Schedule Data, Not Code
In this paper, the authors argue that the scheduler, as the intermediary between hardware and software, needs to be fully data-aware. The old paradigm of envisioning tasks as amorphous blobs of 'Work' to be assigned to processors is incomplete and needs be expanded. Some techniques and projects have emerged that implicitly use this idea, but either focus on a small aspect of data or are targeted to optimizing specific problems. They argue for more general solutions.