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Test-driving Intel Xeon Phi
- Jianbin Fang, H. Sips, Lilun Zhang, Chuanfu Xu, Yonggang Che, A. Varbanescu
- Computer ScienceICPE
- 22 March 2014
The experience indicates that a simple data structure and massive parallelism are critical for Xeon Phi to perform well, and when compiler-driven parallelization and/or vectorization fails, programming Xeon Phi for performance can become very challenging.
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network
This paper explores the use of Multi-channel CNN for the classification task, which aims to extract features from different channels in an end-to-end manner, and explores the using of mixup method, which can provide higher prediction accuracy and robustness in contrast with previous models.
An Empirical Study of Intel Xeon Phi
- Jianbin Fang, A. Varbanescu, H. Sips, Lilun Zhang, Yonggang Che, Chuanfu Xu
- Computer ScienceArXiv
- 22 October 2013
This paper micro-benchmarked the main hardware components of Xeon Phi, and shows that, in ideal microbenchmarking conditions, the performance that can be achieved is very close to the theoretical peak, as given in the official programmer's guide.
Collaborating CPU and GPU for large-scale high-order CFD simulations with complex grids on the TianHe-1A supercomputer
Performance optimizations for scalable CFD applications on hybrid CPU+MIC heterogeneous computing system with millions of cores
Environmental Sound Classification Based on Multi-temporal Resolution Convolutional Neural Network Combining with Multi-level Features
- Boqing Zhu, Kele Xu, Dezhi Wang, Lilun Zhang, Bo Li, Yuxing Peng
- Computer Science, Environmental SciencePCM
- 24 May 2018
Results demonstrate that the proposed method is highly effective in the classification tasks by employing multi-temporal resolution and multi-level features, and it outperforms the previous methods which only account for single- level features.
Large-Scale Whale-Call Classification by Transfer Learning on Multi-Scale Waveforms and Time-Frequency Features
- Lilun Zhang, Dezhi Wang, C. Bao, Yongxian Wang, Kele Xu
- Computer ScienceApplied Sciences
- 12 March 2019
An effective data-driven approach based on pre-trained Convolutional Neural Networks using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales.
Benchmarking Intel Xeon Phi to Guide Kernel Design
Four hardware-centric guidelines and a machine model for Xeon Phi programmers in search for performance are presented and it is shown that, in ideal microbenchmarking conditions, the achieved performance is very close to the theoretical one as given in the official programmer's guide.
Acoustic Scene Classification Based on Dense Convolutional Networks Incorporating Multi-channel Features