Task mapping in heterogeneous embedded systems for fast completion time

Graphics processing units are being widely used in embedded systems as they can achieve high performance and energy efficiency. In such systems, the problem of computation and data mapping for multiple applications while minimizing the completion time is quite challenging due to a large size of the policy space, including heterogeneous application characteristics, complex application structure, data communication costs, and data partitioning. To achieve fast competition time, a fine-grain mapping framework that explores a set of critical factors is needed for heterogeneous embedded systems. In this paper, we consider this mapping problem by presenting a theoretical framework that yields an optimal integer programming solution. Moreover, based upon several interesting measurements-based case studies, we design three practical mapping algorithms with low time complexity, each of which explores a specific set of factors that may affect the completion time performance. We evaluated the proposed algorithms by implementing them on a real heterogeneous system and using a large set of popular benchmarks for evaluation. Experimental results demonstrate that our proposed algorithms can achieve up to 30% faster completion time compared to the state-of-the-art mapping techniques, and can perform consistently well across different workloads.