10.7. Lab: Performance

In the sorting notes (see Sorting Algorithms) we took advantage of a few ideas to show how to do basic benchmarking to compare the various approaches.

  • using randomly-generated data
  • making sure each algorithm is working with the same data
  • making sure that we try a range of sizes to observe the effects of scaling
  • using a timer with sufficiently high resolution (the Stopwatch gives us measurements in milliseconds).

In this lab, you get your chance to learn a bit more about performance by comparing searches. The art of benchmarking is something that is easy to learn but takes a lifetime to master (to borrow a phrase from the famous Othello board game).

Most of the algorithms we cover in introductory courses tend to be polynomial in nature. That is, the execution time can be expressed as a polynomial function of the size of the data size \(n\). Examples include but are not limited to:

  • \(O(n)\) is linear time; often characterized by a single loop
  • \(O(n^2)\) is the time squared; often characterized by a nested loop
  • \(O(log\ n)\) is logarithmic (base 2) time; often characterized by a loop that repeatedly divides its work in half. The binary search is a well-known example.
  • \(O(n\ log\ n)\) is an example of a hybrid. Perhaps there is an outer loop that is linear and an inner loop that is logarithmic.

And there are way more than these shown here. As you progress in computing, you’ll come to know and appreciate these in greater detail.

In this lab, we’re going to look at a few different data structures and methods that perform searches on them and do empirical analysis to get an idea of how well each combination works. Contrasted with other labs where you had to write a lot of code, we’re going to give you some code to do all of the needed work but ask you to do the actual analysis and produce a basic table.

10.7.1. The Experiments

We’re going to measure the performance of data structures we have been learning about in lectures. For this lab, we’ll focus on:

In the interest of fairness, we are only going to look at the time it takes to perform the various search operations. We’re not going to count the time to randomly-generate the data and actually build the data structure. The reasoning is straightforward. We’re interested in the search time, which is completely independent of other aspects that may be at play. We’re not at all saying that the other aspects are unimportant but want to keep the assignment focus on search.

The experimental apparatus that we are constructing will do the following for each of the cases:

  • create the data structure (e.g. new array, new list, new set)
  • use a random seed seed, initialize a random generator that will generate n values.
  • insert the random values into the data structure . For the case of sets, which eliminate duplicates, it is entirely possible you will end up with a tiny fraction of a percent fewer than n values.
  • to measure the performance of any given search method, we need to perform a significant number of lookups (based on numbers in the random sequence) to ensure that we get an accurate idea of the average lookup time in practice. We’ll call this parameter, rep. We will spread out the values looked for by checking data elements that have indices at a regular interval throughout the array. The separation is m = n/rep when rep < n. We wrap around if rep > n.
  • We’ll start a Stopwatch just before entering the loop to perform the lookups.

10.7.2. Starter Project

You will probably find it convenient to start from our Arrays MonoDevelop solution. You can find this in projects/Arrays. You need this entire folder.

To make your life easier, we have put together a project within this solution, PerformanceLab that contains the code for all of the experiments you need to run. (That’s right, we’re giving you the code for the experiments, but you’re going to write some code to run the various experiments and then run for varying sizes of n.)

Here is the code for the first experiment, to test the performance of linear searching on integer arrays:

      public static long ExperimentIntArrayLinearSearch (int n, int rep, int seed)
         Stopwatch watch = new Stopwatch ();
         int[] data = new int[n];
         Sorting.IntArrayGenerate (data, seed);
         watch.Reset ();       
         watch.Start ();
         int m = Math.Max(1, n/rep);
         // perform the rep lookups
         for (int k=0, i=0; k < rep; k++, i=(i+m)%n) {
            Searching.IntArrayLinearSearch (data, data [i]);
         watch.Stop ();
         return watch.ElapsedMilliseconds;

Let’s take a quick look at how this experiment is constructed. We’ll also take a look at the other experiments but these will likely be presented in a bit less detail, except to highlight the differences:

  • On line 3, we create a Stopwatch instance. We’ll be using this to do the timing.
  • On lines 4-5, we are creating the data to be searched. Because we have already written this code in our sorting algorithms examples, we can use the project by taking advantage of project references and using the Sorting class name to access the method IntArrayGenerate() within this class. We take advantage of this in the other experiments.
  • Line 6 resets the stopwatch. It is not technically required; however, we tend to be in the habit of doing it, because we sometimes reuse the same stopwatch and want to make sure it is completely zeroed out. A call to Reset() ensures it is zero.
  • Line 7 actually starts the stopwatch. We are starting here as opposed to before line 3, because the random data generation has nothing to do with the actual searching of the array data structure.
  • Line 8 converts the number of repetitions into the increment in index values for each time.
  • Lines 10 through 12 are searching rep times for an item already known to be in the array.
  • Line 13 stops the stopwatch.
  • Line 14 returns the elapsed time in milliseconds between the Start() and Stop() method calls, which reflects the actual time of the experiment.

Each of the other experiments is constructed similarly. For linear search and binary search we use the methods created earlier. For the lists and the set we use the built-in Contains method to search. The list and set are directly initialized in their constructors from the array data.

You need to fill in the Main method:

  1. Write the code to parse command line args for the parameters rep and any number of values for n. For instance:

    mono PerformanceLab.exe 50 1000 10000 100000

    would generate the table shown below for 50 repetitions for each of the values of n, 1000, 10000, and 100000.

  2. Write the code to run each of the experiments for rep and a given value of n.

  3. Iterate through the values of n and print a table, something like the following, with the number of seconds calculated. Experiment and adjust the repetitions to get perceptible values. Our choice of 50 may not be appropriate with these n values.

         n   rep   linear    binary    list     set
      1000    50   ??.???    ??.???  ??.???  ??.???
     10000    50   ??.???    ??.???  ??.???  ??.???
    100000    50   ??.???    ??.???  ??.???  ??.???
The table would be longer if more values of n were entered on the command line.

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