Model order reduction is necessary in many complex scientific and engineering applications. Among the methods for model reduction, those based on the SVD are well-known for their beneficial theoretical properties, though they require O(n3) floating-point arithmetic operations, with n being in the range of 10^3 -- 10^4 for many practical applications. In this paper we propose several high performance implementations of the Balanced Stochastic Truncation method for model reduction. The new routines carefully distribute the computations among the computational resources of a hybrid platform composed of one (or more) multicore CPU(s) and a manycore GPU. Our results show that model reduction of a large-scale problem with 9,669 state variables, which previously required the use of a cluster of computers, can now be carried out in the target platform in less than 25 minutes.
['Peter Benner', 'Pablo Ezzatti', 'Enrique S. Quintana-Ortí', 'Alfredo Remón']
High Performance Implementations of the BST Method on Hybrid CPU-GPU Platforms