Python Gets a Big Data Boost From DARPA

itwbennett writes "DARPA (the U.S. Defense Advanced Research Projects Agency) has awarded $3 million to software provider Continuum Analytics to help fund the development of Python's data processing and visualization capabilities for big data jobs. The money will go toward developing new techniques for data analysis and for visually portraying large, multi-dimensional data sets. The work aims to extend beyond the capabilities offered by the NumPy and SciPy Python libraries, which are widely used by programmers for mathematical and scientific calculations, respectively. The work is part of DARPA's XData research program, a four-year, $100 million effort to give the Defense Department and other U.S. government agencies tools to work with large amounts of sensor data and other forms of big data."

Comments

I get the impression that

[Chrisq]

I get the impression that in the Engineering and Scientific community Python is the new Fortran. I hope so, because it would be "Fortran done right".

Re:I get the impression that

[jma05]

> I might get to learn Python one day but I'm afraid I'd become a so-so programmer in both languages.

I empathize since I conversely only barely use Ruby. Once someone learns one of these languages, there is not that much that the other offers. But happily, one need not learn advanced Python to benefit from these projects.

> it's a shame that so much effort is being divided between communities

AFAIK, all scientific funding from US and Europe is/was always directed to Python, not Ruby. So Python is firmly established as a research language and there is not much effort being divided with Ruby (which seems to have a much more spotted and amateur movement in this direction), at least as far as scientific stuff is concerned (Ruby is more popular on web app side). For me the tension for scientific use is not between Python and Ruby, but between Python and R. Python community is replicating a lot of R functionality these days but R still has a much better lead in science libraries. Happily, it is quite easy to call R from Python.

Re:I get the impression that

[solidraven]

You're dead wrong, nothing quite beats Fortran in speed when it comes to number crunching. If you need to go through hundreds of gigabytes of data and performance is important there's only one realistic choice: Fortran. Python isn't fit to run on a large cluster to simulate things, too much overhead. And lets not forget what sort of efficiency you can get if you use a good compiler (Intel Composer). You won't find Fortran on the way out over here, it's here to stay!

Re:I get the impression that

[Anonymous Coward]

Short answer, Fortran has stricter aliasing rules so the compiler has more optimization opportunities. Long answer, see Stack Overflow.

Re:I get the impression that

[Kwyj1b0]

Compared to plain old Python, yes. But Cython offers a lot of capabilities that improve speed dramatically - just using a type for your data in Cython gives programs a wonderful boost in speed.

As someone who uses Matlab for most of my programming, I have come to detest languages that do not force specifying a variable type and/or declaring variables. Matlab offers neither, but it is a standard in some circles.

Re:I get the impression that

[LourensV]

You're probably right, but you're also missing the point. Most scientists are not programmers who specialise in numerical methods and software optimisation. Just getting something that does what they want is hard enough for them, which is why they use high-level languages like Matlab and R. If things are too slow, they learn to rewrite their computations in matrix form, so that they get deferred to the built-in linear algebra function libraries (which are written in C or Fortran), which usually gets them to within an order of magnitude of these low-level languages.

If that still isn't good enough, they can either 1) choose a smaller data set and limit the scope of their investigations until things fit, 2) buy or rent a (virtual) machine with more CPU and more memory, or 3) hire a programmer to re-implement everything in a low-level language and so that it can run in parallel on a cluster. The third option is rarely chosen, because it's expensive, good programmers are difficult to find, and in the course of research the software will have to be updated often as the research question and hypotheses evolve (scientific programming is like rapid prototyping, not like software engineering), which makes option 3) even more expensive and time-consuming.

So yes, operational weather forecasts and big well-funded projects that can afford to use it will continue to use Fortran and benefit from faster software. But for run-of-the-mill science, in which the data sets are currently growing rapidly, having a freely available "proper" programming language that is capable of relatively efficiently processing gigabytes of data while being easy enough to learn for an ordinary computer user is a godsend. R and Matlab and clones aren't it, but Python is pretty close, and this new library would be a welcome addition for many people.

Re:I get the impression that

[nadaou]

You're probably right, but you're also missing the point. Most scientists are not programmers who specialise in numerical methods and software optimisation.

Which is exactly why FORTRAN is an excellent choice for them instead of something else fast (close to assembler) like C/C++, and why so many of the top fluid dynamics models continue to use it. It is simple (perhaps a function of its age) and because of that it is simple to do things like break up the calculation for MPI or tell the compiler to "vectorize this" or "automatically make it multi-threaded" in a way which is still a long from maturity for other languages.

Can you guess which language MATLAB was originally written in? You know that funny row,column order on indexes? Any ideas on the history of that?

R is great an all, and is brilliant in its niche, but how's that RAM limitation thing going? It's not a solution for everything.

MATLAB is pretty good too, as is Octave and SciLab, and it has gotten a whole lot faster recently, but ever try much disk I/O or array resizing for something which couldn't be vectorized? Becomes slow as molasses.

If that still isn't good enough, they can either 1) choose a smaller data set and limit the scope of their investigations until things fit,

heh. I don't think you know these people.

2) buy or rent a (virtual) machine with more CPU and more memory,

Many problems are I/O limited and require real machines with high speed low latency network traffic. VMs just don't cut it for many parallelized tasks which need to pass messages quickly.

Forgive me if I'm wrong, but your post sounds a bit like you think you're pretty good on the old computers, but don't know the first thing about FORTRAN and are feeling a bit defensive about that, and attacking something out of ignorance.

Wrong language

[Dishwasha]

The put the money in the wrong place. They should have put it in to R which very popularly interfaces with Python.

Re:Great. Just Great

[Kwyj1b0]

Yeah the govt needs better systems to manage the huge databases and dossiers they are building on everybody with their warrentless wiretaps and reading everybody's emails. Anybody who helps with this project is pretty damn naive if they don't think it will also be used for this.

For that matter anybody who trusts the govt and thinks the govt is your friend is pretty damn naive. Yeah I would like to believe that too. No I won't ignore the mountains of evidence to the contrary. I won't treat all the counterexamples as isolated cases. I see them for what they are: an amazingly consistent pattern. The rule, not the exception. Govt positions are really attractive to sociopath types who just love power and control and a feeling that they are important and they get that feeling by imposing their will on us.

So what you are saying is that DARPA funds will be used in a way to further the goals of DARPA/The government? Shocking. I haven't read anything that says which agencies will/won't have access to these tools - so I'd hazard a guess that any department that wants it can have it (including the famous three letter agencies).

FYI, Continuum Analytics is a company that is based on providing high-performance python-based computing to clients. Any packages they might release will either be open source (and can be checked), or closed source (in which case you don't have to use it). They aren't hijacking the Numpy/Scipy libraries. They are developing libraries/tools for a client (who happens to be DARPA). (Frankly, I'd hope that Continuum Analytics open sources their development because it might be useful to the larger community). You do know that DARPA funds also go to improve robotics, they supported ARPANET, and a lot of their space programs later got transferred to NASA?

Basically, I have no idea what you are ranting about. One government organization funded a project - it happens all the time. Do you rant about NSF/NIH/NASA money as well? If so, you'd better live in a cave - a lot of government sponsored research has gone into almost every modern convenience that we take for granted.

Re:Python 2 or 3?

[SQL Error]

Both. The prebuilt "Anaconda" distro defaults to Python 2.7, but it also works with 3.3 and 2.6.

Re:Great. Just Great

[Anonymous Coward]

What is this APRANET thing? It sounds like some useless crap loaded acronym to me.

Re:Great. Just Great

[sdaug]

Frankly, I'd hope that Continuum Analytics open sources their development because it might be useful to the larger community

Open sourcing is a requirement of the XDATA program.