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Optimizing Memory Mapping Using Deep Reinforcement Learning. (arXiv:2305.07440v1 [cs.PF])
cs.LG updates on arXiv.org arxiv.org
Resource scheduling and allocation is a critical component of many high
impact systems ranging from congestion control to cloud computing. Finding more
optimal solutions to these problems often has significant impact on resource
and time savings, reducing device wear-and-tear, and even potentially improving
carbon emissions. In this paper, we focus on a specific instance of a
scheduling problem, namely the memory mapping problem that occurs during
compilation of machine learning programs: That is, mapping tensors to different
memory layers to …
arxiv carbon cloud cloud computing computing congestion control emissions focus impact mapping memory paper reinforcement reinforcement learning scheduling solutions systems