What is uBLAS in boost?
uBlas is a header-only library and part of the Boost C++ libraries. It provides a set of basic linear and multilinear algebra operations with tensors, matrices and vectors. uBLAS is documented at boost.org or in docs.
Is there a matrix library for C++?
Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
Who wrote Blas?
Pozo, K. Remington, R. C. Whaley, An Updated Set of Basic Linear Algebra Subprograms (BLAS), ACM Trans. Math. Softw., 28-2 (2002), pp.
What is uBLAS?
uBLAS is a C++ template class library that provides BLAS level 1, 2, 3 functionality for dense, packed and sparse matrices. The design and implementation unify mathematical notation via operator overloading and efficient code generation via expression templates.
Does Eigen use Blas?
Eigen: Using BLAS/LAPACK from Eigen. Since Eigen version 3.3 and later, any F77 compatible BLAS or LAPACK libraries can be used as backends for dense matrix products and dense matrix decompositions. When doing so, a number of Eigen’s algorithms are silently substituted with calls to BLAS or LAPACK routines.
Is Eigen faster than LAPACK?
Eigen beats LAPACK using optimization flags ( -O3 ) and a good compiler (GCC, Clang).
Does Tensorflow use Eigen?
tensorflow uses the Tensor module of Eigen (which is mostly maintained by the main author of tensorflow).
Does Blas GPU?
R. With nvblas, nvidia offers a GPU-based BLAS-library, which it claims to be significantly faster than standard procedures. For this comparison we compare R’s default BLAS, the optimized libraries ATLAS and OpenBLAS (all of which are using CPUs only) with nvblas (which uses CPU and GPU).
What is linear algebra used for in real life?
Other real-world applications of linear algebra include ranking in search engines, decision tree induction, testing software code in software engineering, graphics, facial recognition, prediction and so on.
Why is Eigen so fast?
For operations involving complex expressions, Eigen is inherently faster than any BLAS implementation because it can handle and optimize a whole operation globally — while BLAS forces the programmer to split complex operations into small steps that match the BLAS fixed-function API, which incurs inefficiency due to …
What is Boost uBLAS?
Boost.uBLAS is part of the Boost C++ Libraries . It is directed towards scientific computing on the level of basic linear and multilinear algebra operations with tensors, matrices and vectors. uBLAS is documented at boost.org .
What are the factors that affect the complexity of uBLAS?
Firstly the complexity depends on matrix size. Also since prod is transitive (not commutative) the bracket order affects the complexity. uBLAS evaluates expressions without matrix or vector temporaries and honours the bracketing structure. However avoiding temporaries for nested product unnecessarly increases the complexity.
How do I Turn Off proxies in uBLAS?
For the more daring ‘sparse element proxies’ can be completely turned off in uBLAS by defining the configuration macro BOOST_UBLAS_NO_ELEMENT_PROXIES . What is the complexity (the number of add and multiply operations) required to compute the following?