27 citations · 109 across the 24 of their papers we have counts for
5 papers · 1 filter
A Preliminary Study of Neural Network-based Approximation for HPC Applications
Wenqian Dong, Anzheng Guolu, Dong Li
Machine learning, as a tool to learn and model complicated (non)linear relationships between input and output data sets, has shown preliminary success in some HPC problems. Using m…
PARIS: Predicting Application Resilience Using Machine Learning
Luanzheng Guo, Dong Li, Ignacio Laguna
Extreme-scale scientific applications can be more vulnerable to soft errors (transient faults) as high-performance computing systems increase in scale. The common practice to evalu…
Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training
Jiawen Liu, Dong Li, Gokcen Kestor +1
Training neural network often uses a machine learning framework such as TensorFlow and Caffe2. These frameworks employ a dataflow model where the NN training is modeled as a direct…
FlipTracker: Understanding Natural Error Resilience in HPC Applications
Luanzheng Guo, Dong Li, Ignacio Laguna +1
As high-performance computing systems scale in size and computational power, the danger of silent errors, i.e., errors that can bypass hardware detection mechanisms and impact appl…
Characterization and Comparison of Application Resilience for Serial and Parallel Executions
Kai Wu, Qiang Guan, Nathan DeBardeleben +1
Soft error of exascale application is a challenge problem in modern HPC. In order to quantify an application's resilience and vulnerability, the application-level fault injection m…