问题
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.
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.
I am not sure there are no caveats. I am also not sure what to do about measuring space complexity.
I am learning to program in C++. Are there humane tools to achieve what I am trying to do?
回答1:
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.
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)
回答2:
It is operating system and compiler specific (so implementation specific). You could use profiling tools, you could use timing tools, etc.
On Linux, see time(1), time(7), perf(1), gprof(1), pmap(1), mallinfo(3) and proc(5) and about Invoking GCC.
See also this. In practice, be sure that your runs are lasting long enough (e.g. at least one second of time in a process).
Be aware that optimizing compilers can transform drastically your program. See CppCon 2017: Matt Godbolt talk “What Has My Compiler Done for Me Lately? Unbolting the Compiler's Lid”
回答3:
Talking from an architecture point of view, you can also benchmark your C++ code using different architectural tools such as Intel Pin, perf tool. You can use these tools to study the architecture dependency of your code. For example, you can compile your code for different level of optimizations and check the IPC/CPI, cache accesses and load-store accesses. You can even check if your code is suffering a performance hit due to library functions. The tools are powerful and can give you potentially huge insights into your code.
You can also try disassembling your code and study where your code spends most of the time and try and optimize that. You can look at different techniques to ensure that the frequently accessed data remains in the cache and thus ensure a high hit rate.
Say, you realize that your code is heavily dominated by loops, you can run your code for different loop bounds and check for the metrics in 2 cases. For example, set the loop bound for 100,000 and find the desired performance metric 'X' and then set the loop bound for 200,000 and find the performance metric 'Y'. Now,calculate Y-X. This will give you a much better insight into the behavior of the loops because by subtracting the two metrics, you have effectively removed the static effects of the code.
Say, you run your code for 10 times and with different user input size. You can maybe find the runtime per user input size and then sort this new metric in ascending order, remove the first and the last value(to remove the outliers) and then take the average. Finally, find the Coefficient of variance to understand how the run times behave.
On a side note, more often than not, we end up using the term 'average' or 'arithmetic mean' rashly. Look at the metric you plan to average and look at harmonic means, arithmetic means and geometric means in each of the cases. For example,finding the arithmetic mean for rates will give you incorrect answers. Simply finding arithmetic means of two events which do not occur equally in time can give incorrect results. Instead, use weighted arithmetic means.
来源：https://stackoverflow.com/questions/49044422/how-can-i-benchmark-the-performance-of-c-code