48 citations · 52 across the 4 of their papers we have counts for
5 papers
CuPBoP: CUDA for Parallelized and Broad-range Processors
Ruobing Han, Jun Chen, Bhanu Garg +3
CUDA is one of the most popular choices for GPU programming, but it can only be executed on NVIDIA GPUs. Executing CUDA on non-NVIDIA devices not only benefits the hardware communi…
COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs
Ruobing Han, Jaewon Lee, Jaewoong Sim +1
As CUDA programs become the de facto program among data parallel applications such as high-performance computing or machine learning applications, running CUDA on other platforms h…
Supporting CUDA for an extended RISC-V GPU architecture
Ruobing Han, Blaise Tine, Jaewon Lee +2
With the rapid development of scientific computation, more and more researchers and developers are committed to implementing various workloads/operations on different devices. Amon…
Auto-Precision Scaling for Distributed Deep Learning
Ruobing Han, James Demmel, Yang You
It has been reported that the communication cost for synchronizing gradients can be a bottleneck, which limits the scalability of distributed deep learning. Using low-precision gra…
Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes
Peng Sun, Wansen Feng, Ruobing Han +2
It is important to scale out deep neural network (DNN) training for reducing model training time. The high communication overhead is one of the major performance bottlenecks for di…