{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:45:00Z","timestamp":1723016700001},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Running time is a key metric across the standard physical design flow stages. However, with the rapid growth in design sizes, routing runtime has become the runtime bottleneck in the physical design flow. To improve the effectiveness of the modern global router, we propose a global routing framework with GPU-accelerated routing algorithms and a heterogeneous task graph scheduler, called FastGR. Its runtime-oriented version FastGRL achieves 2.489\u00d7 speedup compared with the state-of-the-art global router. Furthermore, the GPU-accelerated L-shape pattern routing used in FastGRL can contribute to 9.324\u00d7 speedup over the sequential algorithm on CPU. Its quality-oriented version FastGRH offers further quality improvement over FastGRL with similar acceleration.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/720","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"6458-6462","source":"Crossref","is-referenced-by-count":0,"title":["FastGR: Global Routing on CPU-GPU with Heterogeneous Task Graph Scheduler (Extended Abstract)"],"prefix":"10.24963","author":[{"given":"Siting","family":"Liu","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, SAR, China"},{"name":"Peking University, Beijing, China"}]},{"given":"Yuan","family":"Pu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, SAR, China"}]},{"given":"Peiyu","family":"Liao","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, SAR, China"},{"name":"Peking University, Beijing, China"}]},{"given":"Hongzhong","family":"Wu","sequence":"additional","affiliation":[{"name":"HiSilicon Technologies Co., Shenzhen, China"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"HiSilicon Technologies Co., Shenzhen, China"}]},{"given":"Zhitang","family":"Chen","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s Ark Lab, Hong Kong, SAR, China"}]},{"given":"Wenlong","family":"Lv","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s Ark Lab, Hong Kong, SAR, China"}]},{"given":"Yibo","family":"Lin","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Bei","family":"Yu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, SAR, China"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:54:34Z","timestamp":1691744074000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/720"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/720","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}