Numerical derivatives in parallel

My last post was about a fairly esoteric new language feature in python: coroutines. I wasn't reading up on them because they're whiz-bang cool (okay, not only because they're whiz-bang cool) but because I have an actual problem that they might help with. For the triple system, I find I need to do very slow numerical optimization problems. That is, the objective function takes seconds to evaluate. It can't be parallelized internally, and numerical minimization is largely a sequential operation: evaluate the function, figure out which way is downhill, go there, repeat. So this code currently uses just one core, and is almost as slow as the highly parallel Markov-Chain Monte Carlo code that does hundreds of times as much work. So obviously it would be nice to find some parallelism. I have a way to do that: parallelize gradient evaluations.

The other reason for this post is to make a point about code robustness. Getting numerical derivatives right is a notoriously difficult process, and getting them both efficient and right is even harder. The package I have been using, MINUIT, has been used by the particle physicists for decades, and they have hammered out all sorts of corner cases that a naive implementation might miss. So I'm not going to use a naive implementation, not least because my problem is numerically difficult. This means the code is not simple, and I don't want to try to restructure it manually to be more concurrent; I want the computer to handle the concurrency for me.

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Numerical coroutines

Python 3.5 has new syntax for coroutines. Like many of python3's new features (unicode strings? who uses non-English languages in astronomy?), it is not immediately obvious that coroutines are of any use to people like me who mostly write relatively simple imperative scripts to wrangle large amounts of data or long computations. But cores aren't getting any faster, so we're going to have to start writing parallel code if we're going to take advantage of new machines. Now, if you want to take a gigantic Fourier transform, or do algebra on enormous matrices, you are going to need a witch design you a parallel algorithm. But our code is often full of easy opportunities for parallelism that we don't take advantage of because it's awkward. So I keep my eye out for ways to take advantage of this easy parallelism. I have found one, in the combination of coroutines and futures, and I'd like to describe it with a worked example. Maybe it'll sell you on some of these cool new language features!

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