NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System

分类与摘要

从处理器、指令、内存或近数据计算角度优化科学工作负载。

证据摘录：cai Jiang 1∗, Buxin Tu 1∗, Xiaoyu Hao 1, Junshi Chen 1,2 and Hong An 1,2,† 1School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 2Laoshan Laboratory, Qingdao, China Abstract—Linear-response time-dependent Density Functional Theory (LR-TDDFT) is a widely used method for accurately predicting the excited-state properties of physical systems. Pre- vious works have atte || cai Jiang 1∗, Buxin Tu 1∗, Xiaoyu Hao 1, Junshi Chen 1,2 and Hong An 1,2,† 1School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 2Laoshan Laboratory, Qingdao, China Abstract—Linear-response time-dependent Density Functional Theory (LR-TDDFT) is a widely used method for accurately predicting the excited-state properties of physical systems. Pre- vious work || ported by Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB0500102). The computing resources are financially sup- ported by Laoshan Laboratory (LSKJ202300305). REFERENCES [1] S. Aga, N. Jayasena, and M. Ignatowski. Co-ml: a case for co llaborative ml acceleration using near-data processing. In Proceedings of the International Symposium on Memory Systems , pages 506–517, 2019 || ported by Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB0500102). The computing resources are financially sup- ported by Laoshan Laboratory (LSKJ202300305). REFERENCES [1] S. Aga, N. Jayasena, and M. Ignatowski. Co-ml: a case for co llaborative ml acceleration using near-data processing. In Proceedings of the International Symposium on Memory Systems , pages 506–

引用

Qingcai Jiang*, Buxin Tu*, Xiaoyu Hao, Junshi Chen, Hong An， NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System, in the Proceeding of the 62th ACM/IEEE Design Automation Conference (DAC'2025) （CCFA）

@article{acsa2025_21,
  title = {NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System},
  year = {2025},
  doi = {10.1109/dac63849.2025.11133299}
}