> arbitrary precision linear algebra i think most linear algebra operations are defined in julia and thus will work for bignums. sticking a call to big() somewhere in the code is usually enough to promote everything
On Fri, Sep 19, 2014 at 8:06 PM, Spencer Lyon <spencerly...@gmail.com> wrote: > Hi David, > > Thanks for the questions, I’ll respond in chunks. > > Any experiences/opinions/pitfalls your group discovered on the Python vs > Julia question? > > I personally love both languages and use both regularly. I find myself > reaching for Julia for most things right now — I love the more advanced > features like metaprogramming and parallel processing that are either > non-existent or not as well integrated into the python language itself (I > know there are many packages that provide similar features for python, but > they don’t feel as natural as they do in Julia). > > Did you find one is faster than the other? > > As you might expect, we found that Julia was faster than Python for most > things. This is probably an artifact of the type of algorithms we used. > Often we would define an operator that loops over a grid, and then iterate > on that operator until we find a fixed point. Because of this looping, > Julia has a predictable speed advantage. > > Were there major areas where Julia lagged behind Python? > > I can think of two places where performance in Julia wasn’t as good as > performance in python: > > 1. We often need to do quadrature in the innermost part of our loops > (to approximate expected values) and we found that Julia’s quadgk > routine was much slower scipy.integrate.fixed_quad. This is *not* a > fair comparison because the algorithms employed by the two functions are > very different. Specifically quadgk uses an adaptive algorithm while > fixed_quad just uses standard Gaussian quadrature. To get around this > we actually implemented a whole suite of quadrature routines in the file > quad.jl > <https://github.com/QuantEcon/QuantEcon.jl/blob/master/src/quad.jl>. > After switching the Julia code from using quadgk to our own internal > quadrature methods, we got approximately the same solutions, but the code > was between 35-115 faster. > 2. Linear interpolation. The function numpy.interp does simple > piecewise linear interpolation. We ended up using the CoordInterpGrid > type from Grid.jl to accomplish this. As of right now the > interpolation steps are the biggest bottleneck in most of the functions we > have. > > We also didn’t really find that Julia was lagging behind Python in terms > of libraries that we needed. All the functionality we needed was already > available to us. We are using PyPlot.jl to generate graphics, so I guess > the some of the Julia code is dependent on Python. Come to think of it the > one thing we would like to have that we haven’t been able to find is > arbitrary precision linear algebra. In Python this can be achieved through > SymPy’s wrapping of mpmath, but as far as I know we don’t yet have > arbitrary precision linear algebra in Julia. > > On Friday, September 19, 2014 12:40:01 PM UTC-4, David Anthoff wrote: > > This is fantastic! >> >> >> >> Any experiences/opinions/pitfalls your group discovered on the Python vs >> Julia question? I guess you pretty much implemented the same algorithms in >> both languages. Did you find one is faster than the other? Were there major >> areas where Julia lagged behind Python? >> >> >> >> Thanks, >> >> David >> >> >> >> *From:* julia...@googlegroups.com [mailto:julia...@googlegroups.com] *On >> Behalf Of *Spencer Lyon >> *Sent:* Thursday, September 18, 2014 7:14 PM >> *To:* julia...@googlegroups.com >> *Subject:* [julia-users] ANN: QuantEcon.jl >> >> >> >> New package QuantEcon.jl <https://github.com/QuantEcon/QuantEcon.jl>. >> >> This package collects code for quantitative economic modeling. It is >> currently comprised of two main parts: >> >> 1. A toolbox of routines useful when doing economics >> >> 2. Implementations of types and solution methods for common >> economic models. >> >> This library has a python twin: QuantEcon.py >> <https://github.com/QuantEcon/QuantEcon.py>. The same development team >> is working on both projects, so we hope to keep the two libraries in sync >> very closely as new functionality is added. >> >> The library contains all the code necessary to do the computations found >> on http://quant-econ.net/, a website dedicated to providing lectures >> that each economics and programming. The website currently (as of 9/18/14) >> has only a python version, but the Julia version is in late stages of >> refinement and should be live very soon (hopefully within a week). >> >> The initial version of the website will feature 6 lectures dedicated to >> helping a new user set up a working Julia environment and learn the basics >> of the language. In addition to this language specific section, the website >> will include 22 other lectures on topics including >> >> · statistics: markov processes (continuous and discrete state), >> auto-regressive processes, the Kalman filter, covariance stationary >> proceses, ect. >> >> · economic models: the income fluctuation problem, an asset >> pricing model, the classic optimal growth model, optimal (Ramsey) taxation >> , the McCall search model >> >> · dynamic programming: shortest path, as well as recursive >> solutions to economic models >> >> All the lectures have code examples in Julia and most of the 22 will >> display code from the QuantEcon.jl library. >> >> >> > >
