296 citations · 963 across the 52 of their papers we have counts for
9 papers · 1 filter
SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates
Baixi Sun, Xiaodong Yu, Chengming Zhang +7
CNN-based surrogates have become prevalent in scientific applications to replace conventional time-consuming physical approaches. Although these surrogates can yield satisfactory r…
TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition
Lizhi Xiang, Miao Yin, Chengming Zhang +4
Tucker decomposition is one of the SOTA CNN model compression techniques. However, unlike the FLOPs reduction, we observe very limited inference time reduction with Tucker-compress…
HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures
Xinyu Chen, Marco Minutoli, Jiannan Tian +3
Influence maximization aims to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. Although computing optimal seed se…
Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5
Sian Jin, Dingwen Tao, Houjun Tang +4
Lossy compression is one of the most efficient solutions to reduce storage overhead and improve I/O performance for HPC applications. However, existing parallel I/O libraries canno…
H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture
Chengming Zhang, Tong Geng, Anqi Guo +4
Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having uns…
TAC: Optimizing Error-Bounded Lossy Compression for Three-Dimensional Adaptive Mesh Refinement Simulations
Daoce Wang, Jesus Pulido, Pascal Grosset +4
Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage spac…
SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets
Xiaodong Yu, Sheng Di, Kai Zhao +4
Today's scientific high performance computing (HPC) applications or advanced instruments are producing vast volumes of data across a wide range of domains, which introduces a serio…
SIMD Lossy Compression for Scientific Data
Griffin Dube, Jiannan Tian, Sheng Di +3
Modern HPC applications produce increasingly large amounts of data, which limits the performance of current extreme-scale systems. Data reduction techniques, such as lossy compress…
Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs
Cody Rivera, Sheng Di, Jiannan Tian +3
More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to…