Current high performance computing architectures are composed of large shared memory NUMA nodes, among other components. Such nodes are becoming increasingly complex as they have several NUMA domains with different access latencies depending on the core where the access is issued. In this work, we propose techniques to efficiently mitigate the negative impact of NUMA effects on parallel applications performance. We leverage runtime system metadata expressed in terms of a task dependency graph, where nodes are sequential pieces of code and edges are control or data dependencies between them, to efficiently reduce data transfers using graph partitioning techniques. With our proposals, we are able to improve the execution time of OpenMP parallel codes a factor of $2.02\times$ on average when run on architectures with strong NUMA effects.