7 papers
Ascend to Science: Exploration of AI Chips for Scientific Computing
Weicheng Xue, Kai Yang, Yongxiang Liu +5
The rapid rise of AI-oriented accelerators has reshaped compute systems around low-precision tensor engines, raising a practical question for the HPC community: under what conditio…
SGEMM-cube: Precision-Recovery FP32 GEMM Approximation on Ascend NPUs with FP16 Matrix Engines
Weicheng Xue, Baisong Xu, Kai Yang +4
Modern AI accelerators provide high-throughput low-precision matrix engines, but often lack efficient support for FP32 GEMM. This paper presents SGEMM-cube, an FP32-accuracy GEMM a…
SMC-AI: Scaling Monte Carlo Simulation to Four Trillion Atoms with AI Accelerators
Xianglin Liu, Kai Yang, Fanli Zhou +9
The rapid advancement of deep learning is reshaping the hardware design landscape toward AI tasks, posing fundamental challenges for HPC workloads such as atomistic simulation. Her…
Towards Computational Microscope of Chemical Order-Disorder via ML-Accelerated Monte Carlo Simulation
Fanli Zhou, Hao Chen, Pengxiang Xu +3
Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-drive…
SMC-X: A Distributed Scalable Monte Carlo Simulation Method for Chemically Complex Alloys
Xianglin Liu, Kai Yang, Fanli Zhou +1
To predict the complex chemical evolution in multicomponent alloys, it is highly desirable to have accurate atomistic simulation methods capable of reaching sufficiently large spat…
Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT
Da Chang, Peng Xue, Yu Li +3
Parameter-Efficient Fine-Tuning (PEFT) methods are crucial for adapting large pre-trained models. Among these, LoRA is considered a foundational approach. Building on this, the inf…