98 citations · 289 across the 32 of their papers we have counts for
33 papers
TOPIQ: Statistical Error Propagation for Quantity-of-Interest Prediction under Lossy Compression
Youyuan Liu, Bo Jiang, Taolue Yang +3
Lossy compression is essential for managing massive scientific data, but per-element error bounds do not translate into bounds on downstream quantities of interest (QoIs) such as r…
Error-bounded Point Cloud Compression Using Truncated Octahedron Quantization
Youyuan Liu, Longtao Zhang, Ruoyu Li +6
With the rapid advancement of large-scale scientific simulations, the massive volume of point cloud data generated has increasingly become a critical bottleneck for scientific stor…
3D Gaussian Splatting for Scientific Particle Data Compression and Rendering
Bo Jiang, Youyuan Liu, Taolue Yang +2
Large-scale particle simulations produce hundreds of millions of particles, straining storage, transfer, and interactive visualization. Existing lossy compressors such as SZ3 opera…
KVSculpt: KV Cache Compression as Distillation
Bo Jiang, Sian Jin
KV cache compression is critical for efficient long-context LLM inference. Approaches that reduce the per-pair footprint -- quantization and low-rank decomposition -- are orthogona…
Event-VStream: Event-Driven Real-Time Understanding for Long Video Streams
Zhenghui Guo, Yuanbin Man, Junyuan Sheng +8
Real-time understanding of long video streams remains challenging for multimodal large language models (VLMs) due to redundant frame processing and rapid forgetting of past context…
PackKV: Reducing KV Cache Memory Footprint through LLM-Aware Lossy Compression
Bo Jiang, Taolue Yang, Youyuan Liu +3
Transformer-based large language models (LLMs) have demonstrated remarkable potential across a wide range of practical applications. However, long-context inference remains a signi…
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…
KVComp: A High-Performance, LLM-Aware, Lossy Compression Framework for KV Cache
Bo Jiang, Taolue Yang, Youyuan Liu +3
Transformer-based large language models (LLMs) demonstrate impressive potential in various practical applications. However, long context inference poses a significant challenge due…
FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression
Wenqi Jia, Zhewen Hu, Baixi Sun +9
Scientific simulation leveraging high-performance computing (HPC) systems is crucial for modeling complex systems and phenomena in fields such as astrophysics, climate science, and…
Accurate Performance Modeling And Uncertainty Analysis of Lossy Compression in Scientific Applications
Youyuan Liu, Taolue Yang, Sian Jin
Scientific applications typically generate large volumes of floating-point data, making lossy compression one of the most effective methods for data reduction, thereby lowering sto…
Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications
Youyuan Liu, Wenqi Jia, Taolue Yang +2
Lossy compression is one of the most effective methods for reducing the size of scientific data containing multiple data fields. It reduces information density through prediction o…
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…
GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data
Wenqi Jia, Sian Jin, Jinzhen Wang +3
The rapid expansion of computational capabilities and the ever-growing scale of modern HPC systems present formidable challenges in managing exascale scientific data. Faced with su…
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…
High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation
Jinyang Liu, Sheng Di, Kai Zhao +7
Error-bounded lossy compression has been identified as a promising solution for significantly reducing scientific data volumes upon users' requirements on data distortion. For the…
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
Jinyang Liu, Sheng Di, Sian Jin +4
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usa…
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…
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…
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…
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…
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
Sian Jin, Chengming Zhang, Xintong Jiang +5
Training wide and deep neural networks (DNNs) require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during…
Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling
Sian Jin, Sheng Di, Jiannan Tian +3
Error-bounded lossy compression is one of the most effective techniques for scientific data reduction. However, the traditional trial-and-error approach used to configure lossy com…
CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Designed Adaptive Lossy Compression
Chengming Zhang, Sian Jin, Tong Geng +3
As HPC systems continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounde…