Analysis and Performance Evaluation of Deep Learning on Big Data Deep Learning (DL) and Big Data (BD) have converged to a hybrid computing paradigm that merges the dynamic processing in DL models with the computational power of the distributed processing of the BD frameworks. In this context, this work aims to conduct an analysis and performance evaluation of DL applications in BD. The experiments evaluate how the application training completion time can be related to the modelxe2x80x99s precision loss and the impacts of distributed computing in DL models. The experiments were performed in Microsoft Azure using BigDL framework, which allows using both Spark and TensorFlow on top of a Yarn cluster. The outcomes revealed a speedup of up to 8x and accuracy higher than 95%.

Performance Analysis and Characterization of Training Deep Learning Models on Mobile Device Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of enhancing user experiences. Most of the existing work on machine learning at mobile devices is focused on the inference of deep learning models, but not training. The performance characterization of training deep learning models on mobile devices is largely unexplored, although understanding the performance characterization is critical for designing and implementing deep learning models on mobile devices. In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models. We introduce a benchmark suite and a tool to study performance of training deep learning models on mobile devices, from the perspectives of memory consumption, hardware utilization, and power consumption. The tool can correlate performance results with finegrained operations in deep learning models, providing capabilities to capture performance variance and problems at a fine granularity. We reveal interesting performance problems and opportunities, including underutilization of heterogeneous hardware, large energy consumption of the memory, and high predictability of workload characterization. Based on the performance analysis, we suggest interesting research directions.