Many complex problems, such as natural language processing or visual object detection, are solved using deep learning. However, efficient training of complex deep convolutional neural networks for large data sets is computationally demanding and requires parallel computing resources. In this paper, we present two parameterized performance models for estimation of execution time of training convolutional neural networks on the Intel many integrated core architecture. While for the first performance model we minimally use measurement techniques for parameter value estimation, in the second model we estimate more parameters based on measurements. We evaluate the prediction accuracy of performance models in the context of training three different convolutional neural network architectures on the Intel Xeon Phi. The achieved average performance prediction accuracy is about 15 for the first model and 11 for second model.
Accelerating Convolutional Neural Networks (CNNs) on GPUs usually involves two stages: training and inference. Traditionally, this two-stage process is deployed on high-end GPU-equipped servers. Driven by the increase in compute power of desktop and mobile GPUs, there is growing interest in performing inference on various kinds of platforms. In contrast to the requirements of high throughput and accuracy during the training stage, end-users will face diverse requirements related to inference tasks. To address this emerging trend and new requirements, we propose Pervasive CNN (P-CNN), a user satisfaction-aware CNN inference framework. P-CNN is composed of two phases: cross-platform offline compilation and run-time management. Based on users' requirements, offline compilation generates the optimal kernel using architecture-independent techniques, such as adaptive batch size selection and coordinated fine-tuning. The runtime management phase consists of accuracy tuning, execution, and calibration. First, accuracy tuning dynamically identifies the fastest kernels with acceptable accuracy. Next, the run-time kernel scheduler partitions the optimal computing resource for each layer and schedules the GPU thread blocks. If its accuracy is not acceptable to the end-user, the calibration stage selects a slower but more precise kernel to improve the accuracy. Finally, we design a user satisfaction metric for CNNs to evaluate ourPervasive deign. Our evaluation results show P-CNN can provide the best user satisfaction for different inference tasks.