A comparison of programming languages in economics

8 Jul

Interesting comparison of programming language speeds. Given that the big data world seems to be all about Python, I wonder if folks start doing complicated calculations over big data if they will move away from Python? SAS is apparently working on “Accelerators” to work on hadoop nodes which appear to address this same problem. They already have them for Databases and Db appliances.

The above makes sense if you consider that for the most part “big Data” is about folks doing simple calculations in parallel  over many data nodes.

The thread of comments below the article are also interesting.


There is a new NBER working paper with that title, by S. Borağan Aruoba and Jesus Fernandez-Villaverde. Here is the abstract:

We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++11, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration with grid search, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and comment on the strength and weakness of each language.

Here are their results:

1. C++ and Fortran are still considerably faster than any other alternative, although one needs to be careful with the choice of compiler.

2. C++ compilers have advanced enough that, contrary to the situation in the 1990s and some folk wisdom, C++ code runs slightly faster (5-7 percent) than Fortran code.

3. Julia, with its just-in-time compiler, delivers outstanding per formance. Execution speed is only between 2.64 and 2.70 times the execution speed of the best C++ compiler.

4. Baseline Python was slow. Using the Pypy implementation, it runs around 44 times slower than in C++. Using the default CPython interpreter, the code runs between 155 and 269 times slower than in C++.

5. However, a relatively small rewriting of the code and the use of Numba (a just-in-time compiler for Python that uses decorators) dramatically improves Python ’s performance: the decorated code runs only between 1.57 and 1.62 times slower than the best C++ executable.

6.Matlab is between 9 to 11 times slower than the best C++ executable. When combined with Mex files, though, the difference is only 1.24 to 1.64 times.

7. R runs between 500 to 700 times slower than C++ . If the code is compiled, the code is between 240 to 340 times slower.

8. Mathematica can deliver excellent speed, about four times slower than C++, but only after a considerable rewriting of the code to take advantage of the peculiarities of the language. The baseline version our algorithm in Mathematica is much slower, even after taking advantage of Mathematica compilation.

There are ungated copies and some discussion here.




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