I had an FEM code which does the pre-processing parts setting up the element matrices and stitching them together. With Eigen, the library is written to make sure that vector instructions result. So naturally, this would involve a lot of memory allocations. On the other side, the last major improvement of uBLAS was in and no significant change was committed since As far as I’m concerned, I have decided to proceed with my code in Boost. I’m running the uBLAS dense vector and matrix benchmarks. Does the memory allocations affect the result?
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Boost Basic Linear Algebra
Does anyone have a better idea about their key uhlas and on which basis can we choose between them? Lightness Races in Orbit k 59 59 gold badges silver badges bronze badges.
We would like to thank all, which supported and contributed to the development of this library: With Eigen, the library ublaa written to make sure that vector instructions result.
So naturally, this would involve a lot of memory allocations. On the other side, the last major improvement of uBLAS was in and no significant change was committed since What operation is the one you unlas performing here?
Views into vectors and matrices can be constructed via ranges, slices, adaptor classes and indirect arrays. Views into vectors and matrices can be constructed via ranges, slices, adaptor classes and indirect arrays.
Eigen lacks some things, such as projection indexing a matrix using another matrixwhile uBLAS has it. Sign up or log in Sign up using Google. This got me wondering if I can get all my work based only on boost as it is already a major library for my code.
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If you are using such a compiler please use this version of uBLAS. Eigen also uses expression templates. Why don’t I get a compile time or runtime diagnostic? As far as I’m concerned, I have decided to proceed with my code in Boost.
I’ve written some uBLAS tests, which try to incorrectly assign different matrix types or overrun vector and matrix dimensions. Eigen is very feature complete. You do not need to disable expression ublass. I just did a time complexity comparison between boost and eigen for fairly trivial matrix computations. It supports all standard numeric types, including std:: Its ecosystem of unsupported modules ublxs many specialized features such as non-linear optimization, matrix functions, a polynomial solver, FFT, and much more.
Support for Boost uBLAS Matrix-matrix Multiplication
Choose Eigen if you care the performance and performance gain introduced by expression templates, and choose uBlas if you only want to learn expression templates. The design and implementation unify mathematical notation via operator overloading and efficient code generation via expression templates.
The library covers the usual basic linear algebra operations on vectors and matrices: Vtik Vtik 2, 1 1 gold badge 14 14 silver badges 32 32 bronze badges.
You will find the library here. How do we handle problem users? And finally uBLAS offers good but not outstanding performance.