We describe an approach that uses combinatorial optimization and machine learning to share the work between the host and device of heterogeneous computing systems such that the overall application execution time is minimized. We propose to use combinatorial optimization to search for the optimal system configuration in the given parameter space (such as, the number of threads, thread affinity, work distribution for the host and device). For each system configuration that is suggested by combinatorial optimization, we use machine learning for evaluation of the system performance. We evaluate our approach experimentally using a heterogeneous platform that comprises two 12-core Intel Xeon E5 CPUs and an Intel Xeon Phi 7120P co-processor with 61 cores. Using our approach we are able to find a near-optimal system configuration by performing only about 5 of all possible experiments.
A trend that has materialized, and has given rise to much attention, is of the increasingly heterogeneous computing platforms. Recently, it has become very common for a desktop or a notebook computer to be equipped with both a multi-core CPU and a GPU. Application development for exploiting the aggregate computing power of such an environment is a major challenge today. Particularly, we need dynamic work distribution schemes that are adaptable to different computation and communication patterns in applications, and to various heterogeneous configurations. This paper describes a general dynamic scheduling framework for mapping applications with different communication patterns to heterogeneous architectures. We first make key observations about the architectural tradeoffs among heterogeneous resources and the communication pattern of an application, and then infer constraints for the dynamic scheduler. We then present a novel cost model for choosing the optimal chunk size in a heterogeneous configuration. Finally, based on general framework and cost model we provide optimized work distribution schemes to further improve the performance.