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  <h1>gprof Assignment</h1>

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  <h2>Due: Friday, May 4, 2007</h2>

  <p>This assignment is intended to give you a chance to demonstrate that you know how to profile code and find 
  performance bottlenecks. Choose 5-10 methods from some component of your project.  If you wrote code, use your 
  code.  Otherwise, pick any component you want, preferably aiming for one where performance will be important.  
  Answer the following questions and record your answers before profiling.</p>

  <ol>
    <li>Before running gprof, make the following predictions:</li>
    <ul>
      <li>Rank the functions from most frequently called to least frequently called</li>
      <li>Rank the functions in terms of time spent in that function ("self" time instead of cumulative time)</li>
    </ul>
    <li>Predict where the hot spots will be in the code you've chosen, and explain why.</li>

    <li>Compile your code without any optimizations and profile it with gprof. Save the output from gprof and 
    answer the questions below.</li>
    <li><strong>Extra Credit:</strong> Compile your code with optimizations (either -O2 or -fast will work, but 
    check the compiler man page for the differences) and then profile it again. Do the same tasks so that the 
    results are comparable. Save the output from gprof and answer the last question below</li>
    <li>Analyze the output from both runs, and answer the following questions:</li>
    <ul>
      <li>What did you do with the application when you ran your program with profiling? Your choice of activities 
      will likely affect what results you get, so it's important to perform a representative set of tasks.</li>
      <li>Where are the hot spots in your code? Were your predictions correct? Include hot spots in routines that 
      you worked with as well as those that you did not.</li>

      <li>Which functions would be good candidates for optimization changes? Why?</li>
      <li><strong>Extra Credit:</strong> Suppose someone made the statement "Having the compiler optimize your code 
      simply makes everything run faster. All functions get called the same number of times, and take the same 
      amount of time to execute relative to one another". Is this statement accurate? What evidence (from the gprof 
      output) do you have to support this? What evidence do you have to contradict this?</li>
    </ul>
  </ol>

  <p>Record the answers to the above questions (both pre- and post-gprof), and include a subset of your gprof 
  output. Include the sections of the call graph profile and the lines from the flat profile which contain the 
  functions mentioned in both your predictions and your analysis. If you did the optimized vs. unoptimized run, 
  include excerpts from both runs, labeled appropriately. Specify which level of optimization you used.</p>

  <h2>Using gprof</h2>

  <p>Here is a brief summary of how to use gprof.</p>

  <ul>
    <li>Recompile all your code for profiling. This means you should specify the <code>-pg</code> option on 
    the compile and link lines.</li>

    <li>Run the program. Perform a carefully crafted set of tasks that will exercise the parts of the program you 
    want to evaluate. Having this be repeatable will make it much easier to evaluate any changes that you make to 
    optimize the code. It must terminate normally (either by returning from main or calling <code>exit(3c)</code>)</li>
    <li>Run <code>gprof -c [executable]</code> to extract the profile.</li>
  </ul>

  <p>The O'Reilly book 
  <a href="http://www.amazon.com/Performance-Computing-Architectures-Optimization-Benchmarks/dp/156592312X">

  High Performance Computing</a> by Kevin Dowd and Charles Severance has a section on interpreting the output 
  from gprof.  Or see a TA!</p>

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