CATS: cache aware task-stealing based on online profiling in multi-socket multi-core architectures
Proceedings of the 26th ACM international conference on Supercomputing
CAP: co-scheduling based on asymptotic profiling in CPU+GPU hybrid systems
Proceedings of the 2013 International Workshop on Programming Models and Applications for Multicores and Manycores
DWS: Demand-aware Work-Stealing in Multi-programmed Multi-core Architectures
Proceedings of Programming Models and Applications on Multicores and Manycores
Adaptive workload-aware task scheduling for single-ISA asymmetric multicore architectures
ACM Transactions on Architecture and Code Optimization (TACO)
CPU+GPU scheduling with asymptotic profiling
Parallel Computing
| Hi-index | 0.00 |
Asymmetric Multi-Core (AMC) architectures have shown high performance as well as power efficiency. However, current parallel programming environments do not perform well on AMC due to their assumption that all cores are symmetric and provide equal performance. Their random task scheduling policies, such as task-stealing, can result in unbalanced workloads in AMC and severely degrade the performance of parallel applications. To balance the workloads of parallel applications in AMC, this paper proposes a Workload-Aware Task Scheduling (WATS) scheme that adopts history-based task allocation and preference-based task stealing. The history-based task allocation is based on a near-optimal, static task allocation using the historical statistics collected during the execution of a parallel application. The preference-based task stealing, which steals tasks based on a preference list, can dynamically adjust the workloads in AMC if the task allocation is less optimal due to approximation in the history-based task allocation. Experimental results show that WATS can improve the performance of CPU-bound applications up to 82.7% compared with the random task scheduling policies.