[Swift-commit] r3946 - text/parco10submission

[noreply at svn.ci.uchicago.edu]

Author: wozniak
Date: 2011-01-10 13:58:53 -0600 (Mon, 10 Jan 2011)
New Revision: 3946

Modified:
   text/parco10submission/paper.tex
Log:
Minor corrections


Modified: text/parco10submission/paper.tex
===================================================================
--- text/parco10submission/paper.tex	2011-01-10 19:35:25 UTC (rev 3945)
+++ text/parco10submission/paper.tex	2011-01-10 19:58:53 UTC (rev 3946)
@@ -184,7 +184,7 @@
 %external programs on clusters, grids and other parallel platforms, providing
 %automated site selection, data management, and reliability.
 
-We choose to make the Swift language purely functional (i.e., all operations
+We chose to make the Swift language purely functional (i.e., all operations
 have a well-defined set of inputs and outputs, all variables are write-once,
 and no script-level side effects are permitted by the language) in order to prevent the difficulties that
 arise from having to track side effects to ensure determinism in complex
@@ -1280,8 +1280,8 @@
 \subsection{Simulation of glass cavity dynamics and thermodynamics.}
 
 Many recent theoretical chemistry studies of the glass transition in model systems have focused
-on calculating from theory or simulation what is known as the ÓMosaic
-lengthÓ. Glen Hocky of the Reichman Group at Columbia is evaluating a new
+on calculating from theory or simulation what is known as the Mosaic
+length. Glen Hocky of the Reichman Group at Columbia is evaluating a new
 cavity method \cite{GlassMethods_2008} for measuring this length scale, where particles are
 simulated by molecular dynamics or Monte Carlo methods within cavities
 having amorphous boundary conditions.
@@ -1401,7 +1401,7 @@
      \includegraphics[scale=\plotscale]{plots/sleep} \\
       Test A.
       Application CPU utilization for 3 task durations
-      (in seconds) with up to 200 concurrent processes on an 8-core
+      (in seconds) with up to 200 concurrent processes on an 16-core
       local host. &
       Test B.
       Application CPU utilization for 3 task durations
@@ -1425,7 +1425,7 @@
 
 Previously published measurements of Swift performance performance on several scientific applications provide evidence that its parallel distributed programming model can be implemented with sufficient scalability and efficiency to make it a practical tool for large-scale parallel application scripting.
 
-The performance of Swift submitting jobs over the wide area network from UChicago to the TeraGrid Ranger cluster at TACC are shown in Figure~\ref{SEMplots} (from \cite{CNARI_2009}), which shows an SEM workload of 131,072 jobs for 4 brain regions and two experimental conditions. This workflow completed in approximately 3 hours.  The logs from the {\tt swift\_plot\_log} utility show the high degree of concurrent overlap between job execution and input and output file staging to remote computing resources. 
+The performance of Swift submitting jobs over the wide area network from UChicago to the TeraGrid Ranger cluster at TACC are shown in Figure~\ref{SEMplots} (from \cite{CNARI_2009}), which shows an SEM workload of 131,072 jobs for 4 brain regions and two experimental conditions. This workflow completed in approximately 3 hours.  The logs from the {\tt swift\_plot\_log} utility show the high degree of concurrent overlap between job execution and input and output file staging to remote computing resources.
 The workflows were developed on and submitted (to Ranger) from a single-core Linux workstation at UChicago running an Intel Xeon 3.20-GHz CPU. Data staging was performed using the Globus GridFTP protocol and job execution was performed over the Globus GRAM~2 protocol.
 During the third hour of the workflow, Swift achieved very high utilization of the 2,048 allocated processor cores and a steady rate of input and output transfers. The first two hours of the run were more bursty, due to fluctuating grid conditions and data server loads.