Scientific applications are generating unprecedented volumes of data that overwhelm storage and transmission systems, posing significant challenges for the design of data management tools and scientific databases. Lossy compression has emerged as a promising strategy to address this problem, but most existing compressors fail to preserve the topology of scientific data, leading to inaccuracies in downstream analyses and potentially erroneous scientific conclusions. In this work, we present a methodology for fully preserving the topology, specifically, Morse-Smale complexes (MSCs), in lossy-compressed 2D and 3D scalar field data from scientific simulations. We generalize the edit-based strategy introduced in MSz [1] (a previous method that preserves only segmentations and cannot preserve saddles or separatrices) by extending the framework to the full MSCs, including all critical points and separatrices. Our approach corrects the MSCs in the decompressed output of any error-bounded lossy compressor (e.g., SZ3 or ZFP), referred to as the base compressor, using an iterative editing strategy that preserves all critical points and their connectivity via separatrices. During compression, we generate a sequence of quantized edits that are applied to the decompressed output, ensuring accurate preservation of topological features while maintaining the error within prescribed bounds. The strategy iteratively fixes critical points and separatrices in alternating steps until convergence is achieved in a finite number of iterations. To meet diverse application needs, our method offers flexible options (e.g., whether to preserve the geometry of separatrices) that balance compression efficiency with feature preservation. To reduce computation time, we leverage GPU parallelism to accelerate each component of the workflow. Experiments on multiple datasets demonstrate that our method achieves 100% preservation of Morse-Smale complexes.

Data compression is a powerful solution for addressing big data challenges in database and data management. In scientific data compression for vector fields, preserving topological information is essential for accurate analysis and visualization. The topological skeleton, a fundamental component of vector field topology, consists of critical points and their connectivity (i.e., separatrices). While previous work has focused on preserving critical points in error-controlled lossy compression, little attention has been given to preserving separatrices, which are equally important. In this work, we introduce TspSZ, an efficient error-bounded lossy compression framework designed to preserve both critical points and separatrices. Our key contributions are threefold. First, we propose TspSZ, a topological-skeleton-preserving lossy compression framework that integrates two algorithms, enabling existing critical-point-preserving compressors to also retain separatrices, significantly enhancing their topology preservation capabilities. Second, we optimize TspSZ for efficiency through tailored improvements and parallelization. Specifically, we introduce a new error control mechanism to achieve high compression ratios and implement a shared-memory parallelization strategy to boost compression throughput. Third, we evaluate TspSZ against state-of-the-art lossy and lossless compressors using four real-world scientific datasets. Experimental results show that TspSZ achieves compression ratios of up to 7.7× while effectively preserving the topological skeleton, ensuring efficient storage and transmission of scientific data without compromising topological integrity.

Large-scale scientific simulations produce unprecedented amounts of data using high-performance computing systems, leading to severe problems in data storage, I/O, and communication. To address the data movement challenge, errorcontrolled lossy compression has been proposed to significantly reduce the data size while retaining the data quality. Recently, interpolation-based compressors, including MGARD, SZ3, QoZ, and HPEZ, have stood out due to their efficiency in obtaining relatively high compression ratios with decent compression and decompression throughput. Nevertheless, these methods focus on data decorrelation in the compression pipeline yet overlook the correlation of the quantization indices generated after decorrelation. In this paper, we develop a generic framework that can use the correlation of quantization indices to significantly improve the compression ratios for state-of-the-art interpolation-based error-bounded lossy compressors. Our contributions are threefold: (1) We carefully characterized the quantization index array produced by the interpolation-based compressors and identified the unused correlation; (2) We designed a generic quantization index prediction method to exploit such correlation, which leads to improved compression ratio with only minor degradation in throughput; (3) We integrate our method into 4 state-of-theart interpolation-based compressors and evaluate them using 5 real-world datasets. Experimental results demonstrate that the proposed method improves the compression ratios of the base compressors by up to 95% while keeping the same quality. It also leads to 16% improvement in end-to-end data transfer performance under a parallel setting.

Today's scientific applications are producing vast amounts of data with cutting-edge high-performance computing systems and high-resolution instruments, causing severe problems in data transmission and storage. While error-controlled data compression is regarded as a direct way to solve the problem, most existing compressors are designed for data from structured meshes. In this work, we propose a generic framework to enable efficient error-controlled compression for scientific data from unstructured meshes. The contributions are four-fold: (1) We design a prediction-based framework with additional preprocessing stages to better incorporate mesh information. (2) We propose three families of prediction methods for unstructured meshes and integrate them into the framework, which yields high prediction accuracy and thus significantly improves the compression ratios and quality. (3) We enhance our framework by enabling invalid node processing and feature preservation. (4) We evaluate our approaches using five datasets from real-world applications and compare them with state-of-the-art error-controlled lossy compressors. Experiments demonstrate that the proposed compression methods deliver up to$26.36 \times, 8.24 \times$, and$2.56 \times$compression ratios over existing compressors under the same error bound, Peak Signal-to-Noise Ratios, and critical point preservation levels, respectively. This leads to$1.63 \times$performance speedup in the end-to-end data transfer on Globus.

Lossy compression has been employed to reduce the unprecedented amount of data produced by today's large-scale scientific simulations and high-resolution instruments. To avoid loss of critical information, state-of-the-art scientific lossy compressors provide error controls on relatively simple metrics such as absolute error bound. However, preserving these metrics does not translate to the preservation of topological features, such as critical points in vector fields. To address this problem, we investigate how to effectively preserve the sign of determinant in error-controlled lossy compression, as it is an important quantity of interest used for the robust detection of many topological features. Our contribution is three-fold. (1) We develop a generic theory to derive the allowable perturbation for one row of a matrix while preserving its sign of the determinant. As a practical use-case, we apply this theory to preserve critical points in vector fields because critical point detection can be reduced to the result of the point-in-simplex test that purely relies on the sign of determinants. (2) We optimize this algorithm with a speculative compression scheme to allow for high compression ratios and efficiently parallelize it in distributed environments. (3) We perform solid experiments with real-world datasets, demonstrating that our method achieves up to 440% improvements in compression ratios over state-of-the-art lossy compressors when all critical points need to be preserved. Using the parallelization strategies, our method delivers up to 1.25 x and 4.38 x performance speedup in data writing and reading compared with the vanilla approach without compression.

The conventional convolutional neural network (CNN) model has limitations in terms of global modeling ability, as it can only extract local features and is susceptible to noise interference. Due to repeated downsampling, small target features are easily lost in the deeper layers of the network. On the other hand, Transformer models are renowned for their exceptional global modeling capabilities; however, for the specific task of polyp images, effective results necessitate local feature extraction. Therefore, when reconsidering the relationship between local and global aspects within CNN and self-attention models, we propose a parallel relevant attention module with a Transformer structure for feature extraction and fusion. By combining channel attention and spatial attention mechanisms, we obtain semantic information from the lowest-level features to determine target feature locations accurately. Additionally, our step-by-step feature fusion module integrates shallow feature information into deeper layers through a CNN structure to capture more detailed target features comprehensively. Finally, these detailed target features are combined with underlying semantic information to achieve precise target segmentation.