10 citations · 17 across the 7 of their papers we have counts for
7 papers
How Good Can Linear Models Be for Time-Series Forecasting?
Lang Huang, Jinglue Xu, Luke Darlow
Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that ca…
Scaling Laws of Global Weather Models
Yuejiang Yu, Langwen Huang, Alexandru Calotoiu +1
Data-driven models are revolutionizing weather forecasting. To optimize training efficiency and model performance, this paper analyzes empirical scaling laws within this domain. We…
EBCC v0.2: Error bounded compression for weather and climate applications
Langwen Huang, Luigi Fusco, Florian Scheidl +5
As the resolution of weather and climate simulations increases, the amount of data produced is growing rapidly from hundreds of terabytes to tens of petabytes. These data volumes l…
CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
Tiancheng Chen, Ales Kubicek, Langwen Huang +1
Training large language models (LLMs) now requires resources that exceed a single datacenter, making cross-datacenter strategies increasingly crucial. We present CrossPipe, a frame…
LLAMP: Assessing Network Latency Tolerance of HPC Applications with Linear Programming
Siyuan Shen, Langwen Huang, Marcin Chrapek +5
The shift towards high-bandwidth networks driven by AI workloads in data centers and HPC clusters has unintentionally aggravated network latency, adversely affecting the performanc…
Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
Lukas Gianinazzi, Alexandros Nikolaos Ziogas, Langwen Huang +9
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In…
DiffDA: a Diffusion Model for Weather-scale Data Assimilation
Langwen Huang, Lukas Gianinazzi, Yuejiang Yu +2
The generation of initial conditions via accurate data assimilation is crucial for weather forecasting and climate modeling. We propose DiffDA as a denoising diffusion model capabl…