![]() If you like the relative simplicity of the syntax, Julia is cleaner, simpler and just as fast in execution. Why does anyone bother with Java? If speed is the critical issue, there are faster alternatives.What justification is there for preferring R to Matlab, other than cost?.Why would anyone prefer Python, given that there is a much faster, free alternative in Julia, which is just as easy a language to program in?.So, as regards developing financial models and trading systems, my questions are (as before): It is barely faster than the old stalwart, Fortran, and only 1.5 – 3 times faster than up-and-coming rivals amongst the higher level languages (especially when you allow for hybrid programming to speed up the slowest algorithms). The baseline version of the algorithm in Mathematica is considerably slower.Ĭ++ still represents the benchmark for speed, but not by much. Mathematica is only about three times slower than C++, but only after a considerable rewriting of the code to take advantage of the peculiarities of the language.Similar numbers hold for Numba (a just-in-time compiler for Python that uses decorators) and Cython (a static compiler for writing C extensions for Python) in the Python ecosystem. For example, when combined with Mex files, Matlab is only 1.24 to 1.64 times slower than C++ and when combined with Rcpp, R is between 3.66 and 5.41 times slower. Hybrid programming and special approaches can deliver considerable speed ups.If the code is compiled, the code is between 243 to 282 times slower. R runs between 475 to 491 times slower than C++.Matlab is between 9 to 11 times slower than the best C++ executable.Using the default CPython interpreter, the code runs between 155 and 269 times slower than in C++. Using the Pypy implementation, it runs around 44 times slower than in C++. ![]() Execution speed is only between 2.64 and 2.70 times slower than the execution speed of the best C++ compiler. Julia delivers outstanding performance.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.C++ and Fortran are still considerably faster than any other alternative, although one needs to be careful with the choice of compiler.The conclusions from the study mirror my own thoughts on the subject very closely. They report the execution times of the codes in a Mac and in a Windows computer and briefly comment on the strengths and weaknesses of each language. Iteration with grid search, in each of the languages. Using the neoclassical growth model, the authors conduct a benchmark test in C++11, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R, implementing the same algorithm, value function Now comes a formal academic study of the topic in A Comparison of Programming Languages in Economics, Aruoba and Fernandez-Villaverde, 2014.
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