@inproceedings{10.1145/3241539.3241552,
author = {Huang, Yan and Li, Shaoran and Hou, Y. Thomas and Lou, Wenjing},
title = {GPF: A GPU-Based Design to Achieve ~100 μs Scheduling for 5G NR},
year = {2018},
isbn = {9781450359030},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {[https://doi.org/10.1145/3241539.3241552](https://doi.org/10.1145/3241539.3241552)},
doi = {10.1145/3241539.3241552},
abstract = {5G New Radio (NR) is designed to operate under a broad range of frequency bands and support new applications with ultra-low latency. To support its diverse operating conditions, a set of different OFDM numerologies has been defined in the standards body. Under this numerology, it is necessary to perform scheduling with a time resolution of ∼100 μs. This requirement poses a new challenge that does not exist in LTE and cannot be supported by any existing LTE schedulers. In this paper, we present the design of GPF -- a GPU-based proportional fair (PF) scheduler that can meet the ∼100 μs time requirement. The key ideas include decomposing the scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU. By implementing GPF on an off-the-shelf Nvidia Quadro P6000 GPU, we show that GPF is able to achieve near-optimal performance while meeting the ∼100 $mathrmμs time requirement. GPF represents the first successful design of a GPU-based PF scheduler that can meet the new time requirement in NR.},
booktitle = {Proceedings of the 24th Annual International Conference on Mobile Computing and Networking},
pages = {207–222},
numpages = {16},
keywords = {gpu, 5g nr, resource scheduling, real-time, optimization},
location = {New Delhi, India},
series = {MobiCom '18}
}