Data layout transformations are an effective technique for improving application memory performance. However, selecting the best layout remains a challenge because of application and architectural dependencies. In this thesis, we propose a framework that analyses a multi-kernel application and machine combination, selects a better memory layout, and automatically rewrites the kernels to use it. We design a layout transformer to automate layout changes (e.g., structure splitting and field reordering) and a benchmarking framework to study the impact of layouts on representative kernels, thereby deriving optimisation guidelines. Based on the findings, we aim to design an analytical model that selects a performance-improving layout for a given (application, machine) combination from the layouts our transformer can produce.