Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and model quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these techniques and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification shows that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

In the compression of scientific data, error-controlled compressors enable to considerably decrease the size of the dataset while maintaining adequate levels of accuracy. In this paper, we note that multi-level refactoring scheme such as MGARD i) rely on an approximation of the data based on the interpolation of coefficients, ii) estimate the resulting error with global metrics on the dataset. To improve on these two aspects, we propose a method that aims to divide the original dataset into blocks based on their smoothness and refactors each block separately with the most relevant interpolation order. We show the relevance of such a method on tailored datasets and the benefits and challenges when applying it to large scientific data.

We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm’s greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP.

Scientists conduct large-scale simulations to compute derived quantities from primary data. Thus, it is crucial that data compression techniques maintain bounded errors on these derived quantities or quantities of interest (QOI). For many spatiotemporal applications, these QOIs are binary in nature and represent presence or absence of a physical phenomenon. In this work, we propose to use a hybrid approah for differential compression for such applications. We use a neural network (NN) approach to determine regions-of-interest (ROIs) where the binary QOIs are going to be prevalent. This is then used with traditional approaches that compress at a lower level (and higher accuracy) for these ROIs as compared to other regions.

As the disparity between compute and I/O on high-performance computing systems has continued to widen, it has become increasingly difficult to perform post-hoc data analytics on full-resolution scientific simulation data due to the high I/O cost. Error-bounded data decomposition and progressive data retrieval framework has recently been developed to address such a challenge by performing data decomposition before storage and reading only part of the decomposed data when necessary. However, the performance of the progressive retrieval framework has been suffering from the over-pessimistic error control theory, such that the achieved maximum error of recomposed data is significantly lower than the required error. Therefore, more data than required is fetched for recomposition, incurring additional I/O overhead. In order to tackle this issue, we propose a DNN-based progressive retrieval framework that can better identify the minimum amount of data to be retrieved. Our contributions are as follows: 1) We provide an in-depth investigation of the recently developed progressive retrieval framework; 2) We propose two designs of prediction models (named D-MGARD and E-MGARD) to estimate the amount of retrieved data size based on error bounds. 3) We evaluate our proposed solutions using scientific datasets generated by real-world simulations from two domains. Evaluation results demonstrate the effectiveness of our solution in accurately predicting the amount of retrieval data size, as well as the advantages of our solution over the traditional approach to reducing the I/O overhead. Based on our evaluation, our solution is shown to read significantly less data (5% - 40% with D-MGARD, 20% - 80% with E-MGARD).

The increase of computer processing speed is significantly outpacing improvements in network and storage bandwidth, leading to the big data challenge in modern science, where scientific applications can quickly generate much more data than that can be transferred and stored. As a result, big scientific data must be reduced by a few orders of magnitude while the accuracy of the reduced data needs to be guaranteed for further scientific explorations. Moreover, scientists are often interested in some specific spatial/temporal regions in their data, where higher accuracy is required. The locations of the regions requiring high accuracy can sometimes be prescribed based on application knowledge, while other times they must be estimated based on general spatial/temporal variation. In this paper, we develop a novel multilevel approach which allows users to impose region-wise compression error bounds. Our method utilizes the byproduct of a multilevel compressor to detect regions where details are rich and we provide the theoretical underpinning for region-wise error control. With spatially varying precision preservation, our approach can achieve significantly higher compression ratios than single-error bounded compression approaches and control errors in the regions of interest.

Solid state storage systems have been very effectively used in small devices; however, their effectiveness for large systems such as supercomputers is not yet proven. Recently, for the first time, a new supercomputer is being deployed with an all-flash storage as its main file system. In this work, we report our preliminary study of the I/O performance on this supercomputer named Perlmutter. We are able to achieve 1.4 TB/s with the default file configuration on the system. This default configuration outperforms dozens of other choices tested, though the current observed performance is still pretty far from the theoretical peak performance of 5 TB/s.

Runtime data sharing across applications is of great importance for avoiding high I/O overhead for scientific data analytics. Sharing data on a staging space running on a set of dedicated compute nodes is faster than writing data to a slow disk-based parallel file system (PFS) and then reading it back for post-processing. Originally, the staging space has been purely based on main memory (DRAM), and thus was several orders of magnitude faster than the PFS approach. However, storing all the data produced by large-scale simulations on DRAM is impractical. Moving data from memory to SSD-based burst buffers is a potential approach to address this issue. However, SSDs are about one order of magnitude slower than DRAM. To optimize data access performance over the staging space, methods such as prefetching data from SSDs according to detected spatial access patterns and distributing data across the network topology have been explored. Although these methods work well for uniform mesh data, which they were designed for, they are not well suited for adaptive mesh refinement (AMR) data. Two major issues must be addressed before constructing such a memory hierarchy and topology-aware runtime AMR data sharing framework: (1) spatial access pattern detection and prefetching for AMR data; (2) AMR data distribution across the network topology at runtime. We propose a framework that addresses these challenges and demonstrate its effectiveness with extensive experiments on AMR data. Our results show the framework's spatial access pattern detection and prefetching methods demonstrate about 26% performance improvement for client analytical processes. Moreover, the framework's topology-aware data placement can improve overall data access performance by up to 18%.

Many scientists rely on indexing and query to identify trends and anomalies within extreme-scale scientific data. Compressed bitmap indexing (e.g., FastBit) is the go-to indexing method for many scientific datasets and query workloads. Recently, the ALACRITY compressed inverted index was shown as a viable alternative approach. Notably, though FastBit and ALACRITY employ very different data structures (inverted list vs. bitmap) and binning methods (bit-wise vs. decimal-precision), close examination reveals marked similarities in index structure.

Efficient handling of large volumes of data is a necessity for exascale scientific applications and database systems. To address the growing imbalance between the amount of available storage and the amount of data being produced by high speed (FLOPS) processors on the system, data must be compressed to reduce the total amount of data placed on the file systems. General-purpose loss less compression frameworks, such as zlib and bzlib2, are commonly used on datasets requiring loss less compression. Quite often, however, many scientific data sets compress poorly, referred to as hard-to-compress datasets, due to the negative impact of highly entropic content represented within the data. An important problem in better loss less data compression is to identify the hard-to-compress information and subsequently optimize the compression techniques at the byte-level. To address this challenge, we introduce the In-Situ Orthogonal Byte Aggregate Reduction Compression (ISOBAR-compress) methodology as a preconditioner of loss less compression to identify and optimize the compression efficiency and throughput of hard-to-compress datasets.

The growing gap between the massive amounts of data generated by petascale scientific simulation codes and the capability of system hardware and software to effectively analyze this data necessitates data reduction. Yet, the increasing data complexity challenges most, if not all, of the existing data compression methods. In fact, lossless compression techniques offer no more than 10% reduction on scientific data that we have experience with, which is widely regarded as effectively incompressible. To bridge this gap, in this paper, we advocate a transformative strategy that enables fast, accurate, and multi-fold reduction of double-precision floating-point scientific data. The intuition behind our method is inspired by an effective use of preconditioners for linear algebra solvers optimized for a particular class of computational "dwarfs" (e.g., dense or sparse matrices). Focusing on a commonly used multi-resolution wavelet compression technique as the underlying "solver" for data reduction we propose the S-preconditioner, which transforms scientific data into a form with high global regularity to ensure a significant decrease in the number of wavelet coefficients stored for a segment of data. Combined with the subsequent EQ-calibrator, our resultant method (called S-Preconditioned EQ-Calibrated Wavelets (SPEQC-Wavelets)), robustly achieved a 4- to 5-fold data reduction-while guaranteeing user-defined accuracy of reconstructed data to be within 1% point-by-point relative error, lower than 0.01 Normalized RMSE, and higher than 0.99 Pearson Correlation. In this paper, we show the results we obtained by testing our method on six petascale simulation codes including fusion, combustion, climate, astrophysics, and subsurface groundwater in addition to 13 publicly available scientific datasets. We also demonstrate that application-driven data mining tasks performed on decompressed variables or their derived quantities produce results of comparable quality with the ones for the original data.