%0 Conference Paper %1 Chen:2012:CCA:2304576.2304599 %A Chen, Quan %A Guo, Minyi %A Huang, Zhiyi %B Proceedings of the 26th ACM international conference on Supercomputing %C New York, NY, USA %D 2012 %I ACM %K profiling task-stealing %P 163--172 %R 10.1145/2304576.2304599 %T CATS: cache aware task-stealing based on online profiling in multi-socket multi-core architectures %X Multi-socket Multi-core architectures with shared caches in each socket have become mainstream when a single multi-core chip cannot provide enough computing capacity for high performance computing. However, traditional task-stealing schedulers tend to pollute the shared cache and incur severe cache misses due to their randomness in stealing. To address the problem, this paper proposes a Cache Aware Task-Stealing (CATS) scheduler, which uses the shared cache efficiently with an online profiling method and schedules tasks with shared data to the same socket. CATS adopts an online DAG partitioner based on the profiling information to ensure tasks with shared data can efficiently utilize the shared cache. One outstanding novelty of CATS is that it does not require any extra user-provided information. Experimental results show that CATS can improve the performance of memory-bound programs up to 74.4% compared with the traditional task-stealing scheduler. %@ 978-1-4503-1316-2

@inproceedings{Chen:2012:CCA:2304576.2304599, abstract = {Multi-socket Multi-core architectures with shared caches in each socket have become mainstream when a single multi-core chip cannot provide enough computing capacity for high performance computing. However, traditional task-stealing schedulers tend to pollute the shared cache and incur severe cache misses due to their randomness in stealing. To address the problem, this paper proposes a Cache Aware Task-Stealing (CATS) scheduler, which uses the shared cache efficiently with an online profiling method and schedules tasks with shared data to the same socket. CATS adopts an online DAG partitioner based on the profiling information to ensure tasks with shared data can efficiently utilize the shared cache. One outstanding novelty of CATS is that it does not require any extra user-provided information. Experimental results show that CATS can improve the performance of memory-bound programs up to 74.4% compared with the traditional task-stealing scheduler.}, acmid = {2304599}, added-at = {2012-11-07T02:09:32.000+0100}, address = {New York, NY, USA}, author = {Chen, Quan and Guo, Minyi and Huang, Zhiyi}, biburl = {https://www.bibsonomy.org/bibtex/26e791cdbc4e502e6ac436d8c71f64c6f/ytyoun}, booktitle = {Proceedings of the 26th ACM international conference on Supercomputing}, doi = {10.1145/2304576.2304599}, interhash = {5e5b2d69c665a18d2aa8c4198403d1dd}, intrahash = {6e791cdbc4e502e6ac436d8c71f64c6f}, isbn = {978-1-4503-1316-2}, keywords = {profiling task-stealing}, location = {San Servolo Island, Venice, Italy}, numpages = {10}, pages = {163--172}, publisher = {ACM}, series = {ICS '12}, timestamp = {2012-11-07T02:09:32.000+0100}, title = {CATS: cache aware task-stealing based on online profiling in multi-socket multi-core architectures}, year = 2012 }