3 ms·
Miles Lubin and Iain Dunning gave an excellent presentation at MIT's IAP tutorial on Julia in January where they benchmarked rather non-trival code for doing li
by StefanKarpinski 14y ago
Miles Lubin and Iain Dunning gave an excellent presentation at MIT's IAP tutorial on Julia in January where they benchmarked rather non-trival code for doing linear programming in a variety of languages:
https://github.com/JuliaLang/julia-tutorial/blob/master/NumericalOptimization/presentation.pdf
http://bit.ly/VQ9rDd (shortened version since that's cut off)
See page 7, "Simplex Benchmarks". Essentially, they implemented the basics you need to do a fast simplex linear programming algorithm in each of C++, Julia, Matlab and Python. Their results were in line with the Julia home page benchmarks:
Julia is between 1-2x slower than good C/C++ code.
PyPy and Matlab are 10-20x slower.
Python is 80-400x slower.
The code can be found here:
https://github.com/mlubin/SimplexBenchmarks
These results are fairly typical.
- dylangs1030 14y ago1-2x slower is still remarkable given what Julia (apparently) has the power to do. Thanks for the citations!
- pygy_ 14y agoRemove the four spaces before the long URL, it will be properly displayed and linkified. Meanwhile: https://github.com/JuliaLang/julia-tutorial/blob/master/NumericalOptimization/presentation.pdf https://github.com/JuliaLang/julia-tutorial/blob/master/Nume... https://github.com/mlubin/SimplexBenchmarks https://github.com/mlubin/SimplexBenchmarks
- qznc 14y agoI think there should be a Python+NumPy variant for comparison as well.