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NUMA-aware scheduling and memory allocation for data-flow task-parallel applications.

, , , , and . PPOPP, page 44:1-44:2. ACM, (2016)

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Progressive Raising in Multi-level IR., , , , , , and . CGO, page 15-26. IEEE, (2021)Leveraging Data-Flow Task Parallelism for Locality-Aware Dynamic Scheduling on Heterogeneous Platforms., , and . IPDPS Workshops, page 540-549. IEEE Computer Society, (2018)Automated Analysis of Task-Parallel Execution Behavior Via Artificial Neural Networks., , and . IPDPS Workshops, page 647-656. IEEE Computer Society, (2018)Accurate and Complete Hardware Profiling for OpenMP - Multiplexing Hardware Events Across Executions., , and . IWOMP, volume 10468 of Lecture Notes in Computer Science, page 266-280. Springer, (2017)Dynamic optimization of data-flow task-parallel applications for large-scale NUMA systems. (Optimisation dynamique des applications à base de tâches data-flow pour des machines NUMA).. Pierre and Marie Curie University, Paris, France, (2015)Fuse: Accurate Multiplexing of Hardware Performance Counters Across Executions., , and . ACM Trans. Archit. Code Optim., 14 (4): 43:1-43:26 (2017)OCC: An Automated End-to-End Machine Learning Optimizing Compiler for Computing-In-Memory., , , , , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 41 (6): 1674-1686 (2022)AfterOMPT: An OMPT-Based Tool for Fine-Grained Tracing of Tasks and Loops., , , and . IWOMP, volume 12295 of Lecture Notes in Computer Science, page 165-180. Springer, (2020)Scalable Task Parallelism for NUMA: A Uniform Abstraction for Coordinated Scheduling and Memory Management., , , , and . PACT, page 125-137. ACM, (2016)