1 citations · 2 across the 4 of their papers we have counts for
6 papers
CD-SGD: Distributed Stochastic Gradient Descent with Compression and Delay Compensation
Enda Yu, Dezun Dong, Yemao Xu +2
Communication overhead is the key challenge for distributed training. Gradient compression is a widely used approach to reduce communication traffic. When combining with parallel c…
ConfInLog: Leveraging Software Logs to Infer Configuration Constraints
Shulin Zhou, Xiaodong Liu, Shanshan Li +5
Misconfigurations have become the dominant causes of software failures in recent years, drawing tremendous attention for their increasing prevalence and severity. Configuration con…
deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search
Chen Zeng, Yue Yu, Shanshan Li +6
With the rapid increase in the amount of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing…
DepOwl: Detecting Dependency Bugs to Prevent Compatibility Failures
Zhouyang Jia, Shanshan Li, Tingting Yu +5
Applications depend on libraries to avoid reinventing the wheel. Libraries may have incompatible changes during evolving. As a result, applications will suffer from compatibility f…
A parallel structured divide-and-conquer algorithm for symmetric tridiagonal eigenvalue problems
Xia Liao, Shengguo Li, Yutong Lu +1
In this paper, a parallel structured divide-and-conquer (PSDC) eigensolver is proposed for symmetric tridiagonal matrices based on ScaLAPACK and a parallel structured matrix multip…
OD-SGD: One-step Delay Stochastic Gradient Descent for Distributed Training
Yemao Xu, Dezun Dong, Weixia Xu +1
The training of modern deep learning neural network calls for large amounts of computation, which is often provided by GPUs or other specific accelerators. To scale out to achieve…