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#TWISTIT PYTHON CODE#
#TWISTIT PYTHON WINDOWS#

#TWISTIT PYTHON CODE#

Here, the use of threads don't increase the performance of an application but instead make the code easier to create and maintain. One of the many problems with threading is that it has multiple meanings.

#TWISTIT PYTHON WINDOWS#

If you're on Windows or OSX you should use the specific installers for Linux easy_install should work. However, if you can fit your project within what I'm showing in this article, things should go easily. It's definitely worth using, but you should be ready to roll up your sleeves. The project is very actively maintained, but it's by people who are good at writing (and testing) code, and not so good at documentation. Despite the issues, Twisted is worth it because the code is very good, and it solves your problem. Normally, caveats like that might send you away from an open-source project. There is an O'Reilly book on Twisted, but it's not great - you get it anyway because that's the only book there is. You usually have to thrash around quite a bit to figure out what you need, and even then it requires luck (but the newsgroup tends to be helpful). They use weird naming conventions, often making things up even though there are accepted names for things, so names are often nonsensical.

Twisted contains lots of libraries to do all kinds of things, and this abundance can be overwhelming. Some platforms have specific solutions (like pipes in Unix), but to create a universal solution we need something that is cross platform.īefore wading into the world of Twisted, you should be forewarned. It might seem strange that a networking library is the solution to concurrency, but once you divide your task into subprocesses, the next problem is to communicate with these subprocesses. And this article by the creators of Twisted gives a broad overview of the library. I've written an earlier article on the basics of Twisted. The framework shown here can be used for embarrassingly parallel problems, but it also shows multiple steps and uses a 'barrier' tool to automatically let all the pieces finish at each step. Very often you'll need to use multiple steps, waiting for all the parts to catch up before moving onto the next step. If your problem is not embarrassingly parallel you'll need to guess and experiment with different tasks in your program, trying to estimate which parts of the problem can be solved independently. Balancing the work across processors so that progress is optimized is relatively easy in this case. Here, you just divide up the work and parcel it out to the different processes. In the simplest case, the so-called 'embarrassingly parallel' scenario, the pieces of a problem are inherently independent. Somehow, your problem must be divisible into pieces that can run independently of each other. This is the tricky part, from both a math and a programming standpoint. There are various ways to implement your particular solution, but what I show here solves all the hard problems of wiring things up. The system you see here is only a framework for creating concurrent programs. The example works on a single machine, but it can easily be modified to work with a cluster. Each subprocess is a worker that solves part of the problem. This article shows a basic framework for putting a subprocess on each CPU in your system. This article is based on some of the work we've done, which came together last week with the help of Michail Xyntarakis, one of the Parsons-Brinkerhoff engineers.

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Some of this consulting is knowledge transfer and some is software development. I've been helping them develop concurrent systems to solve these problems more quickly.

The numerical models for this analysis are extremely detailed and a model can require days to run. For the past few years I've been consulting with a group at Parsons-Brinkerhoff, led by Rick Donnelly, which specializes in traffic analysis.

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