19 citations · 82 across the 18 of their papers we have counts for
4 papers · 1 filter
ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs
Guyue Huang, Yang Bai, Liu Liu +4
Pipelining between data loading and computation is a critical tensor program optimization for GPUs. In order to unleash the high performance of latest GPUs, we must perform a syner…
MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms
Yuke Wang, Boyuan Feng, Zheng Wang +4
The increasing size of input graphs for graph neural networks (GNNs) highlights the demand for using multi-GPU platforms. However, existing multi-GPU GNN systems optimize the compu…
OneQ: A Compilation Framework for Photonic One-Way Quantum Computation
Hezi Zhang, Anbang Wu, Yuke Wang +4
In this paper, we propose OneQ, the first optimizing compilation framework for one-way quantum computation towards realistic photonic quantum architectures. Unlike previous compila…
GMI-DRL: Empowering Multi-GPU Deep Reinforcement Learning with GPU Spatial Multiplexing
Yuke Wang, Boyuan Feng, Zheng Wang +3
With the increasing popularity of robotics in industrial control and autonomous driving, deep reinforcement learning (DRL) raises the attention of various fields. However, DRL comp…