor 0 spent about 3.8 times more doing the \code{update_boundary}
+calculations than Processor 7. This load imbalance reduced the parallel
+efficiency. 
+
+Before doing the shallow equation calculations on a larger number of
+processors we recommend that the \code{update_boundary} calculations be
+optimised as much as possible to reduce the effect of the load imbalance.
+
+ 
+\begin{table} 
+\caption{Parallel Efficiency Results for the Shallow Water Equation on the
+  Merimbula Mesh.\label{tbl:rpsm}}
+\begin{center}
+\begin{tabular}{|c|c c|}\hline
+$n$ & $T_n$ (sec) & $E_n (\%)$ \\\hline
+1 & 7.04 & \\
+2 & 3.62 & 97 \\
+4 & 1.94 & 91 \\
+8 & 1.15 & 77 \\\hline
+\end{tabular}
+\end{center}
+\end{table}

inundation/parallel/parallel_shallow_water.py

@@ -29 +29 @@
 from pyvolution.shallow_water import *
 from Numeric import zeros, Float, Int, ones, allclose, array
-from pypar_dist import pypar
-
+#from pypar_dist import pypar
+import pypar
 
 class Parallel_Domain(Domain):
@@ -98 +98 @@
         """
 
-
-        Domain.update_timestep(self, yieldstep, finaltime)
+        #LINDA:
+        # Moved below so timestep is found before doing update
+        
+        #Domain.update_timestep(self, yieldstep, finaltime)
 
         import time
@@ -118 +120 @@
         self.communication_broadcast_time += time.time()-t0
 
-
+        # LINDA:
+        # Moved timestep to here
+        
+        Domain.update_timestep(self, yieldstep, finaltime)
 
 
@@ -124 +129 @@
         """Calculate local timestep
         """
+
+        # LINDA: Moved below so timestep is updated before
+        # calculating statistic
         
         #Compute minimal timestep on local process
-        Domain.update_timestep(self, yieldstep, finaltime)
+        #Domain.update_timestep(self, yieldstep, finaltime)
 
         pypar.barrier()
@@ -170 +178 @@
 
         self.timestep = self.global_timestep[0]
-
+        
+        # LINDA:
+        # update local stats now
+        
+        #Compute minimal timestep on local process
+        Domain.update_timestep(self, yieldstep, finaltime)
 
     #update_timestep = update_timestep_1