Parallel Programming with numpy and scipy
Multiprocessor and multicore machines are becoming more common, and it would be nice to take advantage of them to make your code run faster. numpy/scipy are not perfect in this area, but there are some things you can do. The best way to make use of a parallel processing system depend on the task you're doing and on the parallel system you're using. If you have a big complicated job or a cluster of machines, taking full advantage will require much thought. But many tasks can be parallelized in a fairly simple way.
As the saying goes, "premature optimization is the root of all evil". Using a multicore machine will provide at best a speedup by a factor of two or four. Get your code working first, before even thinking about parallelization. Then ask yourself whether your code actually needs to be any faster. Don't embark on the bug-strewn path of parallelization unless you have to.
Simple parallelization
Break your job into smaller jobs and run them simultaneously
For example, if you are analyzing data from a pulsar survey, and you have thousands of beams to analyze, each taking a day, the simplest (and probably most efficient) way to parallelize the task is to simply run each beam as a job. Machines with two processors can just run two jobs. No need to worry about locking or communication; no need to write code that knows it's running in parallel. You can have issues if the processes each need as much memory as your machine has, or if they are both IO heavy, but in general this is a simple and efficient way to parallelize your code - if it works. Not all tasks divide up this nicely. If your goal is to process a single image, it's not clear how to do this without a lot of work.
Use parallel primitives
One of the great strengths of numpy is that you can express array operations very cleanly. For example to compute the product of the matrix A and the matrix B, you just do:
>>> C = numpy.dot(A,B)
Not only is this simple and clear to read and write, since numpy knows you want to do a matrix dot product it can use an optimized implementation obtained as part of "BLAS" (the Basic Linear Algebra Subroutines). This will normally be a library carefully tuned to run as fast as possible on your hardware by taking advantage of cache memory and assembler implementation. But many architectures now have a BLAS that also takes advantage of a multicore machine. If your numpy/scipy is compiled using one of these, then dot() will be computed in parallel (if this is faster) without you doing anything. Similarly for other matrix operations, like inversion, singular value decomposition, determinant, and so on. Unfortunately it is not, as far as I know, possible to select parallel or single-threaded at runtime; also as far as I know all the parallel BLAS libraries are proprietary.
Finally, scipy/numpy does not parallelize operations like
>>> A = B + C >>> A = numpy.sin(B) >>> A = scipy.stats.norm.isf(B)
These operations run sequentially, taking no advantage of multicore machines (but see below). In principle, this could be changed without too much work. OpenMP is an extension to the C language which allows compilers to produce parallelizing code for appropriately-annotated loops (and other things). If someone sat down and annotated a few core loops in numpy (and possibly in scipy), and if one then compiled numpy/scipy with OpenMP turned on, all three of the above would automatically be run in parallel. Of course, in reality one would want to have some runtime control - for example, one might want to turn off automatic parallelization if one were planning to run several jobs on the same multiprocessor machine.
Write multithreaded code
Sometimes you can see how to break your problem into several parallel tasks, but those tasks need some kind of synchronization or communication. For example, you might have a list of jobs that can be run in parallel, but you need to gather all their results, do some summary calculation, then launch another batch of parallel jobs. This can be very cumbersome to write as separate processes, so python provides tools for multithreading, in particular the "threading" module.
Be warned: python has some serious limitations when it comes to multithreading. These limitations are somewhat alleviated by numpy and scipy, but multithreading may well not gain you nearly as much performance as you would have expected.
The essential limitation of python's multithreading is the Global Interpreter Lock (GIL). For various reasons (a quick web search will turn up copious discussion, not to say argument, over why this exists and whether it's a good idea), python is implemented in such a way that only one thread can be accessing the interpreter at a time. This basically means only one thread can be running python code at a time. This almost means that you don't take any advantage of parallel processing at all. The exceptions are few but important: while a thread is waiting for IO (for you to type something, say, or for something to come in the network) python releases the GIL so other threads can run. And, more importantly for us, while numpy is doing an array operation, python also releases the GIL. Thus if you tell one thread to do:
>>> print "%s %s %s %s and %s" %( ("spam",) *3 + ("eggs",) + ("spam",) )
>>> A = B + C
>>> print A
During the print operations and the % formatting operation, no other thread can execute. But during the A = B + C, another thread can run - and if you've written your code in a numpy style, much of the calculation will be done in a few array operations like A = B + C. Thus you can actually get a speedup from multiple processors.
Given the limitations of multithreading, it may not be worth carefully rewriting your code in a multithreaded architecture. But sometimes you can do multithreading with little effort, and in these cases it can be worth it. See Cookbook/Multithreading for one example.
Sophisticated parallelization
If you need sophisticated parallelism - you have a computing cluster, say, and your jobs need to communicate with each other frequently - you will need to start thinking about real parallel programming. This is a subject for graduate courses in computer science, and I'm not going to address it here. But there are some python tools you can use to implement the things you learn in that graduate course. (I am perhaps exaggerating - some parallelization is not that difficult, and some of these tools make it fairly easy. But do realize that parallel code is much more difficult to write and debug than serial code.)