Interoperability between STL containers and other templated libraries in Matrices.
My first test with a blog.
Some of the best ways to solve a problem in Linear Algebra. Especially formulation of 2d or 1d tensor in C++.
- Eigen :
Eigen is one of the header only templated libraries which has been extremely optimised for linear and non linear algebra based applications for a multitude of architectures. Eigen supports SSE, AVX, AVX512, AltiVec/VSX (On Power7/8 systems in both little and big-endian mode), ARM NEON for 32 and 64-bit ARM SoCs, and now S390x SIMD (ZVector).
- Thrust:
I am a big fan of thrust as it eases up the API level abstractions of the CUDA to a limited few functions.
- Vectors:
STL Vector Containers are innately provided by standard C++ library, over the years they have become an integrated part of the development. STL vectors encapsulate dynamic sized arrays and are efficiently designed to perform some of the readiy available functions of the std library of C++.
- Double Pointer:
Double pointers are pointer to pointers.
- Armadello : Another Eigen kind of library, I haven’t really tried it out.