The Potential of the Intel Xeon Phi for Supervised Deep Learning

Supervised learning of Convolutional Neural Networks (CNNs), also known as supervised Deep Learning, is a computationally demanding process. To find the most suitable parameters of a network for a given application, numerous training sessions are required. Therefore, reducing the training time per session is essential to fully utilize CNNs in practice. While numerous research groups have addressed the training of CNNs using GPUs, so far not much attention has been paid to the Intel Xeon Phi coprocessor. In this paper we investigate empirically and theoretically the potential of the Intel Xeon Phi for supervised learning of CNNs. We design and implement a parallelization scheme named CHAOS that exploits both the thread- and SIMD-parallelism of the coprocessor. Our approach is evaluated on the Intel Xeon Phi 7120P using the MNIST dataset of handwritten digits for various thread counts and CNN architectures. Results show a 103.5x speed up when training our large network for 15 epochs using 244 threads, compared to one thread on the coprocessor. Moreover, we develop a performance model and use it to assess our implementation and answer what-if questions.Comment: The 17th IEEE International Conference on High Performance Computing and Communications (HPCC 2015), Aug. 24 - 26, 2015, New York, US

Neural correlates of personality dimensions and affective measures during the anticipation of emotional stimuli

Neuroticism and extraversion are proposed personality dimensions for individual emotion processing. Neuroticism is correlated with depression and anxiety disorders, implicating a common neurobiological basis. Extraversion is rather inversely correlated with anxiety and depression. We examined neural correlates of personality in relation to depressiveness and anxiety in healthy adult subjects with functional magnetic resonance imaging during the cued anticipation of emotional stimuli. Distributed particularly prefrontal but also other cortical regions and the thalamus were associated with extraversion. Parieto-occipital and temporal regions and subcortically the caudate were correlated with neuroticism and affective measures. Neuroticism-related regions were partially cross-correlated with anxiety and depression and vice versa. Extraversion-related activity was not correlated with the other measures. The neural correlates of extraversion compared with those of neuroticism and affective measures fit with concepts of different neurobiological bases of the personality dimensions and point at predispositions for affective disorder

Performance of PE pipes under extractive conditions: Effect of the additive package and processing

Polyethylene (PE) compounds were prepared with five primary antioxidants and five application stabilizers, pelletized and extruded to pipes under industrial conditions. The pipes were stored in water at 80 °C for one year. Samples were taken at various intervals and a range of properties were determined from the functional group (methyl, vinyl, t-vinylene) content of the polymer to the crack propagation rate of the pipe. The results showed that chemical reactions take place both during extrusion and soaking. The chain structure of the polymer is modified only during processing, but not during storage, at least in the time scale of the study. The direction and extent of changes are determined mainly by the type of the application stabilizer, but primary antioxidants also influence them to some extent. Soaking modifies the physical, but not the chemical structure of the polymer. On the other hand, the chemical reactions of the additives determine color and stabilizer loss thus the residual stability of the pipes. The chemical structure of the polymer has a larger effect on final properties, on the rate of slow crack propagation and failure, than the physical structure of the pipes. As a consequence, the application stabilizer plays an important role in the determination of pipe performance