Locality-aware task management for unstructured parallelism

Author:

Yoo Richard M.1,Hughes Christopher J.1,Kim Changkyu1,Chen Yen-Kuang1,Kozyrakis Christos2

Affiliation:

1. Intel Labs, Santa Clara, CA, USA

2. Stanford University, Stanford, CA, USA

Publisher

ACM

Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enabling HW-Based Task Scheduling in Large Multicore Architectures;IEEE Transactions on Computers;2024-01

2. Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System Architecture;Electronics;2022-12-23

3. Memory-Aware Scheduling of Tasks Sharing Data on Multiple GPUs with Dynamic Runtime Systems;2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2022-05

4. TaskStream: accelerating task-parallel workloads by recovering program structure;Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems;2022-02-22

5. Locality-Aware Scheduling of Independent Tasks for Runtime Systems;Euro-Par 2021: Parallel Processing Workshops;2022

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