research
∙
03/03/2023
Ada-Grouper: Accelerating Pipeline Parallelism in Preempted Network by Adaptive Group-Scheduling for Micro-Batches
Pipeline parallelism has been demonstrated to be a remarkable approach t...
share
research
∙
02/16/2023
Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform
We present Rhino, a system for accelerating tensor programs with automat...
research
∙
02/13/2023
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment
This paper presents TAG, an automatic system to derive optimized DNN tra...
research
∙
09/26/2022
Optimizing DNN Compilation for Distributed Training with Joint OP and Tensor Fusion
This paper proposes DisCo, an automatic deep learning compilation module...
research
∙
03/09/2021
DISC: A Dynamic Shape Compiler for Machine Learning Workloads
Many recent machine learning models show dynamic shape characteristics. ...
research
∙
09/23/2020
FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads
We show in this work that memory intensive computations can result in se...
research
∙
07/08/2020
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads
The last decade has witnessed growth in the computational requirements f...
research
∙
07/02/2020
