6 citations · 16 across the 26 of their papers we have counts for
29 papers
PLEDGE: Progressive Lossy Error-Bounded Compression of 3D Gaussian Splatting
Mingze Xia, Yuxiao Li, Baixi Sun +6
Existing 3D Gaussian Splatting (3DGS) codecs report reconstruction quality after compression, and give users little control over the distortion of individual primitives. We present…
LOCAA: An Agentic System for Automated Lossy Compressor Tuning
Khondoker Mirazul Mumenin, Dong Dai, Sheng Di +2
Large-scale scientific simulations generate substantial data volumes, making lossy compression essential for reducing storage and data movement costs. However, users configure comp…
FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression
Guoxi Liu, Yuxiao Li, Congrong Ren +6
Modern scientific simulations generate massive volumes of data, making lossy compression essential for efficient storage and transmission. However, preserving critical quantities o…
ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload
Ziyue Liu, Zhengyang Wang, Ruijie Zhang +7
Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient training systems. Yet existing framewor…
Preserving Clusters in Error-Bounded Lossy Compression of Scientific Particle Data
Congrong Ren, Sheng Di, Katrin Heitmann +2
Scientific particle simulations in cosmology, molecular dynamics, and fluid dynamics produce large-scale datasets whose storage, movement, and analysis increasingly rely on lossy c…
Enabling Homomorphic Analytical Operations on Compressed Scientific Data with Multi-stage Decompression
Xuan Wu, Sheng Di, Tripti Agarwal +3
Error-controlled lossy compressors have been widely used in scientific applications to reduce the unprecedented size of scientific data while keeping data distortion within a user-…
Rate-Distortion Bounds for Heterogeneous Random Fields on Finite Lattices
Sujata Sinha, Vishwas Rao, Robert Underwood +4
Since Shannon's foundational work, rate-distortion theory has defined the fundamental limits of lossy compression. Classical results, derived for memoryless and stationary ergodic…
SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs
Jin Lee, Zhonghao Chen, Xuhang He +6
In large-scale LLM pre-training systems with 100k+ GPUs, failures become the norm rather than the exception, and restart costs can dominate wall-clock training time. However, exist…
Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation
Pu Jiao, Sheng Di, Jiannan Tian +5
Error-bounded lossy compression has been regarded as a promising way to address the ever-increasing amount of scientific data in today's high-performance computing systems. Pre-qua…
TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data
Tripti Agarwal, Sheng Di, Xin Liang +5
Error-bounded lossy compression is essential for managing the massive data volumes produced by large-scale HPC simulations. While state-of-the-art compressors such as SZ and ZFP pr…
pMSz: A Distributed Parallel Algorithm for Correcting Extrema and Morse Smale Segmentations in Lossy Compression
Yuxiao Li, Mingze Xia, Xin Liang +7
Lossy compression, widely used by scientists to reduce data from simulations, experiments, and observations, can distort features of interest even under bounded error. Such distort…
FFCz: Fast Fourier Correction for Spectrum-Preserving Lossy Compression of Scientific Data
Congrong Ren, Robert Underwood, Sheng Di +5
This paper introduces a novel technique to preserve spectral features in lossy compression based on a novel fast Fourier correction algorithm\added{ for regular-grid data}. Preserv…
DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction
Khondoker Mirazul Mumenin, Robert Underwood, Dong Dai +4
Error-bounded lossy compression techniques have become vital for scientific data management and analytics, given the ever-increasing volume of data generated by modern scientific s…
Boosting Scientific Error-Bounded Lossy Compression through Optimized Synergistic Lossy-Lossless Orchestration
Shixun Wu, Jinwen Pan, Jinyang Liu +8
As high-performance computing architectures evolve, more scientific computing workflows are being deployed on advanced computing platforms such as GPUs. These workflows can produce…
GPZ: GPU-Accelerated Lossy Compressor for Particle Data
Ruoyu Li, Yafan Huang, Longtao Zhang +10
Particle-based simulations and point-cloud applications generate massive, irregular datasets that challenge storage, I/O, and real-time analytics. Traditional compression technique…
Systematic Evaluation of Optimization Techniques for Long-Context Language Models
Ammar Ahmed, Sheng Di, Franck Cappello +3
Large language models (LLMs) excel across diverse natural language processing tasks but face resource demands and limited context windows. Although techniques like pruning, quantiz…
IPComp: Interpolation Based Progressive Lossy Compression for Scientific Applications
Zhuoxun Yang, Sheng Di, Longtao Zhang +6
Compression is a crucial solution for data reduction in modern scientific applications due to the exponential growth of data from simulations, experiments, and observations. Compre…
Lossy Compression of Scientific Data: Applications Constrains and Requirements
Franck Cappello, Allison Baker, Ebru Bozda +22
Increasing data volumes from scientific simulations and instruments (supercomputers, accelerators, telescopes) often exceed network, storage, and analysis capabilities. The scienti…
ZCCL: Significantly Improving Collective Communication With Error-Bounded Lossy Compression
Jiajun Huang, Sheng Di, Xiaodong Yu +12
With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communication turns out to be a critic…
TurboFFT: Co-Designed High-Performance and Fault-Tolerant Fast Fourier Transform on GPUs
Shixun Wu, Yujia Zhai, Jinyang Liu +6
GPU-based fast Fourier transform (FFT) is extremely important for scientific computing and signal processing. However, we find the inefficiency of existing FFT libraries and the ab…
QPET: A Versatile and Portable Quantity-of-Interest-Preservation Framework for Error-Bounded Lossy Compression
Jinyang Liu, Pu Jiao, Kai Zhao +3
Error-bounded lossy compression has been widely adopted in many scientific domains because it can address the challenges in storing, transferring, and analyzing unprecedented amoun…
DGRO: Diameter-Guided Ring Optimization for Integrated Research Infrastructure Membership
Shixun Wu, Krishnan Raghavan, Sheng Di +2
Logical ring is a core component in membership protocol. However, the logic ring fails to consider the underlying physical latency, resulting in a high diameter. To address this is…
To Compress or Not To Compress: Energy Trade-Offs and Benefits of Lossy Compressed I/O
Grant Wilkins, Sheng Di, Jon C. Calhoun +2
Modern scientific simulations generate massive volumes of data, creating significant challenges for I/O and storage systems. Error-bounded lossy compression (EBLC) offers a solutio…
FRSZ2 for In-Register Block Compression Inside GMRES on GPUs
Thomas Grützmacher, Robert Underwood, Sheng Di +2
The performance of the GMRES iterative solver on GPUs is limited by the GPU main memory bandwidth. Compressed Basis GMRES outperforms GMRES by storing the Krylov basis in low preci…