research
∙
04/22/2022
Efficient Pipeline Planning for Expedited Distributed DNN Training
To train modern large DNN models, pipeline parallelism has recently emer...
share
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
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
It is a challenging task to train large DNN models on sophisticated GPU ...
research
∙
11/24/2019
FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads
Performance optimization is the art of continuous seeking a harmonious m...
research
∙
10/14/2019
Characterizing Deep Learning Training Workloads on Alibaba-PAI
Modern deep learning models have been exploited in various domains, incl...
research
∙
10/11/2019
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning
Existing multi-view learning methods based on kernel function either req...
research
∙
10/10/2019
Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling
In real world machine learning applications, testing data may contain so...
research
∙
11/13/2018
FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs
In recent years, there is a surge on machine learning applications in in...
research
∙
04/30/2017
A Conditional Variational Framework for Dialog Generation
Deep latent variable models have been shown to facilitate the response g...
research
∙
04/12/2016
