我开始认真地研究算法和数据结构，并且有兴趣学习如何比较实现 A&DT 的不同方式的性能.
I am starting to study algorithms and data structures seriously, and interested in learning how to compare the performance of the different ways I can implement A&DTs.
对于简单的测试，我可以获取某件事运行之前/之后的时间，运行那件事 10^5 次，然后平均运行时间.我可以按大小参数化输入，或对随机输入进行抽样，并获得运行时间与输入大小的列表.我可以将其输出为 csv 文件，然后将其输入到 Pandas 中.
For simple tests, I can get the time before/after something runs, run that thing 10^5 times, and average the running times. I can parametrize input by size, or sample random input, and get a list of running times vs. input size. I can output that as a csv file, and feed it into pandas.
我不确定是否有任何警告.我也不知道如何衡量空间复杂度.
I am not sure there are no caveats. I am also not sure what to do about measuring space complexity.
我正在学习用 C++ 编程.是否有人性化的工具来实现我的目标?
I am learning to program in C++. Are there humane tools to achieve what I am trying to do?
对代码进行基准测试并不容易.我发现最有用的是 Google 基准库. 即使您不打算使用它，它也可能是很高兴阅读一些例子.它有很多可能性来参数化测试，将结果输出到文件，甚至返回算法的大 O 符号复杂度(仅举几个例子).如果您熟悉 Google 测试框架，我建议您使用它.它还使编译器优化成为可能，因此您可以确保您的代码没有被优化掉.
Benchmarking code is not easy. What I found most useful was Google benchmark library. Even if you are not planning to use it, it might be good to read some of examples. It has a lot of possibilities to parametrize test, output results to file and even returning you Big O notation complexity of your algorithm (to name just few of them). If you are any familiar with Google test framework I would recommend you to use it. It also keeps compiler optimization possible to manage so you can be sure that your code wasn't optimized away.
在 CppCon 2015: Chandler Carruth "Tuning C++: Benchmarks,和 CPU 和编译器！天哪！".您可能犯的错误有很多见解(它还使用谷歌基准)
There is also great talk about benchmarking code on CppCon 2015: Chandler Carruth "Tuning C++: Benchmarks, and CPUs, and Compilers! Oh My!". There are many insights in possible mistake that you can make (it also uses google benchmark)
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