I have to do calculation on array of 1,2,3...9 dimensional vectors, and the number of those vectors varies significantly (say from 100 to up to couple of millions). Of course, it would be great if the data container can be easily decomposed to enable parallel algorithms.
I came across blitz++(almost impossible to compile for me), but are there any other fast libs that manipulate array of vector data? Is boost::fusion worth a look? Furthermore, vtk's vtkDoubleArray seems nice, but vtk is lib used only for visualization. I must admit that having array of tuples is a tempting idea, but I didn't see any benchmarks regarding boost::fusion and/or vtkDoubleArray. Just as they are not built for speed in mind. Any thoughts?
best regards,
mightydodol
Eigen, supports auto-vectorisation of vector on certains compilers (GCC 4, VC++ 2008).
For linear algebra, you probably want to evaluate Boost uBLAS, which is a subset of the full BLAS package. As you mention, Boost Fusion may also be appropriate, depending on the algorithms you are implementing.
I believe you can use the non-GUI parts of VTK such as vtkDoubleArray without linking in the visualisation libraries if you don't need them. Note that VTK is designed for efficiency of rendering, not of calculations. If you don't want to render the results, you might as well use one of the scientific packages that provide optimized algorithms.
There is a Parallel flavour of BLAS called (strangely enough) PBLAS. I don't think this is available through the Boost wrapping, so you would use the C interface directly.
Without knowing what yo want to do with your arrays, it's hard to give really firm advice. If high performance manipulation of the arrays is needed then Blitz++ is probably your best bet. If you are having trouble compiling it then perhaps you need to change your compiler or system. They do support g++ so a recent version on just about anything should get you going.
I haven't used Boost::fusion but a quick read of the manual suggests that it's major goal is just to make heterogeneous containers. I don't think that's what you want.
I have tried to use the GSL but find it hopelessly awkward for anything I have wanted to do.
I'm no expert, but you might want to consider using a MATLAB API.
There is the GNU Scientific Library for operation in vector or matrix
I would try using Blitz++, it will give you a really good performance. Armadillo is also quite efficient.
Related
I m going to do an algorithm like needleman-wunsch algorithm or smith-watterman algorithm for large sequences. So I'm going to need a way to create matrices of different sizes so my question is which library gives me the best performance on that and would be easy to use.
P.S: I know that OpenCV, and Boost can handle the matrices but I don't know if there are good to do operations on it.
If “the best performance” is the requirement, then you have to look at NVIDIA CUDA or Intel MKL. Libraries like C++ boost uBLAS concentrate not on performance, but on usability.
I'm looking for the best choice of C++ Mathematics library to make easier some operations from LabView blocks.
I need to realize many complex mathematics things on C++ alike: linear regression, peak detection, derivative for the graph and many others alike.
I found there are a lot of libraries for it: http://en.wikipedia.org/wiki/List_of_numerical_libraries#C_and_C.2B.2B
Which library is better to chose for my tasks?
(currently Im thinking about boost BLAS but I never worked with it earlier so maybe this choice is wrong)
Note that there isn't much more to boost uBLAS than basic linear algebra; and even if you consider the larger boost "Math and Numerics" section, it can hardly be considered a complete scientific computing package.
GSL is very good in that it's quite comprehensive. However, it's very much a 'C' library so you need to be prepared to work with raw pointers to array data and function pointer callbacks rather than higher level classes.
(Personally these days I find myself using Python/Numpy/Scipy as much as possible; the scope of Scipy is truly incredible and Numpy arrays are fantastically easy to work with; if there was a LabView/Python/Scipy integration that met any other requirements it'd be the first thing I'd be looking it).
I've been wanting to write my own multithreaded realtime raytracer in C++, but I don't want to implement all the vector and matrix logic that comes with it. I figured I'd do some research to find a good library for this, but I haven't had much success...
It's important that the implementation is fast, and preferably that it comes with some friendly licensing. I've read that boost has basic algebra, but I couldn't find anything on how good it was regarding its speed.
For the rest, Google gave me Armadillo, which claims to be very fast, and compares itself to certain other libraries that I haven't heard of.
Then I got Seldon, which also claims to be efficient and convenient, although I couldn't find out where exactly they are on the scale.
Lastly I read about Eigen, which I've also seen mentioned here on StackOverflow while searching here.
In the CG lecture at my university, they use HLSL for the algebra (making the students implement/optimise parts of the raytracer), which got me thinking whether or not I could use GLSL for this. Again, I have no idea what option is most efficient, or what the general consensus is on algebra libraries. I was hoping SO could help me out here, so I can get started with some real development :)
PS: I tried linking to sites, but I don't have enough rep yet
I'd recommend writing your own routines. When I wrote my raytracer, I found that most of the algebra used the same small collection of methods. Basically all you need is a vector class that supports addition, subtraction, etc. And from there all you really need is Dot and Cross.
And to be honest using GLSL isn't going to give you much more than that anyways (they only support dot, cross and simple vector math, everything else must be hand coded). I'd also recommend prototyping in C++ then moving to CUDA afterwards. It's rather difficult to debug a GPU code, so you can get it working in the CPU then recode it a bit to work in CUDA.
In reality raytracers are fairly simple. It's making them fast that is hard. It's the acceleration structures that are going to take most of your time and optimization. At least it did for me.
You should take look at http://ompf.org/forum/
This forum treats of realtime raytracing, mostly in C++. It will give you pointers, and sample source.
Most of the time, as every cycle count, people do not rely on external vector math libs: optimizations depend on the compiler you're using, inlining, use of SSE (or kindof) or not, etc.
I recommend "IlmBase" that is part of the OpenEXR package. It's well-written C++, developed by ILM, and widely used by people who professionally write and use graphics software.
For my projects I used glm, maybe it would also suit you.
Note that libraries such as boost::ublas or seldon probably won't suit you, because they're BLAS-oriented (and I assume you're looking for a good 3D-driven linear algebra library).
Also, the dxmath DirectX library is pretty good, although sometimes hard to use, because of it's C-compatible style.
You might look at the source code for POVRAY
I'm doing some linear algebra math, and was looking for some really lightweight and simple to use matrix class that could handle different dimensions: 2x2, 2x1, 3x1 and 1x2 basically.
I presume such class could be implemented with templates and using some specialization in some cases, for performance.
Anybody know of any simple implementation available for use? I don't want "bloated" implementations, as I'll running this in an embedded environment where memory is constrained.
Thanks
You could try Blitz++ -- or Boost's uBLAS
I've recently looked at a variety of C++ matrix libraries, and my vote goes to Armadillo.
The library is heavily templated and header-only.
Armadillo also leverages templates to implement a delayed evaluation framework (resolved at compile time) to minimize temporaries in the generated code (resulting in reduced memory usage and increased performance).
However, these advanced features are only a burden to the compiler and not your implementation running in the embedded environment, because most Armadillo code 'evaporates' during compilation due to its design approach based on templates.
And despite all that, one of its main design goals has been ease of use - the API is deliberately similar in style to Matlab syntax (see the comparison table on the site).
Additionally, although Armadillo can work standalone, you might want to consider using it with LAPACK (and BLAS) implementations available to improve performance. A good option would be for instance OpenBLAS (or ATLAS). Check Armadillo's FAQ, it covers some important topics.
A quick search on Google dug up this presentation showing that Armadillo has already been used in embedded systems.
std::valarray is pretty lightweight.
I use Newmat libraries for matrix computations. It's open source and easy to use, although I'm not sure it fits your definition of lightweight (it includes over 50 source files which Visual Studio compiles it into a 1.8MB static library).
CML matrix is pretty good, but may not be lightweight enough for an embedded environment. Check it out anyway: http://cmldev.net/?p=418
Another option, altough may be too late is:
https://launchpad.net/lwmatrix
I for one wasn't able to find simple enough library so I wrote it myself: http://koti.welho.com/aarpikar/lib/
I think it should be able to handle different matrix dimensions (2x2, 3x3, 3x1, etc) by simply setting some rows or columns to zero. It won't be the most fastest approach since internally all operations will be done with 4x4 matrices. Although in theory there might exist that kind of processors that can handle 4x4-operations in one tick. At least I would much rather believe in existence of such processors that than go optimizing those low level matrix calculations. :)
How about just store the matrix in an array, like
2x3 matrix = {2,3,val1,val2,...,val6}
This is really simple, and addition operations are trivial. However, you need to write your own multiplication function.
I would need some basic vector mathematics constructs in an application. Dot product, cross product. Finding the intersection of lines, that kind of stuff.
I can do this by myself (in fact, have already) but isn't there a "standard" to use so bugs and possible optimizations would not be on me?
Boost does not have it. Their mathematics part is about statistical functions, as far as I was able to see.
Addendum:
Boost 1.37 indeed seems to have this. They also gracefully introduce a number of other solutions at the field, and why they still went and did their own. I like that.
Re-check that ol'good friend of C++ programmers called Boost. It has a linear algebra package that may well suits your needs.
I've not tested it, but the C++ eigen library is becoming increasingly more popular these days. According to them, they are on par with the fastest libraries around there and their API looks quite neat to me.
Armadillo
Armadillo employs a delayed evaluation
approach to combine several operations
into one and reduce (or eliminate) the
need for temporaries. Where
applicable, the order of operations is
optimised. Delayed evaluation and
optimisation are achieved through
recursive templates and template
meta-programming.
While chained operations such as
addition, subtraction and
multiplication (matrix and
element-wise) are the primary targets
for speed-up opportunities, other
operations, such as manipulation of
submatrices, can also be optimised.
Care was taken to maintain efficiency
for both "small" and "big" matrices.
I would stay away from using NRC code for anything other than learning the concepts.
I think what you are looking for is Blitz++
Check www.netlib.org, which is maintained by Oak Ridge National Lab and the University of Tennessee. You can search for numerical packages there. There's also Numerical Recipes in C++, which has code that goes with it, but the C++ version of the book is somewhat expensive and I've heard the code described as "terrible." The C and FORTRAN versions are free, and the associated code is quite good.
There is a nice Vector library for 3d graphics in the prophecy SDK:
Check out http://www.twilight3d.com/downloads.html
For linear algebra: try JAMA/TNT . That would cover dot products. (+matrix factoring and other stuff) As far as vector cross products (really valid only for 3D, otherwise I think you get into tensors), I'm not sure.
For an extremely lightweight (single .h file) library, check out CImg. It's geared towards image processing, but has no problem handling vectors.