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Collaborative Heterogeneity-Aware OS Scheduler for Asymmetric Multicore Processors.

, , , , , , and . IEEE Trans. Parallel Distributed Syst., 32 (5): 1224-1237 (2021)

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COLAB: a collaborative multi-factor scheduler for asymmetric multicore processors., , , , and . CGO, page 268-279. ACM, (2020)Machine Learning in Compilers: Past, Present and Future., and . FDL, page 1-8. IEEE, (2020)Using Graph Neural Networks to model the performance of Deep Neural Networks., , , and . CoRR, (2021)Value Function Based Performance Optimization of Deep Learning Workloads., , , and . CoRR, (2020)Priority Sampling of Large Language Models for Compilers., , , and . CoRR, (2024)Vectorization-aware loop unrolling with seed forwarding., , , , , , and . CC, page 1-13. ACM, (2020)Automating reinforcement learning architecture design for code optimization., , , , , , and . CC, page 129-143. ACM, (2022)CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research., , , , , , , , , and 2 other author(s). CGO, page 92-105. IEEE, (2022)A Case Study on Machine Learning for Synthesizing Benchmarks, , , , , and . Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, page 38–46. New York, NY, USA, Association for Computing Machinery, (2019)Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning., , , , , , and . CoRR, (2022)