Accurately predicting the training time of deep learning (DL) workloads is critical for optimizing the utilization of data centers and allocating the required cluster resources for completing critical model training tasks before a deadline. The state-of-the-art prediction models, e.g., Ernest and Cherrypick, treat DL workloads as black boxes, and require running the given DL job on a fraction of the dataset. Moreover, they require retraining their prediction models every time a change occurs in the given DL workload. This significantly limits the reusability of prediction models across DL workloads with different deep neural network (DNN) architectures. In this paper, we address this challenge and propose a novel approach where the prediction model is trained only once for a particular dataset type, e.g., ImageNet, thus completely avoiding tedious and costly retraining tasks for predicting the training time of new DL workloads. Our proposed approach, called PredictDDL, provides an end-to-end system for predicting the training time of DL models in distributed settings. PredictDDL leverages Graph HyperNetworks, a class of neural networks that takes computational graphs as input and produces vector representations of their DNNs. PredictDDL is the first prediction system that eliminates the need of retraining a performance prediction model for each new DL workload and maximizes the reuse of the prediction model by requiring running a DL workload only once for training the prediction model. Our extensive evaluation using representative workloads shows that PredictDDL achieves up to 9.8× lower average prediction error and 10.3× lower inference time compared to the state-of-the-art system, i.e., Ernest, on multiple DNN architectures.

App stores enable users to share their experiences directly with the developers in the form of app reviews. Recent studies have shown that the feedback received from users is a valuable source of information for requirements extraction, which encourages app developers to leverage the reviews for app update and maintenance purposes. Follow-up studies proposed automated techniques to help developers filter the large volume of daily and noisy reviews and/or summarize their content. However, all previous studies approached the app reviews classification and summarization as separate tasks, which complicated the process and introduced unnecessary overhead. Moreover, none of those approaches explored the potential of utilizing the hierarchical relationships that exist between the labels of app reviews for the purpose of building a more accurate model. In this work, we propose Hierarchical Multi-Kernel Relevance Vector Machines (HMK-RVM), a Bayesian multi-kernel technique that integrates app review classification and summarization using a unified model. Moreover, it can provide insights into the learned patterns and underlying data for easier model interpretation. We evaluated our proposed approach on two real-world datasets and showed that in addition to the gained insights, the model produces equal or better results than the state of the art.

We focus on using natural language unstructured textual Knowledge Bases (KBs) to answer questions from community-based Question-and-Answer (Q8A) websites. We propose a novel framework that integrates multi-level tag recommendation with external KBs to retrieve the most relevant KB articles to answer user posted questions. Different from many existing efforts that primarily rely on the Q8A sites’ own historical data (e.g., user answers), retrieving answers from authoritative external KBs (e.g., online programming documentation repositories) has the potential to provide rich information to help users better understand the problem, acquire the knowledge, and hence avoid asking similar questions in future. The proposed multi-level tag recommendation best leverages the rich tag information by first categorizing them into different semantic levels based on their usage frequencies. A post-tag co-clustering model, augmented by a two-step tag recommender, is used to predict tags at different levels for a given user posted question. A KB article retrieval component leverages the recommended multi-level tags to select the appropriate KBs and search/rank the matching articles thereof. We conduct extensive experiments using real-world data from a Q8A site and multiple external KBs to demonstrate the effectiveness of the proposed question-answering framework.