BLASFEO: basic linear algebra subroutines for embedded optimization

8 Apr 2017  ·  Gianluca Frison, Dimitris Kouzoupis, Tommaso Sartor, Andrea Zanelli, Moritz Diehl ·

BLASFEO is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization. A key difference with respect to existing high-performance implementations of BLAS is that the computational performance is optimized for small to medium scale matrices, i.e., for sizes up to a few hundred... BLASFEO comes with three different implementations: a high-performance implementation aiming at providing the highest performance for matrices fitting in cache, a reference implementation providing portability and embeddability and optimized for very small matrices, and a wrapper to standard BLAS and LAPACK providing high-performance on large matrices. The three implementations of BLASFEO together provide high-performance dense linear algebra routines for matrices ranging from very small to large. Compared to both open-source and proprietary highly-tuned BLAS libraries, for matrices of size up to about one hundred the high-performance implementation of BLASFEO is about 20-30% faster than the corresponding level 3 BLAS routines and 2-3 times faster than the corresponding LAPACK routines. read more

PDF Abstract


  Add Datasets introduced or used in this paper