  Recurrence quantification analysis (RQA) is a well established method of
nonlinear data analysis. In this work we present a new strategy for an almost
parameter-free RQA. The approach finally omits the choice of the threshold
parameter by calculating the RQA measures for a range of thresholds (in fact
recurrence rates). Specifically, we test the ability of the RQA measure
determinism, to sort data with respect to their signal to noise ratios. We
consider a periodic signal, simple chaotic logistic equation, and Lorenz system
in the tested data set with different and even very small signal to noise
ratios of lengths $10^2, 10^3, 10^4,$ and $10^5$. To make the calculations
possible a new effective algorithm was developed for streamlining of the
numerical operations on Graphics Processing Unit (GPU).
