# HG changeset patch
# User Sean Halle <seanhalle@yahoo.com>
# Date 1345130263 25200
# Node ID 196871d9eaefc1ea43e6a4bfe56188c0f1ced998
# Parent  bb5df2b662dd114fac9dec01c44e74e98a332d86
per tuning -- chgd authors to anonymous

diff -r bb5df2b662dd -r 196871d9eaef 0__Papers/Holistic_Model/Perf_Tune/latex/Holistic_Perf_Tuning.pdf
Binary file 0__Papers/Holistic_Model/Perf_Tune/latex/Holistic_Perf_Tuning.pdf has changed
diff -r bb5df2b662dd -r 196871d9eaef 0__Papers/Holistic_Model/Perf_Tune/latex/Holistic_Perf_Tuning.tex
--- a/0__Papers/Holistic_Model/Perf_Tune/latex/Holistic_Perf_Tuning.tex	Wed Aug 15 09:42:57 2012 -0700
+++ b/0__Papers/Holistic_Model/Perf_Tune/latex/Holistic_Perf_Tuning.tex	Thu Aug 16 08:17:43 2012 -0700
@@ -51,16 +51,19 @@
 %MOIRAI: MOdel for Integrated Runtime Analysis through Instrumentation
 \title{Integrated Performance Tuning Using Semantic Information Collected by Instrumenting the Language Runtime}
 
-\authorinfo{Nina Engelhardt}
-           {TU Berlin}
-           {nengel@mailbox.tu-berlin.de}
-\authorinfo{Sean Halle}
-           {Open Source Research Institute}
-           {seanhalle@opensourceresearchinstitute.org}
-\authorinfo{Ben Juurlink}
-           {TU Berlin}
-           {b.juurlink@tu-berlin.de}
+%\authorinfo{Nina Engelhardt}
+%           {TU Berlin}
+%           {nengel@mailbox.tu-berlin.de}
+%\authorinfo{Sean Halle}
+%           {Open Source Research Institute}
+%           {seanhalle@opensourceresearchinstitute.org}
+%\authorinfo{Ben Juurlink}
+%           {TU Berlin}
+%           {b.juurlink@tu-berlin.de}
 
+\authorinfo{Anonymous}
+           {No Institute}
+           {email@noplace}
 
 % This \maketitle command is required from ieeepes version 4.0, to make
 % ieeepes work correctly with newer LaTeX versions.
@@ -71,7 +74,7 @@
 Performance tuning is an important aspect of parallel programming. Yet when trying to pinpoint the causes of performance loss, often times insufficient knowledge  of the internal structure of the application and the runtime is available to understand how the observed patterns of performance have come to pass.
 A trend in parallel programming languages is towards models that capture more structural information about the application, in an effort to increase both performance and ease of programming. We propose using this structural information in  performance tuning tools to make the causes of performance loss more readily apparent.
 Our work produces a universal, adaptable set of performance visualizations that integrates this extra application structure, via a new model of parallel computation. The visualizations clearly identify idle cores, and tie the idleness to causal interactions within the runtime and hardware, and from there to the parallelism constructs that constrained the runtime and hardware behavior, thereby eliminating guesswork.
-This approach can be used to instrument the runtime of any parallel programming model without modifying the application. As a case study, we applied it to the SSR message-passing model, and we walk through a tuning session on a large multi-core machine to illustrate how performance loss is identified and how hypotheses for the cause are generated. 
+This approach can be used to instrument the runtime of any parallel language or programming model without modifying the application. As a case study, we applied it to the SSR message-passing model, and we walk through a tuning session on a large multi-core machine to illustrate the improvements in identifying performance loss and generating hypotheses for the cause. 
 \end{abstract}