41 citations · 90 across the 7 of their papers we have counts for
7 papers
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
Zhuoqiang Guo, Denghui Lu, Yujin Yan +11
High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} m…
Tackling Variabilities in Autonomous Driving
Yuqiong Qi, Yang Hu, Haibin Wu +5
The state-of-the-art driving automation system demands extreme computational resources to meet rigorous accuracy and latency requirements. Though emerging driving automation comput…
RISC-NN: Use RISC, NOT CISC as Neural Network Hardware Infrastructure
Taoran Xiang, Lunkai Zhang, Shuqian An +9
Neural Networks (NN) have been proven to be powerful tools to analyze Big Data. However, traditional CPUs cannot achieve the desired performance and/or energy efficiency for NN app…
Understanding Big Data Analytic Workloads on Modern Processors
Zhen Jia, Lei Wang, Jianfeng Zhan +3
Big data analytics applications play a significant role in data centers, and hence it has become increasingly important to understand their behaviors in order to further improve th…
GRE: A Graph Runtime Engine for Large-Scale Distributed Graph-Parallel Applications
Jie Yan, Guangming Tan, Ninghui Sun
Large-scale distributed graph-parallel computing is challenging. On one hand, due to the irregular computation pattern and lack of locality, it is hard to express parallelism effic…
Compression and Sieve: Reducing Communication in Parallel Breadth First Search on Distributed Memory Systems
Huiwei Lv, Guangming Tan, Mingyu Chen +1
For parallel breadth first search (BFS) algorithm on large-scale distributed memory systems, communication often costs significantly more than arithmetic and limits the scalability…