← Back to all news
arXiv Machine Learning By Denghui Lu, Alexander Maeder, Alexandros Nikolaos Ziogas, Mathieu Luisier, Manasa Kaniselvan

Accelerating SO(2)-Equivariant Materials Models through Low-Precision Emulation

Read the original on arXiv Machine Learning →

The paper introduces FlashSO(2), a framework that uses tailored low‑precision emulation under the integer Ozaki scheme to accelerate SO(2)-equivariant layers in graph neural networks for materials modeling. FlashSO(2) outperforms native low‑precision formats such as TF32 in throughput, accuracy, and memory usage, achieving up to 40× speed‑ups on NVIDIA Hopper GPUs and enabling faster training and simulation of ML interatomic potentials and Hamiltonian models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 3

TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning

The paper introduces TACO, a new optimizer for fine‑tuning large language models that drastically reduces optimizer state memory while preserving first‑order gradients. TACO selects the sign of the largest magnitude entry in each column of weight matrices, achieving a 174× reduction in persistent optimizer memory compared to AdamW8bit and a 2.9× decrease in peak training memory on OPT‑13B. This allows full‑parameter fine‑tuning of 30–32B‑parameter models on a single 80 GB GPU across multiple model families and tasks, with comparable accuracy and runtime to existing methods.

By Jichao Jiang (University of Central Florida), Cristian McGee (University of Central Florida), El Houcine Bergou (Mohammed VI Polytechnic University), Hanqin Cai (University of Central Florida), Aritra Dutta (University of Central Florida)
llmsfine-tuning
More like this →
arXiv AI
Jul 10

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale

arXiv:2605. 10886v3 Announce Type: replace-cross Abstract: Recent GPU generations deliver significantly higher FLOPs using lower-precision arithmetic, such as FP8.

By Liang Luo, Yinbin Ma, Quanyu Zhu, Vasiliy Kuznetsov, Yuxin Chen, Neng Shi, Jian Jiao, Jiecao Yu, Buyun Zhang, Tongyi Tang, Xiaohan Wei, Yanli Zhao, Zeliang Chen, Yuchen Hao, Venkatesh Ranganathan, Sandeep Parab, Yantao Yao, Maxim Naumov, Chunzhi Yang, Shen Li, Ellie Wen, Wenlin Chen, Santanu Kolay, Chunqiang Tang
llmsbenchmarks
More like this →
arXiv Machine Learning
Aug 4

Nova: An End-to-End MLIR Compiler for Deep Learning

arXiv:2608. 00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware.

By Adwaid Suresh, Aparna A, Harshini V M, Jona Delcy C A, Killi Uma Maheswara Rao, Ram Charan Golla, Surendra Vendra
efficiency
More like this →