88 citations · 234 across the 33 of their papers we have counts for
7 papers · 1 filter
Scaling Point-based Differentiable Rendering for Large-scale Reconstruction
Hexu Zhao, Xiaoteng Liu, Xiwen Min +6
Point-based Differentiable Rendering (PBDR) enables high-fidelity 3D scene reconstruction, but scaling PBDR to high-resolution and large scenes requires efficient distributed train…
CLM: Removing the GPU Memory Barrier for 3D Gaussian Splatting
Hexu Zhao, Xiwen Min, Xiaoteng Liu +6
3D Gaussian Splatting (3DGS) is an increasingly popular novel view synthesis approach due to its fast rendering time, and high-quality output. However, scaling 3DGS to large (or in…
Beyond Lamport, Towards Probabilistic Fair Ordering
Muhammad Haseeb, Jinkun Geng, Radhika Mittal +3
A growing class of applications demands \emph{fair ordering} of events, which ensures that events generated earlier are processed before later events. However, achieving such seque…
Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
Zhanghan Wang, Ding Ding, Hang Zhu +2
Distributed machine learning training and inference is common today because today's large models require more memory and compute than can be provided by a single GPU. Distributed m…
Understanding Stragglers in Large Model Training Using What-if Analysis
Jinkun Lin, Ziheng Jiang, Zuquan Song +13
Large language model (LLM) training is one of the most demanding distributed computations today, often requiring thousands of GPUs with frequent synchronization across machines. Su…
The Dawn of Disaggregation and the Coherence Conundrum: A Call for Federated Coherence
Jaewan Hong, Marcos K. Aguilera, Emmanuel Amaro +3
Disaggregated memory is an upcoming data center technology that will allow nodes (servers) to share data efficiently. Sharing data creates a debate on the level of cache coherence…