6 citations · 6 across the 3 of their papers we have counts for
4 papers
A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks
Yang Zhao, Yue Dai, Liang Qiao +8
Temporal graph neural networks (TGNNs) achieve superior performance in many dynamic graph learning tasks. These models use node state vectors (i.e., memories) to capture and record…
TERRA: A Hierarchical Parallel Training and Memory Orchestration Framework for High-Resolution AI-based Earth Modeling
Ruohan Wu, Ziqi Zhu, Yang Zhao +6
Training high-resolution AI-based Earth forecasting models is memory-intensive. Window-based Swin Transformers reduce the quadratic cost of global attention, but existing distribut…
Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling
Hua-Liang Liu, Hao Su, Si-Qiu Gong +34
The creation of large-scale, high-fidelity quantum computers is not only a fundamental scientific endeavour in itself, but also provides increasingly robust proofs of quantum compu…
SparStencil: Retargeting Sparse Tensor Cores to Scientific Stencil Computations via Structured Sparsity Transformation
Qi Li, Kun Li, Haozhi Han +7
Sparse Tensor Cores offer exceptional performance gains for AI workloads by exploiting structured 2:4 sparsity. However, their potential remains untapped for core scientific worklo…