Error-controlled lossy compression optimized for high compression ratios of scientific datasets
Today's scientific simulations require a significant reduction of the data size because of
extremely large volumes of data they produce and the limitation of storage bandwidth and …
extremely large volumes of data they produce and the limitation of storage bandwidth and …
Optimizing error-bounded lossy compression for scientific data by dynamic spline interpolation
Today's scientific simulations are producing vast volumes of data that cannot be stored and
transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and …
transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and …
SZ3: A modular framework for composing prediction-based error-bounded lossy compressors
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 …
extremely large amounts of data they produce and the limited I/O bandwidth and storage …
Full-state quantum circuit simulation by using data compression
Quantum circuit simulations are critical for evaluating quantum algorithms and machines.
However, the number of state amplitudes required for full simulation increases exponentially …
However, the number of state amplitudes required for full simulation increases exponentially …
High-ratio lossy compression: Exploring the autoencoder to compress scientific data
Scientific simulations on high-performance computing (HPC) systems can generate large
amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the …
amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the …
Cusz: An efficient gpu-based error-bounded lossy compression framework for scientific data
Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC
applications because it not only significantly reduces storage overhead but also can retain …
applications because it not only significantly reduces storage overhead but also can retain …
Significantly improving lossy compression for HPC datasets with second-order prediction and parameter optimization
Today's extreme-scale high-performance computing (HPC) applications are producing
volumes of data too large to save or transfer because of limited storage space and I/O …
volumes of data too large to save or transfer because of limited storage space and I/O …
Dynamic quality metric oriented error bounded lossy compression for scientific datasets
With ever-increasing execution scale of the high performance computing (HPC)
applications, vast amount of data are being produced by scientific research every day. Error …
applications, vast amount of data are being produced by scientific research every day. Error …
DeepSZ: A novel framework to compress deep neural networks by using error-bounded lossy compression
Today's deep neural networks (DNNs) are becoming deeper and wider because of
increasing demand on the analysis quality and more and more complex applications to …
increasing demand on the analysis quality and more and more complex applications to …
Optimizing lossy compression rate-distortion from automatic online selection between SZ and ZFP
With ever-increasing volumes of scientific data produced by high-performance computing
applications, significantly reducing data size is critical because of limited capacity of storage …
applications, significantly reducing data size is critical because of limited capacity of storage …