The efficiency of Deep Learning (DL) training jobs is critically dependent on dataloaders, which facilitate the transfer of data from storage to DL-accelerated hardware during training. Recent advancements in data loading technology have demonstrated significant improvements, not only in reducing training times but also in introducing capabilities such as seamless integration with cloud storage. This paper examines the dataloader as a distinct component within the DL workflow, offering a detailed analysis of its structure and functionalities. We present a systematic evaluation of various dataloading libraries, investigating their performance across different configurations, including worker count, batch size, GPU scaling, data access patterns and remote loading. The evaluation highlights trade-offs in functionality, usability, and performance. Additionally, we examine the impact of dataset characteristics on data loading performance, showing that throughput decreases exponentially with image resolution. To support ongoing research and practical advancements, we introduce the first open-source benchmarking suite for DL data loading, which allows the community to replicate, extend, and build upon our experiments.

360-degree panoramic videos have gained considerable attention in recent years due to the rapid development of head-mounted displays (HMDs) and panoramic cameras. One major problem in streaming panoramic videos is that panoramic videos are much larger in size compared to traditional ones. Moreover, the user devices are often in a wireless environment, with limited battery, computation power, and bandwidth. To reduce resource consumption, researchers have proposed ways to predict the users’ viewports so that only part of the entire video needs to be transmitted from the server. However, the robustness of such prediction approaches has been overlooked in the literature: it is usually assumed that only a few models, pre-trained on past users’ experiences, are applied for prediction to all users. We observe that those pre-trained models can perform poorly for some users because they might have drastically different behaviors from the majority, and the pre-trained models cannot capture the features in unseen videos. In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction. This paradigm uses two machine learning models, where the first model predicts the viewing direction, and the second model predicts the minimum video prefetch size that can include the actual viewport. We first train two meta models so that they are sensitive to new training data, and then quickly adapt them to users while they are watching the videos. Evaluation results reveal that the meta models can adapt quickly to each user, and can significantly increase the prediction accuracy, especially for the worst-performing predictions.

In this paper, we study the impact of data complexity and data quality in the overlapping community detection problem. We show that community detection algorithms are very unstable against incomplete or erroneous data, and this result is consistent with all the evaluated performance metrics. We verify it using three quality metrics (F1, NMI, and Omega) when the ground-truth community structure is known, in four very popular and representative detection algorithms: Order Statistics Local Optimization Method (OSLOM), Greedy Clique Expansion (GCE) algorithm, Speaker-listener Label Propagation Algorithm (SLPA), and Cluster Affiliation Model for Big Networks (BIG-CLAM). We evaluate it over a set of real instances that arise from detecting the courses that belong to different careers (degrees) of an engineering University, and over large benchmark sets of synthetic instances frequently used in the literature.