13 citations · 25 across the 10 of their papers we have counts for
10 papers
STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data
Daoce Wang, Pascal Grosset, Jesus Pulido +9
Error-bounded lossy compression is one of the most efficient solutions to reduce the volume of scientific data. For lossy compression, progressive decompression and random-access d…
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
Jinda Jia, Cong Xie, Hanlin Lu +8
Recent years have witnessed a clear trend towards language models with an ever-increasing number of parameters, as well as the growing training overhead and memory usage. Distribut…
NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression
Wenqi Jia, Zhewen Hu, Youyuan Liu +10
Large-scale scientific simulations generate massive datasets, posing challenges for storage and I/O. Traditional lossy compression struggles to advance more in balancing compressio…
A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization
Daoce Wang, Pascal Grosset, Jesus Pulido +8
Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is…
A Survey on Error-Bounded Lossy Compression for Scientific Datasets
Sheng Di, Jinyang Liu, Kai Zhao +23
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error…
Analyzing Impact of Data Reduction Techniques on Visualization for AMR Applications Using AMReX Framework
Daoce Wang, Jesus Pulido, Pascal Grosset +3
Today's scientific simulations generate exceptionally large volumes of data, challenging the capacities of available I/O bandwidth and storage space. This necessitates a substantia…
AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications
Daoce Wang, Jesus Pulido, Pascal Grosset +11
As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences expo…
TAC+: Optimizing Error-Bounded Lossy Compression for 3D AMR Simulations
Daoce Wang, Jesus Pulido, Pascal Grosset +5
Today's scientific simulations require significant data volume reduction because of the enormous amounts of data produced and the limited I/O bandwidth and storage space. Error-bou…
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…
Characterizing Impacts of Storage Faults on HPC Applications: A Methodology and Insights
Bo Fang, Daoce Wang, Sian Jin +6
In recent years, the increasing complexity in scientific simulations and emerging demands for training heavy artificial intelligence models require massive and fast data accesses,…