High performance Stingray

X-ray astrophysical objects show variability on a wide range of timescales, i.e. from fractions of second to years. The open-source Python library stingray is able to perform time series analyses with a focus on high-energy astrophysics. Comprising the most commonly used Fourier analyses techniques, it also supports a range of additional extensions able to analyse pulsar data, simulate data sets and perform statistical modelling. With the latest release of the code, new Fourier methods and support for additional missions have been implemented, making Stingray more easily adaptable and extendable to other use cases. In this paper we focus on testing the performance and robustness of the latest version of stingray. We consider the possible different behaviour of the code when dealing with data sets smaller or larger than the RAM. We address the problem of dealing with large data sets and implemented parallel versions of the slowest methods in this regime. For small data sets, we investigate the possibility of a porting in GPU of key functions of the code.