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1. Local Temporal Compression for (Globally) Evolving Spatial Surfaces

   https://doi.org/10.1007/978-3-030-37188-3_19  

   Teng, Xu; Giri, Prabin; Dwyer, Matthew; Trajcevski, Goce (October 2019, Big Data Analytics - 7th International Conference, {BDA} 2019, Ahmedabad, India, December 17-20, 2019, Proceedings)

   null (Ed.)

   The advances in the Internet of Things (IoT) paradigm have enabled generation of large volumes of data from multiple domains, capturing the evolution of various physical and social phenomena of interest. One of the consequences of such enormous data generation is that it needs to be stored, processed and queried – along with having the answers presented in an intuitive manner. A number of techniques have been proposed to alleviate the impact of the sheer volume of the data on the storage and processing overheads, along with bandwidth consumption – and, among them, the most dominant is compression. In this paper, we consider a setting in which multiple geographically dispersed data sources are generating data streams – however, the values from the discrete locations are used to construct a representation of continuous (time-evolving) surface. We have used different compression techniques to reduce the size of the raw measurements in each location, and we analyzed the impact of the compression on the quality of approximating the evolution of the shapes corresponding to a particular phenomenon. Specifically, we use the data from discrete locations to construct a TIN (triangulated irregular networks), which evolves over time as the measurements in each locations change. To analyze the global impact of the different compression techniques that are applied locally, we used different surface distance functions between raw-data TINs and compressed data TINs. We provide detailed discussions based on our experimental observations regarding the corresponding (compression method, distance function) pairs. 

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2. CompressedLUT: An Open Source Tool for Lossless Compression of Lookup Tables for Function Evaluation and Beyond

   https://doi.org/10.1145/3626202.3637575  

   Khataei, Alireza; Bazargan, Kia (April 2024, ACM)

   Lookup tables are widely used in hardware to store arrays of constant values. For instance, complex mathematical functions in hardware are typically implemented through table-based methods such as plain tabulation, piecewise linear approximation, and bipartite or multipartite table methods, which primarily rely on lookup tables to evaluate the functions. Storing extensive tables of constant values, however, can lead to excessive hardware costs in resource-constrained edge devices such as FPGAs. In this paper, we propose a method, called CompressedLUT, as a lossless compression scheme to compress arrays of arbitrary data, implemented as lookup tables. Our method exploits decomposition, self-similarities, higher-bit compression, and multilevel compression techniques to maximize table size savings with no accuracy loss. CompressedLUT uses addition and arithmetic right shift beside several small lookup tables to retrieve original data during the decoding phase. Using such cost-effective elements helps our method use low area and deliver high throughput. For evaluation purposes, we compressed a number of different lookup tables, either obtained by direct tabulation of 12-bit elementary functions or generated by other table-based methods for approximating functions at higher resolutions, such as multipartite table method at 24-bit, piecewise polynomial approximation method at 36-bit, and hls4ml library at 18-bit resolutions. We implemented the compressed tables on FPGAs using HLS to show the efficiency of our method in terms of hardware costs compared to previous works. Our method demonstrated 60% table size compression and achieved 2.33 times higher throughput per slice than conventional implementations on average. In comparison, previous TwoTable and LDTC works compressed the lookup tables on average by 33% and 37%, which resulted in 1.63 and 1.29 times higher throughput than the conventional implementations, respectively. CompressedLUT is available as an open source tool. 

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3. STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization

   https://doi.org/10.1609/aaai.v35i6.16615  

   Nikos Kargas, Cheng Qian (January 2021, The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21))

   Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. We construct a 3-way spatio-temporal tensor (location, attribute, time) of case counts and propose a nonnegative tensor factorization with latent epidemiological model regularization named STELAR. Unlike standard tensor factorization methods which cannot predict slabs ahead, STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete time difference equations of a widely adopted epidemiological model. We use latent instead of location/attribute-level epidemiological dynamics to capture common epidemic profile sub-types and improve collaborative learning and prediction. We conduct experiments using both county- and state level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic. Finally, we evaluate the predictive ability of our method and show superior performance compared to the baselines, achieving up to 21% lower root mean square error and 25% lower mean absolute error for county-level prediction. 

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4. 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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5. Characterization of Transform-Based Lossy Compression for HPC Datasets

   https://doi.org/10.1109/DRBSD56682.2022.00013  

   Moon, Aekyeung; Chen, Jiaxi; Son, Seung Woo; Kim, Minjun (November 2022, IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD))

   As the scale and complexity of high-performance computing (HPC) systems keep growing, data compression techniques are often adopted to reduce the data volume and processing time. While lossy compression becomes preferable to a lossless one because of the potential benefit of generating a high compression ratio, it would lose its worth the effort without finding an optimal balance between volume reduction and information loss. Among many lossy compression techniques, transform-based lossy algorithms utilize spatial redundancy better. However, the transform-based lossy compressor has received relatively less attention because there is a lack of understanding of its compression performance on scientific data sets. The insight of this paper is that, in transform-based lossy compressors, quantifying dominant coefficients at the block level reveals the right balance, potentially impacting overall compression ratios. Motivated by this, we characterize three transformation-based lossy compression mechanisms with different information compaction methods using the statistical features that capture data characteristics. And then, we build several prediction models using the statistical features and the characteristics of dominant coefficients and evaluate the effectiveness of each model using six HPC datasets from three production-level simulations at scale. Our results demonstrate that the random forest classifier captures the behavior of dominant coefficients precisely, achieving nearly 99% of prediction accuracy. 

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