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📁 Design and building parallel program
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<B> Next:</B> <A NAME=tex2html3309 HREF="node112.html" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node112.html"> Exercises</A>
<B>Up:</B> <A NAME=tex2html3307 HREF="node106.html" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node106.html">9 Performance Tools</A>
<B> Previous:</B> <A NAME=tex2html3301 HREF="node110.html" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node110.html">9.4 Tools</A>
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<H1><A NAME=SECTION03650000000000000000>9.5 Summary</A></H1>
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As discussed in Sections <A HREF="node31.html#secexper" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node31.html#secexper">3.5</A> and <A HREF="node32.html#secperfpt" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node32.html#secperfpt">3.6</A>,
parallel programming is an experimental discipline.  An analytic
performance model is an idealization of program behavior that must be
validated by comparison with empirical results.  This validation
process can reveal deficiencies in both the model and the parallel
program.  Performance analysis is most effective when guided by an
understanding rooted in analytic models, and models are most accurate
when calibrated with empirical data.
<P>
Because performance data tend to be complex and multidimensional,
performance tools are a vital part of any parallel programming
toolkit.  In this chapter, we have introduced basic techniques and
surveyed popular tools for collecting, transforming, and analyzing
performance data.  At the data collection level, we distinguished
between profiles, counters, and event traces.  Each approach has a
role to play in performance analysis: profile and counter data are
easier to obtain and to analyze, while traces can show fine detail.
At the data transformation and visualization levels, we have emphasized the
importance of data reduction techniques that are able to reduce raw performance
data to more meaningful and manageable quantities, and data
visualization tools that can facilitate the navigation of large
multidimensional data sets.
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<B> Next:</B> <A NAME=tex2html3309 HREF="node112.html" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node112.html"> Exercises</A>
<B>Up:</B> <A NAME=tex2html3307 HREF="node106.html" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node106.html">9 Performance Tools</A>
<B> Previous:</B> <A NAME=tex2html3301 HREF="node110.html" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/node110.html">9.4 Tools</A>
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<I>&#169 Copyright 1995 by <A href="msgs0.htm#6" tppabs="http://www.dit.hcmut.edu.vn/books/system/par_anl/tppmsgs/msgs0.htm#6">Ian Foster</a></I>
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