213 citations · 240 across the 12 of their papers we have counts for
12 papers
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection
Jiaxing Qi, Shaohan Huang, Zhongzhi Luan +3
The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-…
Mimose: An Input-Aware Checkpointing Planner for Efficient Training on GPU
Jianjin Liao, Mingzhen Li, Qingxiao Sun +8
Larger deep learning models usually lead to higher model quality with an ever-increasing GPU memory footprint. Although tensor checkpointing techniques have been proposed to enable…
EasyScale: Accuracy-consistent Elastic Training for Deep Learning
Mingzhen Li, Wencong Xiao, Biao Sun +9
Distributed synchronized GPU training is commonly used for deep learning. The resource constraint of using a fixed number of GPUs makes large-scale training jobs suffer from long q…
FamilySeer: Towards Optimized Tensor Codes by Exploiting Computation Subgraph Similarity
Shanjun Zhang, Mingzhen Li, Hailong Yang +3
Deploying various deep learning (DL) models efficiently has boosted the research on DL compilers. The difficulty of generating optimized tensor codes drives DL compiler to ask for…
Accelerating Sparse Approximate Matrix Multiplication on GPUs
Xiaoyan Liu, Yi Liu, Ming Dun +4
Although the matrix multiplication plays a vital role in computational linear algebra, there are few efficient solutions for matrix multiplication of the near-sparse matrices. The…
The Deep Learning Compiler: A Comprehensive Survey
Mingzhen Li, Yi Liu, Xiaoyan Liu +7
The difficulty of deploying various deep learning (DL) models on diverse DL hardware has boosted the research and development of DL compilers in the community. Several DL compilers…
Privacy for Rescue: A New Testimony Why Privacy is Vulnerable In Deep Models
Ruiyuan Gao, Ming Dun, Hailong Yang +2
The huge computation demand of deep learning models and limited computation resources on the edge devices calls for the cooperation between edge device and cloud service by splitti…
Intelligent-Unrolling: Exploiting Regular Patterns in Irregular Applications
Changxi Liu, Hailong Yang, Xu Liu +2
Modern optimizing compilers are able to exploit memory access or computation patterns to generate vectorization codes. However, such patterns in irregular applications are unknown…
Massively Scaling Seismic Processing on Sunway TaihuLight Supercomputer
Yongmin Hu, Hailong Yang, Zhongzhi Luan +1
Common Midpoint (CMP) and Common Reflection Surface (CRS) are widely used methods for improving the signal-to-noise ratio in the field of seismic processing. These methods are comp…
CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural Networks
Weicheng Li, Rui Wang, Zhongzhi Luan +4
Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications. Meanwhile, due to the complex model structures against stric…
swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor
Mingzhen Li, Changxi Liu, Jianjin Liao +8
The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to pro…
Scalable Hierarchical Scheduling for Malleable Parallel Jobs on Multiprocessor-based Systems
Yangjie Cao, Hongyang Sun, Depei Qian +1
The proliferation of multi-core and multiprocessor-based computer systems has led to explosive development of parallel applications and hence the need for efficient schedulers. In…