11 papers
FSZ: Breaking the Prediction-Throughput Trade-off in GPU Lossy Compression
Jiajun Huang
Existing fast GPU error-bounded lossy compressors have achieved high throughput through pure-GPU single-kernel designs, but their compression ratios remain limited because they typ…
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…
FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention
Huangliang Dai, Shixun Wu, Jiajun Huang +4
Transformer models rely on High-Performance Computing (HPC) resources for inference, where soft errors are inevitable in large-scale systems, making the reliability of the model pa…
Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers
Chen Zhuang, Lingqi Zhang, Du Wu +8
Graph Convolutional Networks (GCNs), particularly for large-scale graphs, are crucial across numerous domains. However, training distributed full-batch GCNs on large-scale graphs s…
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…