In modern shared-memory NUMA systems which typically consist of two or more multi-core processor packages with local memory, 
affinity of data to computation is crucial for achieving high performance with an OpenMP program. 
OpenMP 3.0 introduced support for task-parallel programs in 2008 and has continued to extend its applicability and expressiveness. 
However, the ability to support data affinity of tasks is missing. In this paper, we investigate several approaches for task-to-data 
affinity that combine locality-aware task distribution and task stealing. We introduce the task affinity clause that will be part of OpenMP 5.0 
and provide the reasoning behind its design. Evaluation with our experimental implementation in the LLVM OpenMP runtime shows that 
task affinity improves execution performance up to 4.5x on an 8-socket NUMA machine and significantly reduces runtime variability of OpenMP tasks. 
Our results demonstrate that a variety of applications can benefit from task affinity and that the presented clause is 
closing the gap of task-to-data affinity in OpenMP 5.0.