402 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Alibaba Group (China)CN60 papers
- Zhejiang UniversityCN46 papers
- Bellevue Hospital CenterUS30 papers
- Peking UniversityCN22 papers
- Nanyang Technological UniversitySG21 papers
- Tsinghua UniversityCN15 papers
- Chinese Academy of SciencesCN14 papers
- Renmin University of ChinaCN13 papers
- Shanghai Jiao Tong UniversityCN11 papers
- Tianjin UniversityCN11 papers
- Alibaba Group (Cayman Islands)KY10 papers
- East China Normal UniversityCN10 papers
11 papers · 1 filter
HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis
Shiwei Zhang, Lansong Diao, Chuan Wu +3
Single-Program-Multiple-Data (SPMD) parallelism has recently been adopted to train large deep neural networks (DNNs). Few studies have explored its applicability on heterogeneous c…
Towards Transmission-Friendly and Robust CNN Models over Cloud and Device
Chuntao Ding, Zhichao Lu, Felix Juefei-Xu +3
Deploying deep convolutional neural network (CNN) models on ubiquitous Internet of Things (IoT) devices has attracted much attention from industry and academia since it greatly fac…
GearV: A Two-Gear Hypervisor for Mixed-Criticality IoT Systems
Kaiwen Long, Chong Xing, Yuebin Qi +10
This paper presents GearV, a two-gear lightweight hypervisor architecture to address the some known challenges. By dividing hypervisor into some partitions, and dividing scheduling…
FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Compute
Ao Wang, Shuai Chang, Huangshi Tian +5
Serverless computing, or Function-as-a-Service (FaaS), enables a new way of building and scaling applications by allowing users to deploy fine-grained functions while providing ful…
DISC: A Dynamic Shape Compiler for Machine Learning Workloads
Kai Zhu, Wenyi Zhao, Zhen Zheng +8
Many recent machine learning models show dynamic shape characteristics. However, existing AI compiler optimization systems suffer a lot from problems brought by dynamic shape model…
FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads
Zhen Zheng, Pengzhan Zhao, Guoping Long +6
We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range…
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
Shiqing Fan, Yi Rong, Chen Meng +10
It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an e…
HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy
Yao Chen, Xin Long, Jiong He +5
The pervasive adoption of Deep Learning (DL) and Graph Processing (GP) makes it a de facto requirement to build large-scale clusters of heterogeneous accelerators including GPUs an…
FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads
Guoping Long, Jun Yang, Wei Lin
Performance optimization is the art of continuous seeking a harmonious mapping between the application domain and hardware. Recent years have witnessed a surge of deep learning (DL…
AliGraph: A Comprehensive Graph Neural Network Platform
Rong Zhu, Kun Zhao, Hongxia Yang +5
An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements. Graph…
Fork and Join Queueing Networks with Heavy Tails: Scaling Dimension and Throughput Limit
Yun Zeng, Jian Tan, Cathy H. Xia
Parallel and distributed computing systems are foundational to the success of cloud computing and big data analytics. These systems process computational workflows in a way that ca…