Deep learning plays a critical role in numerous intelligent software applications. Enterprise developers submit and run deep learning jobs on shared, multi-tenant platforms to efficiently train and test models. These platforms are typically equipped with a large number of graphics processing units (GPUs) to expedite deep learning computations. However, certain jobs exhibit rather low utilization of the allocated GPUs, resulting in substantial resource waste and reduced development productivity. This paper presents a comprehensive empirical study on low GPU utilization of deep learning jobs, based on 400 real jobs (with an average GPU utilization of 50% or less) collected from Microsoft's internal deep learning platform. We discover 706 low-GPU-utilization issues through meticulous examination of job metadata, execution logs, runtime metrics, scripts, and programs. Furthermore, we identify the common root causes and propose corresponding fixes. Our main findings include: (1) Low GPU utilization of deep learning jobs stems from insufficient GPU computations and interruptions caused by non-GPU tasks; (2) Approximately half (46.03%) of the issues are attributed to data operations; (3) 45.18% of the issues are related to deep learning models and manifest during both model training and evaluation stages; (4) Most (84.99%) low-GPU-utilization issues could be fixed with a small number of code/script modifications. Based on the study results, we propose potential research directions that could help developers utilize GPUs better in cloud-based platforms.

Image enhancement is a technique to enhance the illumination of dark images while keeping the reality and the naturalness of the enhanced images at the same time. For color images, most methods tackle different color channels in a separate way, which overlooks the connection between the color channels. Therefore, in this paper, we consider a quaternion-based model to reserve the color connectivity, which integrates the color information of a pixel by a quaternion number. Moreover, we propose a regularizer based on the gamma-correction function and incorporate it into a screened Poisson equation for the image enhancement task. The uniqueness and existence of the solution of the proposed model are analyzed. The numerical results also prove the superiority of our scheme for color image enhancement.