61 citations · 84 across the 8 of their papers we have counts for
8 papers
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
Hanjing Wang, Man-Kit Sit, Congjie He +5
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transf…
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Zeyuan Tan, Xiulong Yuan, Congjie He +7
Systems for serving inference requests on graph neural networks (GNN) must combine low latency with high throughout, but they face irregular computation due to skew in the number o…
TorchOpt: An Efficient Library for Differentiable Optimization
Jie Ren, Xidong Feng, Bo Liu +4
Recent years have witnessed the booming of various differentiable optimization algorithms. These algorithms exhibit different execution patterns, and their execution needs massive…
MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
Jie Ren, Wenteng Liang, Ran Yan +3
Large-scale Bundle Adjustment (BA) requires massive memory and computation resources which are difficult to be fulfilled by existing BA libraries. In this paper, we propose MegBA,…
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo
Le Xu, Shivaram Venkataraman, Indranil Gupta +2
Resource provisioning in multi-tenant stream processing systems faces the dual challenges of keeping resource utilization high (without over-provisioning), and ensuring performance…
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers
Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich +3
Deep learning models are trained on servers with many GPUs, and training must scale with the number of GPUs. Systems such as TensorFlow and Caffe2 train models with parallel synchr…