Foresight: Analysis That Matters for Data Reduction

Grosset, Pascal; Biwer, C.; Pulido, Jesus; Mohan, Arvind; Biswas, Ayan; Patchett, John; Turton, Terece L.; Rogers, David; Livescu, Daniel; Ahrens, James

doi:10.1109/sc41405.2020.00087

Cited by 21 publications

(15 citation statements)

References 31 publications

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“…To control the accuracy in the reduced data error-bounded lossy compression algorithms such as SZ [3] and ZFP [4] provide order-ofmagnitude larger reductions than lossless compression while meeting a users specified level of data fidelity. However, setting the compressor's error bound to ensure data fidelity is an open question [6], [18], [19].…”

Section: A Lossy Data Reductionmentioning

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: A Lossy Data Reductionmentioning

confidence: 99%

SIMD Lossy Compression for Scientific Data

Griffin¹,

Tian²,

Di³

et al. 2022

Preprint

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“…We conduct our evaluation with Foresight [16], an open-source toolkit used to evaluate, analyze, and visualize lossy compressors for extreme-scale cosmological simulations . We modified the toolkit so that we can gather necessary parameters for our framework to deploy adaptive lossy compression configuration to various data partitions in Nyx cosmological simulation.…”

Section: Experimental Setup and Datasetmentioning

confidence: 99%

“…Apart from fine-grained adaptive compression, we must also be able to precisely control the compression error for domain-specific post-hoc analysis. Research has shown that general-purpose data distortion metrics, such as peak signal-to-noise ratio (PSNR), normalized root-mean-square error, mean relative error (MRE), and mean square error (MSE), on their own cannot satisfy the demand of quality for cosmological simulation post-hoc analysis [16,21]. For example, PSNR does not tell us how the mass of a halo would be impacted after compression.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling

Jin

Pulido

Grosset

et al. 2021

Proceedings of the 30th International Symposium on High-Performance Parallel and Distributed Computing

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Extreme-scale cosmological simulations have been widely used by today's researchers and scientists on leadership supercomputers. A new generation of error-bounded lossy compressors has been used in workflows to reduce storage requirements and minimize the impact of throughput limitations while saving large snapshots of high-fidelity data for post-hoc analysis. In this paper, we propose to adaptively provide compression configurations to compute partitions of cosmological simulations with newly designed postanalysis aware rate-quality modeling. The contribution is fourfold:(1) We propose a novel adaptive approach to select feasible error bounds for different partitions, showing the possibility and efficiency of adaptively configuring lossy compression for each partition individually. (2) We build models to estimate the overall loss of post-analysis result due to lossy compression and to estimate compression ratio, based on the property of each partition.(3) We develop an efficient optimization guideline to determine the best-fit configuration of error bounds combination in order to maximize the compression ratio under acceptable post-analysis quality loss. (4) Our approach introduces negligible overheads for feature extraction and error-bound optimization for each partition, enabling post-analysis-aware in situ lossy compression for cosmological simulations. Experiments show that our proposed models are highly accurate and reliable. Our fine-grained adaptive configuration approach improves the compression ratio of up to 73% on the tested datasets with the same post-analysis distortion with only 1% performance overhead.

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“…Compression of scientific data has been identified as a major data reduction technique to address this issue. More specifically, the new generation of error-bounded lossy compression techniques, such as SZ [5]- [7] and ZFP [8], have been widely used in the scientific community [4]- [13]. Compared to lossless compression that typically achieves only 2× compression ratio [14] on scientific data, error-bounded lossy compressors provide much higher compression ratios with controllable loss of accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…However, for HDF5 to take advantage of lossy compressors, it is essential for users to identify the optimal trade-off between the compression ratio and compressed data quality, which is fairly complex. Since there is no analytical model available to foresee/estimate the compression quality accurately, the configuration setting (such as error bound types and values) of error-bounded lossy compressors for scientific applications relies on empirical validations/studies based on domain scientists' trial-and-error experiments [11]- [13]. The trial-anderror method 1 suffers from two significant drawbacks, which leads to significant issues in practice.…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling

Jin¹,

Di²,

Tian³

et al. 2021

Preprint

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Error-bounded lossy compression is one of the most effective techniques for reducing scientific data sizes. However, the traditional trial-and-error approach used to configure lossy compressors for finding the optimal trade-off between reconstructed data quality and compression ratio is prohibitively expensive. To resolve this issue, we develop a general-purpose analytical ratio-quality model based on the prediction-based lossy compression framework, which can effectively foresee the reduced data quality and compression ratio, as well as the impact of lossy compressed data on post-hoc analysis quality. Our analytical model significantly improves the prediction-based lossy compression in three use-cases: (1) optimization of predictor by selecting the best-fit predictor; (2) memory compression with a target ratio; and (3) in-situ compression optimization by fine-grained tuning error-bounds for various data partitions. We evaluate our analytical model on 10 scientific datasets, demonstrating its high accuracy (93.47% accuracy on average) and low computational cost (up to 18.7× lower than the trialand-error approach) for estimating the compression ratio and the impact of lossy compression on post-hoc analysis quality. We also verify the high efficiency of our ratio-quality model using different applications across the three use-cases. In addition, our experiment demonstrates that our modeling based approach reduces the time to store the 3D RTM data with HDF5 by up to 3.4× with 128 CPU cores over the traditional solution.

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Foresight: Analysis That Matters for Data Reduction

Cited by 21 publications

References 31 publications

SIMD Lossy Compression for Scientific Data

SIMD Lossy Compression for Scientific Data

Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling

Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling

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