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1. ARC: An Automated Approach to Resiliency for Lossy Compressed Data via Error Correcting Codes

   Fulp, A.; Poulos, A.; Underwood, R.; Calhoun, J. (June 2020, 30th International Symposium on High-Performance Parallel and Distributed Computing)

   Progress in high-performance computing (HPC) systems has led to complex applications that stress the I/O subsystem by creating vast amounts of data. Lossy compression reduces data size considerably, but a single error renders lossy compressed data unusable. This sensitivity stems from the high information content per bit in compressed data and is a critical issue as soft errors that cause bit-flips have become increasingly commonplace in HPC systems. While many works have improved lossy compressor performance, few have sought to address this critical weakness. 

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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. FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

   https://doi.org/10.1145/3588195.3592994  

   Zhang, Boyuan; Tian, Jiannan; Di, Sheng; Yu, Xiaodong; Feng, Yunhe; Liang, Xin; Tao, Dingwen; Cappello, Franck (June 2023, The 32nd ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2023))

   Today’s large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, data compression is becoming a critical technique to mitigate the storage burden and data-movement cost. However, existing lossy compressors for scientific data cannot achieve a high compression ratio and throughput simultaneously, hindering their adoption in many applications requiring fast compression, such as in-memory compression. To this end, in this work, we develop a fast and high-ratio error-bounded lossy compressor on GPUs for scientific data (called FZ-GPU). Specifically, we first design a new compression pipeline that consists of fully parallelized quantization, bitshuffle, and our newly designed fast encoding. Then, we propose a series of deep architectural optimizations for each kernel in the pipeline to take full advantage of CUDA architectures. We propose a warp-level optimization to avoid data conflicts for bit-wise operations in bitshuffle, maximize shared memory utilization, and eliminate unnecessary data movements by fusing different compression kernels. Finally, we evaluate FZ-GPU on two NVIDIA GPUs (i.e., A100 and RTX A4000) using six representative scientific datasets from SDRBench. Results on the A100 GPU show that FZ-GPU achieves an average speedup of 4.2× over cuSZ and an average speedup of 37.0× over a multi-threaded CPU implementation of our algorithm under the same error bound. FZ-GPU also achieves an average speedup of 2.3× and an average compression ratio improvement of 2.0× over cuZFP under the same data distortion. 

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4. Bit-Error Aware Quantization for DCT-based Lossy Compression

   Zhang, Jialing; Chen, Jiaxi; Moon, Aekyeung; Zhuo, Xiaoyan; Son, Seung Woo (September 2020, IEEE Conference on High Performance Extreme Computing)

   Scientific simulations run by high-performance computing (HPC) systems produce a large amount of data, which causes an extreme I/O bottleneck and a huge storage burden. Applying compression techniques can mitigate such overheads through reducing the data size. Unlike traditional lossless compressions, error-controlled lossy compressions, such as SZ, ZFP, and DCTZ, designed for scientists who demand not only high compression ratios but also a guarantee of certain degree of precision, is coming into prominence. While rate-distortion efficiency of recent lossy compressors, especially the DCT-based one, is promising due to its high-compression encoding, the overall coding architecture is still conservative, necessitating the quantization that strikes a balance between different encoding possibilities and varying rate-distortions. In this paper, we aim to improve the performance of DCT-based compressor, namely DCTZ, by optimizing the quantization model and encoding mechanism. Specifically, we propose a bit-efficient quantizer based on the DCTZ framework, develop a unique ordering mechanism based on the quantization table, and extend the encoding index. We evaluate the performance of our optimized DCTZ in terms of rate-distortion using real-world HPC datasets. Our experimental evaluations demonstrate that, on average, our proposed approach can improve the compression ratio of the original DCTZ by 1.38x. Moreover, combined with the extended encoding mechanism, the optimized DCTZ shows a competitive performance with state-of-the-art lossy compressors, SZ and ZFP. 

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5. 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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