Graph Neural Networks (GNNs) have emerged as powerful machine learning models for numerous graph-based applications. However, existing GNN training frameworks cannot scale the training process elastically, resulting in poor training throughput and low cluster utilization. Although elastic training has been proposed for Deep Neural Networks (DNNs), it cannot be directly adopted to GNNs due to the prohibitive scaling cost and inefficient scheduling. In this paper, we present ElasGNN, an elastic GNN training framework that achieves efficient dynamic resource allocation for GNN jobs. ElasGNN proposes an efficient elastic training engine to achieve high-performant GNN job scaling and introduces novel graph repartitioning algorithms for both scale-in and scale-out processes to further minimize the scaling cost. Moreover, ElasGNN designs an efficient elastic scheduler, utilizing a scaling-cost-aware scheduling policy to improve the GPU utilization and system throughput. The experimental results show that the ElasGNN can achieve shorter job completion time and makespan for training jobs of diverse GNN models.

Self-Supervised Semantic Segmentation, aiming to leverage masses of unlabeled data for boosting semantic segmentation, has been rapidly emerging as an active task in recent years. However, existing self-supervised semantic segmentation approaches mainly focus on planar images, leaving multiple distorted objects encountered in panoramic images unexplored due to the formidable challenge of handling heterogeneous degrees of distortions across different locations. In this paper, we propose a novel Self-Supervised Panoramic Semantic Segmentation model, termed DASC-SPT, built upon the mainstream contrastive learning framework. Towards distortions in panoramic images, we present two structures to better learn from distorted features by applying planar images. For the input images of self-supervision, we design a Spherical Projection Transformation (SPT) strategy that involves randomly projecting planar images onto various locations of the sphere to introduce the distortions. For pixel-wise distorted features, we construct a Deformation-aware Sampling Consistency (DASC) framework to further utilize the shared content and discrepancies caused by different distortions of paired views, where the deformation-aware consistency can be quantified on pixel-wise features. Both of the two components facilitate the model to adapt to distortions and boost panoramic semantic segmentation. Extensive comprehensive experiments on three panoramic datasets demonstrate the effectiveness and superiority of DASC-SPT approach.

Fault localization (FL) and automated program repair (APR) are two main tasks of automatic software debugging. Compared with traditional methods, deep learning-based approaches have been demonstrated to achieve better performance in FL and APR tasks. However, the existing deep learning-based FL methods ignore the deep semantic features or only consider simple code representations. And for APR tasks, existing template-based APR methods are weak in selecting the correct fix templates for more effective program repair, which are also not able to synthesize patches via the embedded end-to-end code modification knowledge obtained by training models on large-scale bug-fix code pairs. Moreover, in most of FL and APR methods, the model designs and training phases are performed separately, leading to ineffective sharing of updated parameters and extracted knowledge during the training process. This limitation hinders the further improvement in the performance of FL and APR tasks. To solve the above problems, we propose a novel approach called MTL-TRANSFER, which leverages a multi-task learning strategy to extract deep semantic features and transferred knowledge from different perspectives. First, we construct a large-scale open-source bug datasets and implement 11 multi-task learning models for bug detection and patch generation sub-tasks on 11 commonly used bug types, as well as one multi-classifier to learn the relevant semantics for the subsequent fix template selection task. Second, an MLP-based ranking model is leveraged to fuse spectrum-based, mutation-based and semantic-based features to generate a sorted list of suspicious statements. Third, we combine the patches generated by the neural patch generation sub-task from the multi-task learning strategy with the optimized fix template selecting order gained from the multi-classifier mentioned above. Finally, the more accurate FL results, the optimized fix template selecting order, and the expanded patch candidates are combined together to further enhance the overall performance of APR tasks. Our extensive experiments on widely-used benchmark Defects4J show that MTL-TRANSFER outperforms all baselines in FL and APR tasks, proving the effectiveness of our approach. Compared with our previously proposed FL method TRANSFER-FL (which is also the state-of-the-art statement-level FL method), MTL-TRANSFER increases the faults hit by 8/11/12 on Top-1/3/5 metrics (92/159/183 in total). And on APR tasks, the number of successfully repaired bugs of MTL-TRANSFER under the perfect localization setting reaches 75, which is 8 more than our previous APR method TRANSFER-PR. Furthermore, another experiment to simulate the actual repair scenarios shows that MTL-TRANSFER can successfully repair 15 and 9 more bugs (56 in total) compared with TBar and TRANSFER, which demonstrates the effectiveness of the combination of our optimized FL and APR components.