  This paper describes the numerical implementation in a high-performance
computing environment of an open-source library for model order reduction in
fluid dynamics. This library, called pyLOM, contains the algorithms of proper
orthogonal decomposition (POD), dynamic mode decomposition (DMD) and spectral
proper orthogonal decomposition (SPOD), as well as, efficient SVD and
matrix-matrix multiplication, all of them tailored for supercomputers. The
library is profiled in detail under the MareNostrum IV supercomputer. The
bottleneck is found to be in the QR factorization, which has been solved by an
efficient binary tree communications pattern. Strong and weak scalability
benchmarks reveal that the serial part (i.e., the part of the code that cannot
be parallelized) of these algorithms is under 10% for the strong scaling and
under 0.7% for the weak scaling. Using pyLOM, a POD of a dataset containing
1.14 x 108 gridpoints and 1808 snapshots that takes 6.3Tb of memory can be
computed in 81.08 seconds using 10368 CPUs. Additioally, the algorithms are
validated using the datasets of a flow around a circular cylinder at ReD = 100
and ReD = 1 x 104, as well as the flow in the Stanford diffuser at Reh = 1 x
104.
