transaction blog
Sep 27, 2017 • 11 min read

Benchmark Visualization with LaTeX and gnuplot

Visualizing benchmark results is an important step in performance testing. In this blog post, we’re going to use LaTeX, gnuplot and Perl to convert raw performance data into a nice-looking PDF document.

We’re going to produce a script named run_benchmark.pl, which executes a set of tests and produces a PDF document with a set of nicely formated diagrams that illustrate the test results. Of course we cannot go through the script line-by-line, we all the major points are explained.

The produced PDF looks like in this example. The full source code is available in picotm’s picotm-perf repository.

Turning Raw Numbers into Actionable Data

During September your author was busy with the implementation of a new testing infrastructure for picotm’s unit tests and continuous integration.

Part of this infrastructure is a set of performance tests. Each test run a predefined workloads and generate a test result that gives information about the performance of the system. Picotm is a manager for system-level transactions, which means that the most interesting performance information is the number of transactions it’s able to perform per unit of time.

The first interesting information is the number of completed transactions per time unit. Our test constantly begins and commits transactions for a certain amount of time and measures the number of successfully committed transactions. This is the throughput measured in commits per second. The single-thread throughput mostly depends on the transaction manager’s overhead, so this information is relevant during optimization.

Occationally (or maybe frequently) the transaction manager stops, rolls back and restarts a transaction. This happens for error handling and conflict resolution. Restarting transactions can have quite a bit of overhead and minimizing the number of restarts will improve the system’s performance. Therefore the second interesting information is the number of restarted transactions per time, measured in restarts per second. Minimizing the number of restarts should have positive impact on throughput.

Our test program picotm-perf executes a number of load and store transactions in the main memory. The resulting output on the console looks like this:

0 60000 10000 0

These numbers are

  1. the thread id. 0 as there’s only one thread in this example.
  2. The number of milliseconds that the test ran. The equivalent of 60 seconds in this case.
  3. The number of commited transactions on this thread. 10000.
  4. And finally the number of restarts. There are no restarts because a single transaction cannot conflict.

This test program can be executed for various combinations of load and store operations and I/O patterns. This only depends on the command-line parameters.

Running all these combinations result is an endless list of such single-line performance results. This is unintuitive, so we have to feed the data into further processing stages.

The tools your author has picked are

You can find the source code of the script in the picotm-perf repository. If you want to use it for other performance visualization, all you have to replace is the test program, and adopt the data parser in the Perl script and the gnuplot instructions. The rest should be independent from the actual performance tests.

Generating the Raw Data

As mentioned before, test results are generated by picotm-perf. To generate larger amounts of test data, the benchmark script run_bechmark.pl calls picotm-perf with different combinations of parameters for the number of concurrent transactions, the number of load operations per transaction, the number of store operations per transaction, and the I/O pattern. It’s a set of nested loops in Perl.

foreach my $pattern ('random', 'sequential') {

    foreach my $nloads (0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100) {

        my $nstores = 100 - $nloads;

        foreach my $ncores (1 .. 4) {

            run_picotm_perf($pattern, $nloads, $nstores, $ncores);

        }
    }
}

sub run_picotm_perf() {

    my ($pattern, $nloads, $nstores, $ncores) = @_;

    return `picotm-perf -T 60000 -P $pattern -L $nloads -S $nstores -t $ncores`;
}

The example code is mostly self-explaining. It executes the function run_picotm_perf() for all different combinations of I/O patterns, numbers of loads and stores, and number of concurrent transactions / processor cores.

The function run_picotm_perf() simply runs the external test program with the given parameters and returns the resulting output string of the format we’ve seen before.

Generating the Diagram Data

With the raw data at hand, we can now generate the information required by gnuplot to create the diagrams. The question is what our diagrams shall tell us.

We are interested in the number of commits and restarts per second for each combination of I/O pattern and loads and stores. For each of these combinations, we’re also interested in how the values change if we add or remove concurrent transactions in the workload.

So we want a single diagram for each combination of I/O pattern, loads and stores, and have the number of transactions change along each diagram’s x axis. The y axis’ display the result.

We can build this by replacing the inner-most loop of the previous example by the function generate_dat(), which is shown below.

sub generate_dat {

    my ($pattern, $nloads, $nstores) = @_;

    foreach my $ncores (1 .. 4) {

        my $result_string = run_picotm_perf($pattern, $nloads, $nstores, $ncores);

        my dat_string

        my $ncommits = 0;
        my $nretries = 0;

        my @lines = split /\n/, $result_string;

        foreach my $line (@lines) {

            # <thread id> <nmsecs> <ncommits> <nretries>
            $line =~ m/^(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s*$/ or die;

            # Sum up normalized results
            $ncommits += $3 * 1000 / $2;
            $nretries += $4 * 1000 / $2;
        }

        dat_string .= "\n" . "$ncores $ncommits $nretries";
    }

    return dat_string;
}

This code produces the data for a single diagram, but it’s probably non-obvious what happens.

First of all the function runs the test program 4 times: with 1, 2, 3, or 4 concurrent transactions. Each run produces a result that is stored in result_string.

For each test run, the result string is parsed line by line for the number of seconds that the test ran, the number of committed transactions and the number of restarted transactions. The result numbers are normalized by time and added up. The result is appended to the string dat_string in the following format.

1 123400 0
2 12300  100
3 1200   1200
4 100    12300

Each line in dat_string now contains the number of concurrent transactions, the number of committed transactions per second, and the number of restarted transactions per second. This is what we want to display in each diagram and it’s in a format that gnuplot understands.

Generating a LaTeX document

At this point we have performed a large number of tests for different combinations of I/O patterns, loads and stores, and for each combination we know how the results change when the number of concurrent transactions increases.

In principle the hardest work is done and all that’s left to do is to put the data into a nice format.1 As mentioned before, we use LaTeX and gnuplot for the data visualization. It’s fair to say that both programs are fantastic in terms of features, but less than optimal in terms of usability. Both have a file format that is rather inelegant and breaks easily. It’s something most people write and debug once, and then try to leave alone.

The input for gnuplot is a format that describes the resulting diagram. It allows to connect the input data, which we generated in the previous step, to the individual axes, and allows for configuring colors and style of the graph. The x axis on each of our diagrams shows the number of concurrent transactions. The y axis on the left shows the number of commits per second; the y axis on the right shows the number of restarts per second. Running gnuplot on each set of input data produces the corresponding image of the diagram.

We won’t go through the details of the LaTeX document source code here, but your author likes to highlight the use of LaTeX’ gnuplot package. The gnuplot tool typically gets it input from an external file. One can run gnuplot to generate an image file from such an external file and then reference the image’s filename in the LaTeX source code.

The gnuplot package for LaTeX simplifies this significantly. It allows for storing the gnuplot diagram description directly within the LaTeX document. The gnuplot tool is run automatically by the package while processing the LaTeX document and the resulting image is included where the description was located before. This does away with all the file-name juggling. Your author appreciated the gnuplot package a lot while working on the benchmark script.

The rest of the LaTeX document is set in the standard format, which is widely documented on the web. The end result is a file named results.tex, which can be converted into a PDF document.

Generating a PDF Document

The PDF output is produced by the LaTeX tool pdflatex. Running LaTeX tools seems just as arkward as writing LaTeX documents. We have to run pfdlatex 3 times on results.tex to get a correct PDF document. The benchmark script does this internally.

Ideas for Future Improvements

Finally we have a PDF document named results.pdf. With picotm’s current implementation the performance numbers don’t have much meaning yet, but from here we can easily change and optimize and compare different versions against each other.

For the benchmark script itself, your author has a number of ideas.

Of course, more combinations of our test parameters are always possible. Adding these is a low-hanging fruit.

Since we already have a lot of performance data, we could archive it for future use. Then a user could point the script to a certain directory with test results and have it generate performance diagrams from these.

With the old results stored away, the script could also compute the differences between different test runs, visualize them, and automatically warn about performance regressions above a certain threshold.

Summary

In this blog post, we’ve developed a script for creating and visualizing performance data.

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Footnotes

  1. One could argue that collecting the data is easy, but writing LaTeX source files is hard. :p 

Post by: Thomas Zimmermann


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