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MATLAB Can't Manipulate 64-Bit Integers
An anonymous reader writes "MATLAB, an important package of mathematical software heavily used in industry and academia, has had support for 64-bit machines for several years now. However, the MATLAB developers still haven't gotten around to implementing even basic arithmetic operations for 64-bit integers. Attempting to add, divide, subtract, or multiply two 64-bit integers will result in an error message saying that the corresponding method does not exist. As one commentator put it, 'What is the point of having numerical data types that can't be manipulated?'" The post notes that the free MATLAB clone GNU Octave deals with 64-bit integers just fine.
MATLAB isn't strongly typed, and by default variables are floating-point (I think 64-bit is the standard if type isn't specified). Makes sense for scientific programming. You need to go out of your way to use integer types in MATLAB, and the only reason I've ever had to do it is when trying to convert MATLAB scripts to C code to run on fixed-point processors. I do think that not supporting 64-bit integer operations is an oversight but I don't think it affects the vast majority of MATLAB users.
I'm not a MATLAB user, just someone who has had to troubleshoot problems with it for a variety of clients.
A while back, more than a few years now, MATLAB on HPUX was limited to about 1GB of memory. Any MATLAB code that needed more memory than that was shit out of luck - even on a 64-bit machine with 64GB of RAM. This was partly due to MATLAB only being available as a 32-bit binary for HPUX and partly due to MATLAB having been compiled and linked in the most naive way possible. After diagnosing the problem with a client's MATLAB code (they had a lab full of $2M computers and couldn't run this software that only needed a couple of GB of data), I wrote a short explanation of the compile and link flags necessary to enable any process to access at least 2GB of RAM with practically no impact and 3GB with only minimal impact. In either case, no code changes necessary whatsoever.
MATLAB's customer support group responded with a categorical denial that it was even possible to do - that HPUX architecturally limited all 32-bit processes to 1GB of addressable memory. While a customer-specific test release would have been the ideal response, I was really only expecting them to open a feature request and get the next release built the right way. But they wouldn't even give my client that (despite them having an expensive support contract) - just a flat out denial of reality instead. The solution for my client was ultimately to rewrite their software in C and link it with the right flags to get access to 3GB of memory.
So, given just how strong their disinterest was in even trying to make their software work for big boys doing scientific computing, I'm not surprised to hear that all these years later they still haven't even bothered to implement native 64-bit math. They are entrenched and there just isn't enough competition to make them lift a finger.
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If you really want this support, get the user contributed package from matlab central. That wasn't too hard was it?
The summary mentioned Octave as an alternative to Matlab. There is also Scilab (which has some more c-like features).
In recency I have simply been using Python. Use the iPython (interactive python) shell and load scipy (from scipy import *) and you have a very nice calculating environment. The scipy arrays are quite a joy to work with compared with what I remember from Matlab. If you're working with equal size 1D arrays then they can be used without modification in normal mathematical expressions, so a lot of my code no longer involves any iteration with for loops.
There is a graphing library (pylab) based on Matlab syntax. If you start iPython with the -pylab flag it will print out plots the same way as in Matlab. There is also Easyviz which I believe also uses Matlab syntax but interfaces with a number of standard graphing programs (like Gnuplot.)
The sympy package for doing symbolic manipulations is also quite nice, IMHO.
Disclaimer: I only used Matlab casually for my undergraduate math classes.
When things get complex, multiply by the complex conjugate.
Just another reason to switch to numeric python. The more I use Matlab the less I find that I like it.
This sig wasn't worth reading, was it.
You can always use the c interface (which itself is weird, considering matlab's roots in fortran...)
The reason the C interface is weird is because MATLAB stores multidimensional arrays in column-major order, like Fortran. C, on the other hand, uses row-major order. However, if you work with linear algebra, then you'll appreciate the column-major layout, because it coincides with the order returned by the vec operator (which is used all the time in computational linear algebra, and stacks the columns of a matrix).
but then you'd have to learn c. Matlab is a tool for physicists and engineers, not computer scientists. They don't necessarily want to take the time to learn c, or they'd have done that. Some do, anyway, of course, but usually what they produce will be one off functions for a specific goal, not entire libraries suitable for sharing.
I work with digital signal processing and use MATLAB almost on a daily basis. The reason DSP engineers use MATLAB is not because they don't know or don't want to know C. In fact, a good DSP engineer must be very competent at writing clear and efficient C code, because that's what he needs to actually implement algorithms on hardware. Modern high performance DSPs are so complex that coding things in assembly is completely out of the question.
The reason MATLAB is so valuable is that it allows one to prototype things extremely fast with minimal performance loss (if you know what you're doing). Of course you won't have a MATLAB environment running on a DSP, so you'll eventually have to write the C code. But since most of my time is spent developing algorithms instead of actually implementing them, MATLAB lets me be much more productive.
So it clones Matlab very well then.
3D data is all but ungraphable on Linux systems anyway, so I suppose Octave is not alone here.
As I recall, MATLAB has a Linux port. As does Maple, Mathematica, et cetera. And Mayavi is an open source program capable of excellent 3D graphics that works with Python, and therefore SciPy.
So what you really mean is that 3D data graphing is inadequate with Octave and gnuplot on any system. 3D data is perfectly graphable in Linux.
Being the guy who implemented the proper 64-bit arithmetics support in Octave 3.2, I can maybe share some interesting points. Matlab's design choice of double as the default type is both a blessing and a curse. Usually the blessing strikes you first (I always disliked it that 1/2 is 0 in C++ and Python, finally Python 3 changed that as well), but you start to feel the curse when diving deeper, and integer arithmetics (which I agree is far less used than floating point) is a perfect example. Initially, Matlab probably had no integers. Given that double is the default, Matlab creators decided to make the integers "intrusive", in the sense that integers combined with reals result in integers, not reals, contrary to most other languages. The motivation is probably so that you can write things like a + 1 or 2*a without a silent conversion. Hence, when I is integer, D is double and OP is any operator, I OP D behaves like int (double (I) OP D). Except that things like a + 1 seem to be optimized (something Octave currently lacks, but it shouldn't be hard to do). int64 is where things start to get messy, because not all 64-bit integers can be exactly represented in double. So, using the simple formula above, 0.5 * i64 could occassionally do something else that i64 / 2, which is highly undesirable. In order to do the "right thing", Octave will choose one of two options: first, if it discovers that "long double" is at least 80-bits wide (so that it can hold any 64-bit integers), it will use that to do the operations. If not, it will employ a custom code to emulate the operation as if it was performed with enough precision. It's based on manipulating the mantissa and exponent of the double and is much slower. Although it was kinda fun to implement it, it is really a lot of work for too little music, so that can partially explain MathWorks' attitude to this. Unlike Octave, MathWorks doesn't really need to aim at source portability (as they just distribute binaries), so maybe they're just waiting for proper long double support in all compilers they use, and then they will just use the simple approach. Or maybe they're waiting for some important future design change. When I implemented this, I was fully aware that it's not a killer feature, yet I thought it may make Octave more interesting to some Matlab users. So I'm glad someone noticed :)
In any case, I suppose at some point Matlab will support this as well.