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The 16,384-node parallelism of 3D-CNN Training on An Arm CPU based Supercomputer

The 16,384-node Parallelism of 3D-CNN Training on An Arm CPU...

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International conference on High Performance Computing

作者： Akihiro Tabuchi Koichi Shirahata Masafumi Yamazaki Akihiko Kasagi Takumi Honda Kouji Kurihara Kentaro Kawakami Tsuguchika Tabaru Naoto Fukumoto Akiyoshi Kuroda Takaaki Fukai Kento Sato Fujitsu Limited Kawasaki Kanagawa Japan RIKEN Center for Computational Science Kobe Hyogo Japan

ISBN: (纸本)9781665410175

As the computational cost and datasets available for deep neural network training continue to increase, there is a significant demand for fast distributed training on supercomputers. However, porting and tuning applications for new advanced supercomputers requires tremendous amount of development efforts. Therefore, we present software tuning best practice for a 3D-CNN model training on a new Arm CPU based supercomputer, Fugaku. We (i) tune computation in DL by a JIT translator for aarch64, (ii) optimize collective communication such as Allreduce for 6D mesh/torus network topology, (iii) tune I/O by data staging with compression and data loader with caching, and (iv) parallelize training in data and model parallelism. We apply the proposed methods to a CosmoFlow 3D-CNN model, and achieve the training in 30 minutes using 16,384 nodes consisting of 4096 data- and 4 model-parallelism. This is the fastest result of any CPU-based systems in MLPerf HPC v0.7 in the world.

关键词： Training Computational modeling Neural networks parallel processing Supercomputers Software Computational efficiency

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Task Distribution Method for Index Classification based on Color Segmentation in Remote Sensing

Task Distribution Method for Index Classification based on C...

引用

International conference on Advanced Information Technologies (ICAIT)

作者： Yan Naing Htun Bawin Aye Aung Aung Higher Education Center Pyin Oo Lwin Myanmar

ISBN: (数字)9781728183640

ISBN: (纸本)9781728183657

Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer.

关键词： Task analysis image color analysis image segmentation Program processors Indexes Support vector machines Multicore processing

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The Research of Implementation Method of Canny Edge Detection of Video on FPGA

The Research of Implementation Method of Canny Edge Detectio...

引用

Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Li Quan Tan Bin He School of information science and technology Beijing University of chemical technology

ISBN: (数字)9781728196688

ISBN: (纸本)9781728196695

Edge detection algorithm has an important application in video image processing. The algorithm overcomes the shortcomings of traditional video image edge detection methods, and significantly improves the quality of video image edge detection. However, the amount of calculation is very large, and the traditional implementation method is difficult to meet the real-time requirements of video image edge detection, and the implementation cost is high. In this design, field programmable gate array (FPGA) is used as the core to construct and implement Canny video image edge detection system. Firstly, the video image is collected by ov7725 camera in real time; then, the edge detection of video image based on Canny algorithm is implemented in FPGA. Finally, the effect of Canny video image edge detection is compared and analyzed. This design has a good reference value for canny video image edge detection.

关键词： image edge detection Information science image filtering

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Supervised Multi-View distributed Hashing

Supervised Multi-View Distributed Hashing

引用

IEEE International conference on image processing

作者： Yunpeng Tang Xiaobo Shen Zexuan Ji Tao Wang Peng Fu Quan-Sen Sun School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized sub-problems. The sub-problems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.

关键词： distributed databases Convex functions Benchmark testing Linear programming Computational modeling Binary codes Optimization

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MAD-C: Multi-stage Approximate distributed Cluster-Combining for Obstacle Detection and Localization 24th

MAD-C: Multi-stage Approximate Distributed Cluster-Combining...

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24th International European conference on parallel and distributed Computing (Euro-Par)

作者： Keramatian, Amir Gulisano, Vincenzo Papatriantafilou, Marina Tsigas, Philippas Nikolakopoulos, Yiannis Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9783030105495;9783030105488

Efficient distributed multi-sensor monitoring is a key feature of upcoming digitalized infrastructures. We address the problem of obstacle detection, having as input multiple point clouds, from a set of laser-based distance sensors;the latter generate high-rate data and can rapidly exhaust baseline analysis methods, that gather and cluster all the data. We propose MAD-C, a distributed approximate method: it can build on any appropriate clustering, to process disjoint subsets of the data distributedly;MAD-C then distills each resulting cluster into a data-summary. The summaries, computable in a continuous way, in constant time and space, are combined, in an order-insensitive, concurrent fashion, to produce approximate volumetric representations of the objects. MAD-C leads to (i) communication savings proportional to the number of points, (ii) multiplicative decrease in the dominating component of the processing complexity and, at the same time, (iii) high accuracy (with RandIndex > 0.95), in comparison to its baseline counterpart. We also propose MAD-C-ext, building on the MAD-C's output, by further combining the original data-points, to improve the outcome granularity, with the same asymptotic processing savings as MAD-C.

关键词： Point cloud processing Approximations Fog computing

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Classification Of Human Facial Expressions For Emotion Recognition Using A distributed Computer System

Classification Of Human Facial Expressions For Emotion Recog...

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International conference on Computer Science and Engineering (UBMK)

作者： Timuçin Korkmaz Hamza Erol Department of Computer Engineering Mersin University Mersin Turkey

ISBN: (数字)9781728175652

ISBN: (纸本)9781728175669

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image flies were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed are taken into account; and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

关键词： Face recognition distributed databases Computer architecture IP networks Servers Emotion recognition

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PowerSGD: Practical Low-Rank Gradient Compression for distributed Optimization 33

PowerSGD: Practical Low-Rank Gradient Compression for Distri...

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33rd conference on Neural Information processing Systems (NeurIPS)

作者： Vogels, Thijs Karinireddy, Sai Praneeth Jaggi, Martin Ecole Polytech Fed Lausanne Lausanne Switzerland

We study lossy gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well, or fail to achieve the target test accuracy. We propose a new low-rank gradient compressor based on power iteration that can i) compress gradients rapidly, ii) efficiently aggregate the compressed gradients using all-reduce, and iii) achieve test performance on par with SGD. The proposed algorithm is the only method evaluated that achieves consistent wall-clock speedups when benchmarked against regular SGD using highly optimized off-the-shelf tools for distributed communication. We demonstrate reduced training times for convolutional networks as well as LSTMs on common datasets. Our code is available at https://***/epfml/powersgd.

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Learning Hierarchical Graph for Occluded Pedestrian Detection 20

Learning Hierarchical Graph for Occluded Pedestrian Detectio...

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28th ACM International conference on Multimedia (MM)

作者： Li, Gang Li, Jian Zhang, Shanshan Yang, Jian Nanjing Univ Sci & Technol Nanjing Peoples R China Tencent Youtu Lab Shenzhen Peoples R China Nanjing Univ Sci & Technol PCA Lab Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Nanjing Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Socia Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781450379885

Although pedestrian detection has made significant progress with the help of deep convolution neural networks, it is still a challenging problem to detect occluded pedestrians since the occluded ones can not provide sufficient information for classification and regression. In this paper, we propose a novel Hierarchical Graph Pedestrian Detector (HGPD), which integrates semantic and spatial relation information to construct two graphs named intra-proposal graph and inter-proposal graph, without relying on extra cues w.r.t visible regions. In order to capture the occlusion patterns and enhance features from visible regions, the intra-proposal graph considers body parts as nodes and assigns corresponding edge weights based on semantic relations between body parts. On the other hand, the inter-proposal graph adopts spatial relations between neighbouring proposals to provide additional proposal-wise context information for each proposal, which alleviates the lack of information caused by occlusion. We conduct extensive experiments on standard benchmarks of CityPersons and Caltech to demonstrate the effectiveness of our method. On CityPersons, our approach outperforms the baseline method by a large margin of 5.24pp on the heavy occlusion set, and surpasses all previous methods;on Caltech, we establish a new state of the art of 3.78% MR. Code is available at https://***/ligang-cs/PedestrianDetection-HGPD.

关键词： computer vision object detection occluded pedestrian detection graph neural networks

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Accelerating Deep Learning Tasks with Optimized GPU-assisted image Decoding

Accelerating Deep Learning Tasks with Optimized GPU-assisted...

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International conference on parallel and distributed Systems (ICPADS)

作者： Lipeng Wang Qiong Luo Shengen Yan HKUST Hong Kong China SenseTime Research Shenzhen China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

In computer vision deep learning (DL) tasks, most of the input image datasets are stored in the JPEG format. These JPEG datasets need to be decoded before DL tasks are performed on them. We observe two problems in the current JPEG decoding procedures for DL tasks: (1) the decoding of image entropy data in the decoder is performed sequentially, and this sequential decoding repeats with the DL iterations, which takes significant time; (2) Current parallel decoding methods under-utilize the massive hardware threads on GPUs. To reduce the image decoding time, we introduce a pre-scan mechanism to avoid the repeated image scanning in DL tasks. Our pre-scan generates boundary markers for entropy data so that the decoding can be performed in parallel. To cooperate with the existing dataset storage and caching systems, we propose two modes of the pre-scan mechanism: a compatible mode and a fast mode. The compatible mode does not change the image file structure so pre-scanned files can be stored back to disk for subsequent DL tasks. In comparison, the fast mode crafts a JPEG image into a binary format suitable for parallel decoding, which can be processed directly on the GPU. Since the GPU has thousands of hardware threads, we propose a fine-grained parallel decoding method on the pre-scanned dataset. The fine-grained parallelism utilizes the GPU effectively, and achieves speedups of around 1.5× over existing GPU-assisted image decoding libraries on real-world DL tasks.

关键词： Deep learning Transform coding Graphics processing units Hardware Decoding Task analysis Iterative decoding

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DENOISING CONVOLUTIONAL AUTOENCODER BASED B-MODE ULTRASOUND TONGUE image FEATURE EXTRACTION 44

DENOISING CONVOLUTIONAL AUTOENCODER BASED B-MODE ULTRASOUND ...

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44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing Univ Posts & Telecommun Automat Dept Beijing 100876 Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Changsha Hunan Peoples R China Univ Michigan Dept Linguist Ann Arbor MI 48109 USA

ISBN: (纸本)9781479981311

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://***/DeePBluE666/Source-code1.

关键词： B-mode ultrasound tongue imaging unsupervised learning feature extraction convolutional autoencoder denoising convolutional autoencoder

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