Celero - Easy Benchmark Charts for your C++ Algorithms

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Introduction

Developing consistent and meaningful benchmark results for code is a complex task. In my first article (Celero - A C++ Benchmark Authoring Library), I discussed how to use Celero to measure the performance of algorithms in C++. Here, I will discuss how to use Celero to understand how a given algorithm scales with respect to its problem size.

This article assumes a basic knowledge of Celero and intermediate knowledge of C++.

Background

It has been noted many times that writing an algorithm to solve small problems is relatively easy. "Brute force" methods tend to function just as well as more graceful approaches. However, as the size of data increases, truly effective algorithms scale their performance to match.

Theoretically, the best we can hope for with an algorithm is that is scales lineally (Order N, O(N) complexity) with respect to the problem size. That is to say that if the problem set doubles, the time it takes for the algorithm to execute doubles. While this seems obvious, it is often an elusive goal.

Even well performing algorithms eventually run into problems with available memory or CPU cache. When making decisions within our software about algorithms and improvements to existing code, only through measurement and experimentation can we know our complex algorithms perform acceptably.

Using the code

While Celero offers simple benchmarking of code and algorithms, it also offers a more complex method or directly producing performance graphs of how the benchmarks change with respect to some independent variable, referred to here as the Problem Set.

Within Celero, a test fixture can push integers into a ProblemSetValues vector which allows for the fixture's own SetUp function to scale a problem set for the benchmarks to run against. For each value pushed into the ProblemSetValues vector, a complete set of benchmarks is executed. These measured values are then stored and can be written out to a CSV file for easy plotting of results.

To demonstrate, we will study the performance of three common sorting algorithms: BubbleSort, SelectionSort, and std::sort. (The source code to this demo is distributed with Celero, available on GitHub.)  First, we will write a test fixture for Celero.

class DemoSortFixture : public celero::TestFixture
{
public:
    DemoSortFixture()
    {
        // We will run some total number of sets of tests all together. 
        // Each one growing by a power of 2.
        const int totalNumberOfTests = 12;

        for(int i = 0; i < totalNumberOfTests; i++)
        {
            // ProblemSetValues is part of the base class and allows us to specify
            // some values to control various test runs to end up building a nice graph.
            this->ProblemSetValues.push_back(static_cast<int32_t>(pow(2, i+1)));
        }
    }

    /// Before each run, build a vector of random integers.
    virtual void SetUp(const int32_t problemSetValue)
    {
        this->arraySize = problemSetValue;

        for(int i = 0; i < this->arraySize; i++)
        {
            this->array.push_back(rand());
        }
    }

    /// After each run, clear the vector of random integers.
    virtual void TearDown()
    {
        this->array.clear();
    }

    std::vector<int> array;
    int arraySize;
};

The constructor defines the experiment size,  totalNumberOfTests This number can be changed at will to adjust how many values are pushed into the ProblemSetValues array. Celero uses this array internally when calling the fixture's SetUp() virtual function.

The SetUp() virtual function is called before each benchmark test is executed. When using a ProblemSetValues array, the function will be given a value that was previously pushed into the array within the constructor. The function's code can then decide what to do with it. Here, we are using the value to indicate how many elements should be in the array that we intend to sort. For each of the array elements, we simply add a pseudo-random integer.

Now for implementing the actual sorting algorithms. For the baseline case, I implemented the first sorting algorithm I ever learned in school: Bubble Sort. The code for bubble sort is straight forward.

// For a baseline, I'll choose Bubble Sort.
BASELINE_F(DemoSort, BubbleSort, DemoSortFixture, 0, 10000)
{
    for(int x = 0; x < this->arraySize; x++)
    {
        for(int y = 0; y < this->arraySize - 1; y++)
        {
            if(this->array[y] > this->array[y+1])
            {
                std::swap(this->array[y], this->array[y+1]);
            }
        }
    }
}

Celero will use the values from this baseline when computing a base lined measurement for the other two algorithms in the test group "DemoSort". However, when we run this at the command line, we will specify an output file. The output file will contain the measured number of seconds the algorithm took to execute on the given array size.

Next, we will implement the Selection Sort algorithm.

BENCHMARK_F(DemoSort, SelectionSort, DemoSortFixture, 0, 10000)
{
    for(int x = 0; x < this->arraySize; x++)
    {
        auto minIdx = x;

        for(int y = x; y < this->arraySize; y++)
        {
            if(this->array[minIdx] > this->array[y])
            {
                minIdx = y;
            }
        }

        std::swap(this->array[x], this->array[minIdx]);
    }
}

BENCHMARK_F(DemoSort, stdSort, DemoSortFixture, 0, 10000)
{
    std::sort(this->array.begin(), this->array.end());
}

Results

This test was ran on a 4.00 GHz AMD with four cores, eight logical processors, and 32 GB of memory. (Hardware aside, the relative performance of these algorithms should be the same on any modern hardware.) 

Celero outputs timing and benchmark references for each test automatically. However, to write to an output file for easy plotting, simply specify an output file on the command line.

>celeroDemo results.csv

While not particularly surprising std::sort is by far the best option with any meaningful problem set size. The results are summarized in the following table output written directly by Celero:

DemoTransform,,,,,,,,,,,,
,1,2,4,8,16,32,64,128,256,512,1024,2048
ForLoop,0.0084,0.0125,0.0186,0.0315,0.0595,0.133,0.2353,0.439,0.8607,1.7392,3.4112,7.0522
SelfForLoop,0.0093,0.012,0.0186,0.0304,0.0567,0.1265,0.225,0.4194,0.8096,1.6094,3.2035,6.4252
SelfStdTransform,0.0112,0.0113,0.0167,0.0265,0.0478,0.119,0.1953,0.3788,0.7257,1.4189,2.8082,5.5696
SelfStdTransformLambda,0.0112,0.0111,0.0167,0.0264,0.0474,0.119,0.1953,0.3822,0.7257,1.4192,2.8078,5.5707
StdTransform,0.0074,0.0111,0.0167,0.0288,0.0539,0.1153,0.2046,0.3832,0.7411,1.4555,2.8884,5.9487
StdTransformLambda,0.0074,0.0111,0.0167,0.0288,0.0539,0.1153,0.2046,0.3832,0.7411,1.4556,2.8915,5.9451

The data shows first the test group name. Next, all of the data sizes are output. Then each row shows the baseline or benchmark name and the corresponding time for the algorithm to complete measured in useconds. This data, in CSV format, can be directly read by programs such as Microsoft Excel and plotted without any modification.

Note the table uses a log(2) scale for both axis. 

The point here is not that std::sort is better than more elementary sorting methods, but how easily measureable results can be obtained. In making such measurements more accessible and easier to code, they can become part of the way we code just as automated testing has become.

Test early and test often!

Points of Interest

* Because I like explicitness as much as the next programmer, I want to note that the actual sorting algorithm used by std::sort is not defined in the standard, but references cite Introsort as a likely contender for how an STL implementation would approach std::sort. http://en.wikipedia.org/wiki/Introsort.

When choosing a sorting algorithm, start with std::sort and see if you can make improvements from there.

Don't just trust your experience, measure your code!

Source Code Repository

The source code to this demo is distributed with Celero, available on GitHub.