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While High Performance Computing systems are increas-ingly based on heterogeneous cores, their effectiveness depends on howwell the scheduler can allocate workloads onto appropriate computing de-vices and how communication and computation can be overlapped. Withdifferent types of resources integrated into one system, the complexity ofthe scheduler correspondingly increases. Moreover, for applications withvarying problem sizes on different heterogeneous resources, the optimalscheduling approach may vary accordingly. We thus present PDAWL, anevent-driven profile-based Iterative Dynamic Adaptive Work-Load bal-ance scheduling approach to dynamically and adaptively adjust workloadto efficiently utilize heterogeneous resources. It combines online schedul-ing (DAWL), which can adaptively adjust workload based on availablereal time heterogeneous resources, with an offline machine learning (profile-based estimation model) which can build a device-specific communica-tion computation estimation model. Our scheduling approach is tested oncontrol-regular applications, Stencil kernel (based on a Jacobi Algorithm)and Sparse Matrix-Vector Multiplication (SpMV) in an event-driven run-time system. Experimental results show that PDAWL is either on-par orfar outperforms whichever yields the best results (CPU or GPU). 

While High Performance Computing systems are increasingly based on heterogeneous cores, their eectiveness depends on how well the scheduler can allocate workloads onto appropriate computing devices and how communication and computation can be overlapped. With dierent types of resources integrated into one system, the complexity of the scheduler correspondingly increases. Moreover, for applications with varying problem sizes on dierent heterogeneous resources, the optimal scheduling approach may vary accordingly. We thus present PDAWL, an event-driven prole-based Iterative Dynamic Adaptive Work-Load balance scheduling approach to dynamically and adaptively adjust workload to eciently utilize heterogeneous resources. It combines online scheduling (DAWL), which can adaptively adjust workload based on available real time heterogeneous resources, with an oine machine learning (prolebased estimation model) which can build a device-specic communication computation estimation model. Our scheduling approach is tested on control-regular applications, Stencil kernel (based on a Jacobi Algorithm) and Sparse Matrix-Vector Multiplication (SpMV) in an event-driven runtime system. Experimental results show that PDAWL is either on-par or far outperforms whichever yields the best results (CPU or GPU).