Optimization of the HEFT Algorithm for a CPU-GPU Environment

Scheduling applications efficiently on a network of computing systems is crucial for high performance. This problem is known to be NP-Hard and is further complicated when applied to a CPU-GPU heterogeneous environment. Heuristic algorithms like Heterogeneous Earliest Finish Time (HEFT) have shown to produce good results for other heterogeneous environments like Grids and Clusters. In this paper, we propose a novel optimization of this algorithm that takes advantage of dissimilar execution times of the processors in the chosen environment. We optimize both the task ranking as well as the processor selection steps of the HEFT algorithm. By balancing the locally optimal result with the globally optimal result, we show that performance can be improved significantly without any change in the complexity of the algorithm (as compared to HEFT). Using randomly generated Directed A cyclic Graphs (DAGs), the new algorithm HEFT-NC (No-Cross) is compared with HEFT both in terms of speedup and schedule length. We show that the HEFT-NC outperforms HEFT algorithm and is consistent across different graph shapes and task sizes.