41 citations · 60 across the 8 of their papers we have counts for
8 papers
AlphaSparse: Generating High Performance SpMV Codes Directly from Sparse Matrices
Zhen Du, Jiajia Li, Yinshan Wang +3
Sparse Matrix-Vector multiplication (SpMV) is an essential computational kernel in many application scenarios. Tens of sparse matrix formats and implementations have been proposed…
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…
I/O Lower Bounds for Auto-tuning of Convolutions in CNNs
Xiaoyang Zhang, Junmin Xiao, Guangming Tan
Convolution is the most time-consuming part in the computation of convolutional neural networks (CNNs), which have achieved great successes in numerous applications. Due to the com…
Quadboost: A Scalable Concurrent Quadtree
Keren Zhou, Guangming Tan, Wei Zhou
Building concurrent spatial trees is more complicated than binary search trees since a space hierarchy should be preserved during modifications. We present a non-blocking quadtree-…
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…
SMAT: An Input Adaptive Sparse Matrix-Vector Multiplication Auto-Tuner
Jiajia Li, Xiuxia Zhang, Guangming Tan +1
Sparse matrix vector multiplication (SpMV) is an important kernel in scientific and engineering applications. The previous optimizations are sparse matrix format specific and expos…