Memory failures in large-scale production environments pose critical threats to system reliability and service availability. While existing studies have conducted in-depth analyses of the temporal and spatial correlations of memory errors, differences in characteristics across architectures remain largely unexplored. To uncover these overlooked correlations, this paper conducts an extensive analysis of over 130,000 DDR4 DIMMs collected from large-scale heterogeneous production clusters over a nine-month period. Through systematic spatial and temporal analysis across two Intel x86 architectures and four major DRAM vendors, we uncover five new findings and propose a novel twostage training strategy. This strategy addresses sample quality issues by applying temporal weighting to positive samples and adaptive reweighting to negative samples. It also incorporates comprehensive multi-dimensional feature engineering, covering static, spatial, temporal, and micro-level characteristics. Finally, it integrates dual-driven sampling strategies and adaptive prediction timing to balance prediction accuracy and operational efficiency. Extensive evaluation shows that our CatBoost-based model achieves F1-scores of 49.9% on Intel x86v5 and 57.6% on Intel x86v6, substantially outperforming existing methods. This cross-architecture validation demonstrates the robustness and generalization of our approach across different hardware platforms. To the best of our knowledge, our work presents the first large-scale cross-architecture analysis of memory error patterns and provides new insights for production-scale memory failure prediction systems.

High-bandwidth memory (HBM) is regarded as a promising technology for fundamentally overcoming the memory wall. It stacks up multiple DRAM dies vertically to dramatically improve the memory access bandwidth. However, this architecture also comes with more severe reliability issues, since HBM not only inherits error patterns of the conventional DRAM, but also introduces new error causes. In this article, we conduct the first systematical study on HBM errors, which cover over 460 million error events collected from 19 data centers and span over two years of deployment under a variety of services. Through error analyses and methodology validations, we confirm that the HBM exhibits different error patterns from conventional DRAM, in terms of spatial locality, temporal correlation, and sensor metrics which make empirical prediction models for DRAM error prediction ineffective for HBM. We design and implement Calchas , a hierarchical failure prediction framework for HBM based on our findings, which integrate spatial, temporal, and sensor information from various device levels to predict upcoming failures. The results demonstrate the feasibility of failure prediction across hierarchical levels.

Non-volatile memory (NVM) opens up new opportunities to resolve scaling restrictions of main memory, yet it is still hindered by the write disturbance (WD) problem. The WD problem mistakenly transforms the values of NVM cells, hence seriously deteriorating memory reliability and downgrading access performance. Existing studies mainly mitigate the WD problem via encoding WD-prone data patterns under in-place updates, yet we find that when turning to out-of-place updates, they can gain the potential to reduce more WD errors. We present LearnWD, an approach that mitigates the WD problem in NVM via coupling machine learning with out-of-place updates. LearnWD first employs clustering algorithms to classify the stale data based on the error proneness. To perform a write operation, LearnWD carefully examines the aggressivity of new data and the error proneness of stale data, so as to speculatively minimize the resulting WD errors. We conduct extensive experiments using 15 real-world datasets with different data types, showing that LearnWD can assist a variety of data encoding schemes to further reduce 19.5% of WD errors, shorten 10.1% of write latency, and extend 22.2% of write endurance.

We present a novel privacy preservation strategy for aggregated visual query of decentralized data. The key idea is to imitate the flowchart of the federated learning framework, and reformulate the visualization process within a federated infrastructure. The federation of visualization is fulfilled by leveraging a shared global module that composes the encrypted externalizations of transformed visual features of data pieces in local modules. We design two implementations of federated visualization: a prediction-based scheme, and a query-based scheme. We demonstrate the effectiveness of our approach with a set of visual forms, and verify its robustness with evaluations. We report the value of federated visualization in real scenarios with an expert review.

Federated Learning (FL) provides a powerful solution to distributed machine learning on a large corpus of decentralized data. It ensures privacy and security by performing computation on devices (which we refer to as clients) based on local data to improve the shared global model. However, the inaccessibility of the data and the invisibility of the computation make it challenging to interpret and analyze the training process, especially to distinguish potential client anomalies. Identifying these anomalies can help experts diagnose and improve FL models. For this reason, we propose a visual analytics system, VADAF, to depict the training dynamics and facilitate analyzing potential client anomalies. Specifically, we design a visualization scheme that supports massive training dynamics in the FL environment. Moreover, we introduce an anomaly detection method to detect potential client anomalies, which are further analyzed based on both the client model’s visual and objective estimation. Three case studies have demonstrated the effectiveness of our system in understanding the FL training process and supporting abnormal client detection and analysis.

We design and evaluate a novel layout fine-tuning technique for node-link diagrams that facilitates exemplar-based adjustment of a group of substructures in batching mode. The key idea is to transfer user modifications on a local substructure to other substructures in the entire graph that are topologically similar to the exemplar. We first precompute a canonical representation for each substructure with node embedding techniques and then use it for on-the-fly substructure retrieval. We design and develop a light-weight interactive system to enable intuitive adjustment, modification transfer, and visual graph exploration. We also report some results of quantitative comparisons, three case studies, and a within-participant user study.

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.

Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.