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The stochastic growth model, which is the workhorse of modern economics, cannot be easily vectorized, and usually requires tight inner loops to get good perform

The stochastic growth model, which is the workhorse of modern economics, cannot be easily vectorized, and usually requires tight inner loops to get good performance, or rewrite the computational kernels in C. Codes such as Dynare (http://www.dynare.org http://www.dynare.org) do exactly that. In my opinion, this is where Julia shines - it provides high performance in a dynamic language, much at the level of R or MATLAB. Then again, being a co-creator of Julia, I am probably biased, but this is exactly the kind of thing that motivated work on Julia in the first place.

minimax 12y ago
The two things I like about Julia are actually using it and all the smart people I have met who are also using it. It's awesome. After playing with this particular problem a bit, I do see how it is a pathological case for R due to the recursive nature of the value function. I still think I could do better than 500x slower than C++.
bachmeier 12y ago
The stochastic growth model is the workhorse of a narrow subfield of economics. It is not in any way representative of the research we do. Further, you will not find any examples that are better suited to compiled languages or less suited to using R than the one in this paper. To my knowledge, value function iteration isn't even used much in this area of research, so the claim that the stochastic growth model is a "workhorse" is a bit misleading - the stochastic growth model gets used, but it is not solved very often using the methodology in this paper.
c0g 12y ago
Hi Viral, do you have an intuition for why Numba would be faster than Julia here? It appears Julia spends a lot of time calculating the log on line 71.
- ihnorton 12y ago
  (not Viral, but) yes - log was the first thing that came up when this paper was discussed on the Julia mailing list. Julia's openlibm is intended to provide cross-platform accuracy and consistency, but the log function turned out to be slower than log in the system libm used by C++ and Python. The other big reason that Numba was faster was GC performance. There is an outstanding pull request adding incremental GC to Julia, which should help when it is fully integrated, and several other improvements are planned.

minimax 12y ago

The two things I like about Julia are actually using it and all the smart people I have met who are also using it. It's awesome. After playing with this particular problem a bit, I do see how it is a pathological case for R due to the recursive nature of the value function. I still think I could do better than 500x slower than C++.

bachmeier 12y ago

The stochastic growth model is the workhorse of a narrow subfield of economics. It is not in any way representative of the research we do. Further, you will not find any examples that are better suited to compiled languages or less suited to using R than the one in this paper. To my knowledge, value function iteration isn't even used much in this area of research, so the claim that the stochastic growth model is a "workhorse" is a bit misleading - the stochastic growth model gets used, but it is not solved very often using the methodology in this paper.

c0g 12y ago

Hi Viral, do you have an intuition for why Numba would be faster than Julia here? It appears Julia spends a lot of time calculating the log on line 71.

ihnorton 12y ago
(not Viral, but) yes - log was the first thing that came up when this paper was discussed on the Julia mailing list. Julia's openlibm is intended to provide cross-platform accuracy and consistency, but the log function turned out to be slower than log in the system libm used by C++ and Python. The other big reason that Numba was faster was GC performance. There is an outstanding pull request adding incremental GC to Julia, which should help when it is fully integrated, and several other improvements are planned.