To solve big data problems which occur in modern data mining applications, a comprehensive approach is required that combines a flexible model and an optimisation algorithm with fast convergence and a potential for efficient parallelisation both in the number of data points and the number of features. In this paper we present an algorithm for fitting additive models based on the basis expansion principle. The classical backfitting algorithm that solves the underlying normal equations cannot be properly parallelised due to inherent data dependencies and leads to a limited error reduction under certain circumstances. Instead, we suggest a modified BiCGStab method adapted to suit the special block structure of the problem. The new method demonstrates superior convergence speed and promising parallel scalability. We discuss the convergence properties of the method and investigate its convergence and scalability further using a set of benchmark problems.
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