Vasile SIMA, Diana Maria SIMA High-Performance Algorithms for Linear Multivariable System Identification Abstract. Basic algorithmic and computational issues involved in subspace-based linear multivariable system identification are described. A new identification toolbox – SLIDENT – has been developed and incorporated in the freely available Subroutine Library in Control Theory (SLICOT). Reliability, efficiency, and ability to solve large, industrial identification problems received a special consideration. Two algorithmic subspace-based approaches (MOESP and N4SID) and their combination, and both standard or fast techniques for data compression are provided. Structure exploiting algorithms and dedicated linear algebra tools enhance the computational efficiency. Extensive comparisons with the available subspace techniques have been made. The numerical results show that the SLIDENT toolbox is highly performant and able to solve large identification problems.