A Data-Oriented Method for Scheduling Dependent Tasks on High-Density Multi-GPU Systems

The rapidly-changing computer architectures, though improving the performance of computers, have been challenging the programming environments for efficiently harnessing the potential of novel architectures. In this area, though the high-density multi-GPU architecture enabled unparalleled performance advantage of dense GPUs in a single server, it has increased the difficulty for scheduling diversified and dependent tasks. We therefore propose a data-oriented method for scheduling dependent tasks for this architecture while providing its implementation. In our method, we model a parallel program as a collection of data-dependent tasks for which data dependencies are managed by an expressive matrix. Accordingly, we develop a hierarchical scheduler infrastructure for our model. In this, a top scheduler is built for querying the data-dependency matrix; three downstream schedulers for queuing computation tasks that are exclusively assigned to processor, accelerator or either; and a multitude of bottom schedulers each for providing a processing element with assigned tasks. We experiment our scheduler for examples of Strassen matrix multiplication and Cholesky matrix inversion algorithms on a computer that has 8 Tesla K40 GPUs. The results show that our method is capable of offering the efficient task parallelism while fulfilling the complex task dependencies. When advanced task-oriented schedulers have been widely designed for distributed systems, a lightweight data-driven scheduler could be an alternative and handy approach that can handle the dependent yet diversified tasks of data-intensive applications for the novel high-density multi-accelerator system.