In this document, we introduce Minas, a memory affinity management framework for cache-coherent NUMA Non-Uniform Memory Access) platforms, which provides either explicit memory affinity management or automatic one with efficiency and architecture abstraction for numerical scientic applications. The explicit tuning is based on an API called MAi (Memory Affinity interface) which provides simple functions to manage allocation and data placement using an extensive set of memory policies. An automatic tuning mechanism is provided by the preprocessor named MApp (Memory Anity preprocessor). MApp analyses both the application source code and the target cache-coherent NUMA platform characteristics in order to automatically apply MAi functions at compile time. Minas efficiency and architecture abstraction have been evaluated on two cache-coherent NUMA platforms using three numerical scientic HPC applications. The results have shown signicant gains when compared to other solutions available on Linux (First-touch, libnuma and numactl).