Analyzing the Impact of Lossy Compressor Variability on Checkpointing Scientific Simulations

Triantafyllides, Pavlo; Reza, Tasmia; Calhoun, Jon C.

doi:10.1109/cluster.2019.8891052

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…SZ supports a variety of error bounding modese.g., absolute and relative error [3], PSNR [9]. Newer versions of SZ optimize the compression ratio and the compression/decompression bandwidth [10]; however, to significantly improve the compression/decompression bandwidth, SZ must take advantage of accelerators [11], [12]. The CPU version of SZ is limited to coarse grain parallelism for its prediction and quantization process.…”

Section: Introductionmentioning

confidence: 99%

SIMD Lossy Compression for Scientific Data

Griffin¹,

Tian²,

Di³

et al. 2022

Preprint

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Modern HPC applications produce increasingly large amounts of data, which limits the performance of current extreme-scale systems. Data reduction techniques, such as lossy compression, help to mitigate this issue by decreasing the size of data generated by these applications. SZ, a current state-of-theart lossy compressor, is able to achieve high compression ratios, but the prediction/quantization methods used introduce dependencies which prevent parallelizing this step of the compression. Recent work proposes a parallel dual prediction/quantization algorithm for GPUs which removes these dependencies. However, some HPC systems and applications do not use GPUs, and could still benefit from the fine-grained parallelism of this method. Using the dual-quantization technique, we implement and optimize a SIMD vectorized CPU version of SZ, and create a heuristic for selecting the optimal block size and vector length. We also investigate the effect of non-zero block padding values to decrease the number of unpredictable values along compression block borders. We measure performance of vecSZ against an O3 optimized CPU version of SZ using dual-quantization, pSZ, as well as SZ-1.4. We evaluate our vectorized version, vecSZ, on the Intel Skylake and AMD Rome architectures using real-world scientific datasets. We find that applying alternative padding reduces the number of outliers by 100% for some configurations. Our implementation also results in up to 32% improvement in rate-distortion and up to 15× speedup over SZ-1.4, achieving a prediction and quantization bandwidth in excess of 3.4 GB/s.

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Section: Introductionmentioning

confidence: 99%

SIMD Lossy Compression for Scientific Data

Griffin¹,

Tian²,

Di³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The trial-and-error is often done offline to ensure that the selected error bound is robust for multiple timesteps and does diminish the quality of the analysis. However, if the lossy compressed data is used to advance the simulation the simulation trial-and-error is possible [26], but recent works have explored the relation of compression error to numerical errors present in the simulation and provide strategies on error tolerance selection [21], [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data

Underwood

Calhoun

et al. 2020

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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With ever-increasing volumes of scientific floatingpoint data being produced by high-performance computing applications, significantly reducing scientific floating-point data size is critical, and error-controlled lossy compressors have been developed for years. None of the existing scientific floating-point lossy data compressors, however, support effective fixed-ratio lossy compression. Yet fixed-ratio lossy compression for scientific floating-point data not only compresses to the requested ratio but also respects a user-specified error bound with higher fidelity. In this paper, we present FRaZ: a generic fixed-ratio lossy compression framework respecting user-specified error constraints. The contribution is twofold. (1) We develop an efficient iterative approach to accurately determine the appropriate error settings for different lossy compressors based on target compression ratios. (2) We perform a thorough performance and accuracy evaluation for our proposed fixed-ratio compression framework with multiple state-of-the-art error-controlled lossy compressors, using several real-world scientific floating-point datasets from different domains. Experiments show that FRaZ effectively identifies the optimum error setting in the entire error setting space of any given lossy compressor. While fixed-ratio lossy compression is slower than fixed-error compression, it provides an important new lossy compression technique for users of very large scientific floating-point datasets.

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ZFP: A compressed array representation for numerical computations

Lindstrom,

Hittinger,

Diffenderfer

et al. 2024

The International Journal of High Performance Computing Applica

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HPC trends favor algorithms and implementations that reduce data motion relative to FLOPS. We investigate the use of lossy compressed data arrays in place of traditional IEEE floating point arrays to store the primary data of calculations. Simulation is fundamentally an exercise in controlled approximation, and error introduced by finite-precision arithmetic (or lossy compression) is just one of several sources of error that need to be managed to ensure sufficient accuracy in a computed result. We describe ZFP, a compressed numerical format designed for in-memory storage of multidimensional arrays, and summarize theoretical results that demonstrate that the error of repeated lossy compression can be bounded and controlled. Furthermore, we establish a relationship between grid resolution and compression-induced errors and show that, contrary to conventional floating point, ZFP reduces finite-difference errors with finer grids. We present example calculations that demonstrate data reduction by 4x or more with negligible impact on solution accuracy. Our results further demonstrate several orders-of-magnitude increase in accuracy using ZFP over IEEE floating point and Posits for the same storage budget.

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Analyzing the Impact of Lossy Compressor Variability on Checkpointing Scientific Simulations

Cited by 3 publications

References 24 publications

SIMD Lossy Compression for Scientific Data

SIMD Lossy Compression for Scientific Data

FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data

ZFP: A compressed array representation for numerical computations

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