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1. Towards Guaranteeing Error Bound in DCT-based Lossy Compression

   https://doi.org/10.1109/BigData55660.2022.10020345  

   Chen, Jiaxi; Moon, Aekyeung; Son, Seung Woo (December 2022, IEEE International Conference on Big Data (Big Data))

   High-performance computing (HPC) systems that run scientific simulations of significance produce a large amount of data during runtime. Transferring or storing such big datasets causes a severe I/O bottleneck and a considerable storage burden. Applying compression techniques, particularly lossy compressors, can reduce the size of the data and mitigate such overheads. Unlike lossless compression algorithms, error-controlled lossy compressors could significantly reduce the data size while respecting the user-defined error bound. DCTZ is one of the transform-based lossy compressors with a highly efficient encoding and purpose-built error control mechanism that accomplishes high compression ratios with high data fidelity. However, since DCTZ quantizes the DCT coefficients in the frequency domain, it may only partially control the relative error bound defined by the user. In this paper, we aim to improve the compression quality of DCTZ. Specifically, we propose a preconditioning method based on level offsetting and scaling to control the magnitude of input of the DCTZ framework, thereby enforcing stricter error bounds. We evaluate the performance of our method in terms of compression ratio and rate distortion with real-world HPC datasets. Our experimental result shows that our method can achieve a higher compression ratio than other state-of-the-art lossy compressors with a tighter error bound while precisely guaranteeing the user-defined error bound. 

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2. Analyzing the Impact of Lossy Compressor Variability on Checkpointing Scientific Simulations

   https://doi.org/10.1109/CLUSTER.2019.8891052  

   Triantafyllides, Pavlo; Reza, Tasmia; Calhoun, Jon C. (September 2019, 2019 IEEE International Conference on Cluster Computing (CLUSTER))

   Lossy compression algorithms are effective tools to reduce the size of high-performance computing data sets. As established lossy compressors such as SZ and ZFP evolve, they seek to improve the compression/decompression bandwidth and the compression ratio. Algorithm improvements may alter the spatial distribution of errors in the compressed data even when using the same error bound and error bound type. If HPC applications are to compute on lossy compressed data, application users require an understanding of how the performance and spatial distribution of error changes. We explore how spatial distributions of error, compression/decompression bandwidth, and compression ratio change for HPC data sets from the applications PlasComCM and Nek5000 between various versions of SZ and ZFP. In addition, we explore how the spatial distribution of error impacts application correctness when restarting from lossy compressed checkpoints. We verify that known approaches to selecting error tolerances for lossy compressed checkpointing are robust to compressor selection and in the face of changes in the distribution of error. 

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3. cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data

   Jiannan Tian, Sheng Di (October 2020, Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques)

   null (Ed.)

   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 high fidelity for postanalysis. Because supercomputers and HPC applications are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existing state-of-the-art GPU-based lossy compressors suffer from either low compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version, cuSZ, for one of the best error-bounded lossy compressors-SZ. To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data. Our contributions are fourfold. (1) We propose a dual-quantization scheme to entirely remove the data dependency in the prediction step of SZ such that this step can be performed very efficiently on GPUs. (2) We develop an efficient customized Huffman coding for the SZ compressor on GPUs. (3) We implement cuSZ using CUDA and optimize its performance by improving the utilization of GPU memory bandwidth. (4) We evaluate our cuSZ on five real-world HPC application datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and GPUs. Experiments show that our cuSZ improves SZ's compression throughput by up to 370.1x and 13.1x, respectively, over the production version running on single and multiple CPU cores, respectively, while getting the same quality of 

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4. cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data

   https://doi.org/10.1145/3410463.3414624  

   Tian, Jiannan; Di, Sheng; Zhao, Kai; Rivera, Cody; Hickman Fulp, Megan; Underwood, Robert; Jin, Sian; Liang, Xin; Calhoun, Jon; Tao, Dingwen; et al (October 2020, The 29th International Conference on Parallel Architectures and Compilation Techniques (PACT 2020))

   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 high fidelity for postanalysis. Because supercomputers and HPC applications are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existing state-of-the-art GPU-based lossy compressors suffer from either low compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version, cuSZ, for one of the best error-bounded lossy compressors-SZ. To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data. Our contributions are fourfold. (1) We propose a dual-quantization scheme to entirely remove the data dependency in the prediction step of SZ such that this step can be performed very efficiently on GPUs. (2) We develop an efficient customized Huffman coding for the SZ compressor on GPUs. (3) We implement cuSZ using CUDA and optimize its performance by improving the utilization of GPU memory bandwidth. (4) We evaluate our cuSZ on five real-world HPC application datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and GPUs. Experiments show that our cuSZ improves SZ's compression throughput by up to 370.1x and 13.1x, respectively, over the production version running on single and multiple CPU cores, respectively, while getting the same quality of reconstructed data. It also improves the compression ratio by up to 3.48x on the tested data compared with another state-of-the-art GPU supported lossy compressor. 

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5. Efficient Encoding and Reconstruction of HPC Datasets for Checkpoint/Restart

   https://doi.org/10.1109/MSST.2019.00-14  

   Zhang, Jialing; Zhuo, Xiaoyan; Moon, Aekyeung; Liu, Hang; Son, Seung Woo (May 2019, 35th Symposium on Mass Storage Systems and Technologies (MSST))

   As the amount of data produced by HPC applications reaches the exabyte range, compression techniques are often adopted to reduce the checkpoint time and volume. Since lossless techniques are limited in their ability to achieve appreciable data reduction, lossy compression becomes a preferable option. In this work, a lossy compression technique with highly efficient encoding, purpose-built error control, and high compression ratios is proposed. Specifically, we apply a discrete cosine transform with a novel block decomposition strategy directly to double-precision floating point datasets instead of prevailing prediction-based techniques. Further, we design an adaptive quantization with two specific task-oriented quantizers: guaranteed error bounds and higher compression ratios. Using real-world HPC datasets, our approach achieves 3x-38x compression ratios while guaranteeing specified error bounds, showing comparable performance with state-of-the-art lossy compression methods, SZ and ZFP. Moreover, our method provides viable reconstructed data for various checkpoint/restart scenarios in the FLASH application, thus is considered to be a promising approach for lossy data compression in HPC I/O software stacks. 

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