We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range of deep learning models.
The last decade has witnessed growth in the computational requirements for training deep neural networks.
One critical issue for efficiently operating practical AI clouds, is to characterize the computing and data transfer demands of these workloads, and more importantly, the training performance given the underlying software framework and hardware configurations.
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning.
In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data.
In recent years, there is a surge on machine learning applications in industry.
Distributed, Parallel, and Cluster Computing Mathematical Software
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems.
We also applied GPU-FV for realtime video monitoring tasks and found that GPU-FV outperforms a number of previous works.