10 citations · 11 across the 10 of their papers we have counts for
10 papers
Im2win: An Efficient Convolution Paradigm on GPU
Shuai Lu, Jun Chu, Luanzheng Guo +1
Convolution is the most time-consuming operation in deep neural network operations, so its performance is critical to the overall performance of the neural network. The commonly us…
MOARD: Modeling Application Resilience to Transient Faults on Data Objects
Luanzheng Guo, Dong Li
Understanding application resilience (or error tolerance) in the presence of hardware transient faults on data objects is critical to ensure computing integrity and enable efficien…
Reinit++: Evaluating the Performance of Global-Restart Recovery Methods For MPI Fault Tolerance
Giorgis Georgakoudis, Luanzheng Guo, Ignacio Laguna
Scaling supercomputers comes with an increase in failure rates due to the increasing number of hardware components. In standard practice, applications are made resilient through ch…
MATCH: An MPI Fault Tolerance Benchmark Suite
Luanzheng Guo, Giorgis Georgakoudis, Konstantinos Parasyris +2
MPI has been ubiquitously deployed in flagship HPC systems aiming to accelerate distributed scientific applications running on tens of hundreds of processes and compute nodes. Main…
A High-Performance Sparse Tensor Algebra Compiler in Multi-Level IR
Ruiqin Tian, Luanzheng Guo, Jiajia Li +2
Tensor algebra is widely used in many applications, such as scientific computing, machine learning, and data analytics. The tensors represented real-world data are usually large an…
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…
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…
Indoor Frame Recovery from Refined Line Segments
Luanzheng Guo, Jun Chu
An important yet challenging problem in understanding indoor scene is recovering indoor frame structure from a monocular image. It is more difficult when occlusions and illuminatio…
Application-Level Resilience Modeling for HPC Fault Tolerance
Luanzheng Guo, Hanlin He, Dong Li
Understanding the application resilience in the presence of faults is critical to address the HPC resilience challenge. Currently, we largely rely on random fault injection (RFI) t…