1 citations · 1 across the 2 of their papers we have counts for
5 papers
Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores
Xiang Fu, Jixiang Ma, Xinpeng Zhang +3
Convolution is a principal computational bottleneck in deep neural networks, and its efficiency depends on tight integration between algorithms and GPU hardware. Existing GPU convo…
Scrutinizing Variables for Checkpoint Using Automatic Differentiation
Xin Huang, Weiping Zhang, Shiman Meng +4
Checkpoint/Restart (C/R) saves the running state of the programs periodically, which consumes considerable system resources. We observe that not every piece of data is involved in…
Distributed Order Recording Techniques for Efficient Record-and-Replay of Multi-threaded Programs
Xiang Fu, Shiman Meng, Weiping Zhang +6
After all these years and all these other shared memory programming frameworks, OpenMP is still the most popular one. However, its greater levels of non-deterministic execution mak…
AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis
Xiang Fu, Weiping Zhang, Xin Huang +4
Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, ther…
High Performance Im2win and Direct Convolutions using Three Tensor Layouts on SIMD Architectures
Xiang Fu, Xinpeng Zhang, Jixiang Ma +3
Convolution is the core component within deep neural networks and it is computationally intensive and time consuming. Tensor data layouts significantly impact convolution operation…