1 citations · 2 across the 4 of their papers we have counts for
5 papers
Conduit: An Experience Data Plane for Distributed Reinforcement Learning
Sitong Zhang, Tuo Shi, Mario Di Francesco +2
Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, however, the buffer becomes more tha…
HyperParallel-FSDP: Topology-Aware Fully Sharded Training with Layout-Driven Muon on Ascend SuperPods
Mo Sun, Yifan Yao, Yanwei Liu +14
Declarative SPMD programming uses tensor sharding descriptions to drive distributed execution, separating parallelization from model code. However, the evaluated PyTorch DTensor st…
Breaking the Storage-Compute Bottleneck in Billion-Scale ANNS: A GPU-Driven Asynchronous I/O Framework
Yang Xiao, Mo Sun, Ziyu Song +4
With the advancement of information retrieval, recommendation systems, and Retrieval-Augmented Generation (RAG), Approximate Nearest Neighbor Search (ANNS) gains widespread applica…
FpgaHub: Fpga-centric Hyper-heterogeneous Computing Platform for Big Data Analytics
Zeke Wang, Jie Zhang, Hongjing Huang +10
Modern data analytics requires a huge amount of computing power and processes a massive amount of data. At the same time, the underlying computing platform is becoming much more he…
TorchGT: A Holistic System for Large-scale Graph Transformer Training
Meng Zhang, Jie Sun, Qinghao Hu +4
Graph Transformer is a new architecture that surpasses GNNs in graph learning. While there emerge inspiring algorithm advancements, their practical adoption is still limited, parti…