Hi I used the full_analysis.py script until convergence for the 4 diffusion models (sphere, prolate, oblate, ellipsoid), each on one different computer. Those computer, however, are quite similar, all 32-bits x86 Gentoo Linux with same kernel, gcc, python, etc. For the final run, I switched on a different system, our dual core pseudo 64-bits NMR console computer running Red Hat Enterprise 4 with almost everything different from our Gentoo workstations which are really more up-to-date. Before starting the final run, I wanted to check if number rounding would be the same... Well, is wasn't and the run with the ellipsoid diffusion model ended up saying it wasn't converged yet : ##################### # Convergence tests # ##################### Chi-squared test: chi2 (k-1): 7022.7261139599996 chi2 (k): 7022.7261139563052 The chi-squared value has not converged. Identical model-free models test: The model-free models have converged. Identical parameter test: Spin system: 26 PHE Parameter: S2f Value (k-1): 0.84811676720047557 Value (k): 0.84811676720047491 The model-free parameters have not converged. Convergence: [ No ] As is obvious, the differences are really small, but still relax thinks it's enough to spend many hours more trying to get absolute reproducibility. My question. Is it really necessary to get convergence on so small digits ? Probably yes, as it was designed this way... So, if yes, why ? Isn't it a problem for multi-computer processing ? Thanks ! Séb :)