Existing numerical algorithms face their limits when running on a large number of cores. For instance, parallel iterative methods meet serious scalability limitation due to the synchronization procedure occurring between the processors at the end of each iteration. The traditional scheme for parallel iterative algorithms is synchronous iterations. This describes a method where a new iteration is only started when all the data from the previous one has been received. These iterative algorithms have been widely studied and are often simply called parallel iterative algorithms, synchronous being omitted. Another kind of iterative scheme, called chaotic iterations, can help solve these scalability problems. Iterative domain decomposition methods are well suited for parallel computations. Indeed, the division of a problem into smaller subproblems, through artificial subdivisions of the domain, is a means for introducing parallelism. Iterative domain decomposition strategies include in one way or another the following ingredients : (i) a decomposer to split a mesh into subdomains ; (ii) local solvers to find solutions for the subdomains for specific boundary conditions on the interface ; (iii) interface conditions enforcing compatibility and equilibrium between overlapping or non-overlapping subdomains ; (iv) an iterative solution strategy for the interface problem. The differences between the methods lies in how those ingredients are actually put to work and how they are combined to form an efficient solution strategy for the problem at hand. This talk shows how iterative domain decomposition methods have efficiently evolved over the years. In order to use such methods on massive parallel computers, the iterative scheme should be modified, and chaotic iterations are here proposed for the solution strategy of the interface problem, leading to some convergence difficulties. After the presentation of the method, numerical experiments are performed in parallel on large scale engineering problems to illustrate the robustness and efficiency of the proposed method. [1] F. Magoulès and G. Gbikpi-Benissan. JACK : An asynchronous communication kernel library for iterative algorithms. Journal of Supercomputing, pages 1-20, (2016, in press). [2] F. Magoulès, F.-X. Roux, and G. Houzeaux. Parallel Scientific Computing. Computer Engineering Series. Wiley-ISTE, London, UK, 2015. [3] D.P. Bertsekas and J.N. Tsitsiklis. Parallel and Distributed Computation : Numerical Methods. Prentice-Hall, 1989.