Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP

With ever-increasing volumes of scientific data produced by HPC applications, significantly reducing data size is critical because of limited capacity of storage space and potential bottlenecks on I/O or networks in writing/reading or transferring data. SZ and ZFP are the two leading lossy compressors available to compress scientific data sets. However, their performance is not consistent across different data sets and across different fields of some data sets: for some fields SZ provides better compression performance, while other fields are better compressed with ZFP. This situation raises the need for an automatic online (during compression) selection between SZ and ZFP, with a minimal overhead. In this paper, the automatic selection optimizes the rate-distortion, an important statistical quality metric based on the signal-to-noise ratio. To optimize for rate-distortion, we investigate the principles of SZ and ZFP. We then propose an efficient online, low-overhead selection algorithm that predicts the compression quality accurately for two compressors in early processing stages and selects the best-fit compressor for each data field. We implement the selection algorithm into an open-source library, and we evaluate the effectiveness of our proposed solution against plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results on three data sets representing about 100 fields show that our selection algorithm improves the compression ratio up to 70% with the same level of data distortion because of very accurate selection (around 99%) of the best-fit compressor, with little overhead (less than 7% in the experiments).Comment: 14 pages, 9 figures, first revisio

Toward smart and efficient scientific data management

Scientific research generates vast amounts of data, and the scale of data has significantly increased with advancements in scientific applications. To manage this data effectively, lossy data compression techniques are necessary to reduce storage and transmission costs. Nevertheless, the use of lossy compression introduces uncertainties related to its performance. This dissertation aims to answer key questions surrounding lossy data compression, such as how the performance changes, how much reduction can be achieved, and how to optimize these techniques for modern scientific data management workflows. One of the major challenges in adopting lossy compression techniques is the trade-off between data accuracy and compression performance, particularly the compression ratio. This trade-off is not well understood, leading to a trial-and-error approach in selecting appropriate setups. To address this, the dissertation analyzes and estimates the compression performance of two modern lossy compressors, SZ and ZFP, on HPC datasets at various error bounds. By predicting compression ratios based on intrinsic metrics collected under a given base error bound, the effectiveness of the estimation scheme is confirmed through evaluations using real HPC datasets. Furthermore, as scientific simulations scale up on HPC systems, the disparity between computation and input/output (I/O) becomes a significant challenge. To overcome this, error-bounded lossy compression has emerged as a solution to bridge the gap between computation and I/O. Nonetheless, the lack of understanding of compression performance hinders the wider adoption of lossy compression. The dissertation aims to address this challenge by examining the complex interaction between data, error bounds, and compression algorithms, providing insights into compression performance and its implications for scientific production. Lastly, the dissertation addresses the performance limitations of progressive data retrieval frameworks for post-hoc data analytics on full-resolution scientific simulation data. Existing frameworks suffer from over-pessimistic error control theory, leading to fetching more data than necessary for recomposition, resulting in additional I/O overhead. To enhance the performance of progressive retrieval, deep neural networks are leveraged to optimize the error control mechanism, reducing unnecessary data fetching and improving overall efficiency. By tackling these challenges and providing insights, this dissertation contributes to the advancement of scientific data management, lossy data compression techniques, and HPC progressive data retrieval frameworks. The findings and methodologies presented pave the way for more efficient and effective management of large-scale scientific data, facilitating enhanced scientific research and discovery. In future research, this dissertation highlights the importance of investigating the impact of lossy data compression on downstream analysis. On the one hand, more data reduction can be achieved under scenarios like image visualization where the error tolerance is very high, leading to less I/O and communication overhead. On the other hand, post-hoc calculations based on physical properties after compression may lead to misinterpretation, as the statistical information of such properties might be compromised during compression. Therefore, a comprehensive understanding of the impact of lossy data compression on each specific scenario is vital to ensure accurate analysis and interpretation of results

Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Data

Lossy compression has become an important technique to reduce data size in many domains. This type of compression is especially valuable for large-scale scientific data, whose size ranges up to several petabytes. Although Autoencoder-based models have been successfully leveraged to compress images and videos, such neural networks have not widely gained attention in the scientific data domain. Our work presents a neural network that not only significantly compresses large-scale scientific data but also maintains high reconstruction quality. The proposed model is tested with scientific benchmark data available publicly and applied to a large-scale high-resolution climate modeling data set. Our model achieves a compression ratio of 140 on several benchmark data sets without compromising the reconstruction quality. Simulation data from the High-Resolution Community Earth System Model (CESM) Version 1.3 over 500 years are also being compressed with a compression ratio of 200 while the reconstruction error is negligible for scientific analysis.Comment: 15 pages, 15 figure

AIMES: advanced computation and I/O methods for earth-system simulations

Dealing with extreme scale Earth-system models is challenging from the computer science perspective, as the required computing power and storage capacity are steadily increasing. Scientists perform runs with growing resolution or aggregate results from many similar smaller-scale runs with slightly different initial conditions (the so-called ensemble runs). In the fifth Coupled Model Intercomparison Project (CMIP5), the produced datasets require more than three Petabytes of storage and the compute and storage requirements are increasing significantly for CMIP6. Climate scientists across the globe are developing next-generation models based on improved numerical formulation leading to grids that are discretized in alternative forms such as an icosahedral (geodesic) grid. The developers of these models face similar problems in scaling, maintaining and optimizing code. Performance portability and the maintainability of code are key concerns of scientists as, compared to industry projects, model code is continuously revised and extended to incorporate further levels of detail. This leads to a rapidly growing code base that is rarely refactored. However, code modernization is important to maintain productivity of the scientist working with the code and for utilizing performance provided by modern and future architectures. The need for performance optimization is motivated by the evolution of the parallel architecture landscape from homogeneous flat machines to heterogeneous combinations of processors with deep memory hierarchy. Notably, the rise of many-core, throughput-oriented accelerators, such as GPUs, requires non-trivial code changes at minimum and, even worse, may necessitate a substantial rewrite of the existing codebase. At the same time, the code complexity increases the difficulty for computer scientists and vendors to understand and optimize the code for a given system. Storing the products of climate predictions requires a large storage and archival system which is expensive. Often, scientists restrict the number of scientific variables and write interval to keep the costs balanced. Compression algorithms can reduce the costs significantly but can also increase the scientific yield of simulation runs. In the AIMES project, we addressed the key issues of programmability, computational efficiency and I/O limitations that are common in next-generation icosahedral earth-system models. The project focused on the separation of concerns between domain scientist, computational scientists, and computer scientists

Temporal Lossy In-Situ Compression for Computational Fluid Dynamics Simulations

Während CFD Simulationen für Metallschmelze im Rahmen des SFB920 fallen auf dem Taurus HPC Cluster in Dresden sehr große Datenmengen an, deren Handhabung den wissenschaftlichen Arbeitsablauf stark verlangsamen. Zum einen ist der Transfer in Visualisierungssysteme nur unter hohem Zeitaufwand möglich. Zum anderen ist interaktive Analyse von zeitlich abhängigen Prozessen auf Grund des Speicherflaschenhalses nahezu unmöglich. Aus diesen Gründen beschäftigt sich die vorliegende Dissertation mit der Entwicklung sog. Temporaler In-Situ Kompression für wissenschaftliche Daten direkt innerhalb von CFD Simulationen. Dabei werden mittels neuer Quantisierungsverfahren die Daten auf ~10% komprimiert, wobei dekomprimierte Daten einen Fehler von maximal 1% aufweisen. Im Gegensatz zu nicht-temporaler Kompression, wird bei temporaler Kompression der Unterschied zwischen Zeitschritten komprimiert, um den Kompressionsgrad zu erhöhen. Da die Datenmenge um ein Vielfaches kleiner ist, werden Kosten für die Speicherung und die Übertragung gesenkt. Da Kompression, Transfer und Dekompression bis zu 4 mal schneller ablaufen als der Transfer von unkomprimierten Daten, wird der wissenschaftliche Arbeitsablauf beschleunigt

CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Design of Efficient and Adaptive Lossy Compression

As supercomputers continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design of efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) We propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline (from a variety of representative scientific datasets). (2) We derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also helps us create a fixed-ratio compression mode for consistent throughput. (3) We develop an efficient compression pipeline by adopting cuSZ's dual-quantization algorithm to our hardware use case. (4) We evaluate CEAZ on five real-world datasets with both a single FPGA board and 128 nodes from Bridges-2 supercomputer. Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2X of throughput and 9.6X of compression ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 25.8X and 24.8X, respectively.Comment: 14 pages, 17 figures, 8 table

Performance Modeling of Inline Compression With Software Caching for Reducing the Memory Footprint in PYSDC

Modern HPC applications compute and analyze massive amounts of data. The data volume is growing faster than memory capabilities and storage improvements leading to performance bottlenecks. An example of this is pySDC, a framework for solving collocation problems iteratively using parallel-in-time methods. These methods require storing and exchanging 3D volume data for each parallel point in time. If a simulation consists of M parallel-in-time stages, where the full spatial problem has to be stored for the next iteration, the memory demand for a single state variable is M ×Nx ×Ny ×Nz per time-step. For an application simulation with many state variables or stages, the memory requirement is considerable. Data compression helps alleviate the overhead in memory by reducing the size of data and keeping it in compressed format. Inline compression compresses and decompresses the application’s working set as it moves in and out of main memory. Thus, it provides the system with the appearance of more main memory. Naive compressed arrays require a compression or decompression operation for each store or load and therefore hurt the performance of the application. By incorporating a software cache and storing decompressed values of the array, we limit the number of compression and decompression operations for the stores and loads, thereby improving performance overall. In this thesis, we build a compression manager and software cache manager for the pySDC framework to reduce the memory requirements and computational overhead. The compression manager wraps around LibPressio, a C++ compression library that abstracts all compressors. We utilize blosc, a lossless compressor for our compression manager, and build a software cache manager with various cache configurations and cache policies to work in cohesion with the compression manager. We build a performance model which evaluates the compression manager and cache manager’s performance on different metrics such as compression ratio and compression/decompression time. We test our framework on two different pySDC applications — e.g., Allen-Cahn and Heat-diffusion. ii Results show that incorporating compression and increasing the cache size for our applications inflates the total compressed size in bytes for the arrays and therefore reduces the compression ratio, in contrast to our expectations. However, incorporating the cache and a greater cache size reduces the number of compression/decompression calls to LibPressio as well as cache evictions, significantly reducing the computational overhead for pySDC. Thus, overall, our compression and cache manager help reduce the memory footprint in pySDC. Future work involves looking at improving the compression ratio and using lossy compression to achieve significant reduction in memory footprint

Optimizing Error-Bounded Lossy Compression for Three-Dimensional Adaptive Mesh Refinement Simulations

Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most effective solutions to the above problem. However, little work has been done to improve error-bounded lossy compression for Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that only leverages 1D compression, in this work, we propose to leverage high-dimensional (e.g., 3D) compression for each refinement level of AMR data. To remove the data redundancy across different levels, we propose three pre-process strategies and adaptively use them based on the data characteristics. Experiments on seven AMR datasets from a real-world large-scale AMR simulation demonstrate that our proposed approach can improve the compression ratio by up to 3.3X under the same data distortion, compared to the state-of-the-art method. In addition, we leverage the flexibility of our approach to tune the error bound for each level, which achieves much lower data distortion on two application-specific metrics.Comment: 13 pages, 17 figures, 3 tables, accepted by ACM HPDC 202