DagTM: An Energy-Efficient Threads Grouping Mapping for Many-Core Systems Based on Data Affinity
AbstractMany-core processors are becoming mainstream computing platforms nowadays. How to map the application threads to specific processing cores and exploit the abundant hardware parallelism of a many-core processor efficiently has become a pressing need. This work proposes a data affinity based threads grouping mapping strategy Data Affinity Grouping based Thread Mapping (DagTM), which categorizes threads into different groups according to their data affinity and the hardware architecture feature of many-core processors. After that, the thread groups are mapped to the specific processing cores to be energy efficiently executed. More specifically, first, the intra-thread data locality is analyzed by computing the data reuse distance, and the inter-thread data affinity is quantified by affinity matrix. Second, the threads are categorized into different groups via affinity subtree spanning algorithm. Finally, the thread groups are assigned to different processing cores through static binding. DagTM is able to reduce conflicts of the shared memory access and additional data transmission, increase utilization of the computing resources, and reduce entire system energy consumption. Experimental results show that DagTM obtains a nearly 14% improvement in computing performance, and a nearly 10% reduction in energy consumption compared with the traditional thread mapping mechanism under the condition of not introducing additional runtime overhead. View Full-Text
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Ju, T.; Dong, X.; Chen, H.; Zhang, X. DagTM: An Energy-Efficient Threads Grouping Mapping for Many-Core Systems Based on Data Affinity. Energies 2016, 9, 754.
Ju T, Dong X, Chen H, Zhang X. DagTM: An Energy-Efficient Threads Grouping Mapping for Many-Core Systems Based on Data Affinity. Energies. 2016; 9(9):754.Chicago/Turabian Style
Ju, Tao; Dong, Xiaoshe; Chen, Heng; Zhang, Xingjun. 2016. "DagTM: An Energy-Efficient Threads Grouping Mapping for Many-Core Systems Based on Data Affinity." Energies 9, no. 9: 754.
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