Distributed model validation with Epsilon

Abstract Scalable performance is a major challenge with current model management tools. As the size and complexity of models programs increases cost computing falls, one solution for improving to perform computations on multiple computers. In this paper, we demonstrate low-overhead data-parallel approach distributed validation in context an OCL-like language. Our minimises communication costs by exploiting deterministic structure can take advantage cores each (heterogeneous) machine highly configurable computational granularity. evaluation shows that implementation extremely low overhead, achieving speed up 24.5 $$\times $$ × 26 computers over sequential case, 122 when utilising all six computer.