BLASFEO：嵌入式优化的基本线性代数子程序
BLASFEO: basic linear algebra subroutines for embedded optimization
摘要：
BLASFEO是一个密集的线性代数库，提供用于嵌入式优化的BLAS和LAPACK类例程的高性能实现。相对于BLAS的现有高性能实现的关键区别在于，计算性能针对小到中等规模的矩阵进行了优化，即，尺寸可达几百个。BLASFEO有三种不同的实现：一种高性能实现，旨在为高速缓存中的矩阵提供最高性能;一种参考实现，提供可移植性和嵌入性，并针对非常小的矩阵进行了优化;以及一种标准BLAS和LAPACK的包装器，在大型矩阵上提供高性能。BLASFEO的三个实现一起为从非常小到很大的矩阵提供了高性能的密集线性代数例程。与开源和专有的高度调优的BLAS库相比，对于大小高达约100的矩阵，BLASFEO的高性能实现比相应的3级BLAS例程快约20-30%，比相应的LAPACK例程快2-3倍。
英文摘要：
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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