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arXiv · 1704.02457

BLASFEO: basic linear algebra subroutines for embedded optimization

Gianluca Frison·Dimitris Kouzoupis·Tommaso Sartor·Andrea Zanelli·Moritz Diehl
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Abstract

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.

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Gianluca Frison, Dimitris Kouzoupis, Tommaso Sartor, Andrea Zanelli, Moritz Diehl. 2017-04-08. BLASFEO: basic linear algebra subroutines for embedded optimization. https://doi.org/10.1145/3210754

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