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Memory Access Optimization of high-Order CFD stencil Computations on GPU 1

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21st international Conference on Parallel and Distributed computing, Applications, and Technologies, PDCAT 2020

作者： Wang, Shengxiang Li, Zhuoqian Che, Yonggang Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

stencils computations are a class of computations commonly found in scientific and engineering applications. They have relatively lower arithmetic intensity. Therefore, their performance is greatly affected by memory access. This paper studies the issue of memory access optimization for the key stencil computations of a high-order CFD program on the NVidia GPU. Two methods are used to optimize the performance. First, we use registers to cache the data used by the stencil computations in the kernel. We use the CUDA warp shuffle functions to exchange data between neighboring grid points, and adjust the thread computation granularity to increase the data reuse. Second, we use the shared memory to buffer the grid data used by the stencil computations in the kernel, and utilize loop tiling to reduce redundant accesses to the global memory. performance evaluation is done on an NVidia Tesla K80 GPU. The results show that compared to the original implementation that only uses the global memory, the optimized implementation that utilizes the registers achieves a maximum speedup of 2.59 and 2.79 relatively for 15M and 60M grids, and the optimized implementation that utilizes the shared memory achieves a maximum speedup of 3.51 and 3.36 relatively for 15M and 60M grids. © 2021, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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AVM-BTB: Adaptive and Virtualized Multi-level Branch Target Buffer

AVM-BTB: Adaptive and Virtualized Multi-level Branch Target ...

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Annual international symposium on computer architecture, ISCA

作者： Yunzhe Liu Xinyu Li Tingting Zhang Tianyi Liu Qi Guo Fuxin Zhang Jian Wang State Key Lab of Processors Insititute of Computing Technology CAS University of Chinese Academy of Sciences Loongson Technology Co. Ltd. University of Texas at San Antonio

ISBN: (数字)9798350326581

ISBN: (纸本)9798350326598

Branch Target Buffer (BTB) plays an important role in modern processors. It is used to identify branches in the instruction stream and predict branch targets. The accuracy of BTB is highly impacted by BTB capacity. However, expanding BTB capacity using traditional methods requires valuable on-chip SRAM. Both timing and area restriction make these approaches unsustainable. Moreover, these methods overlook the different demands of various applications, leading to increased power consumption and resource waste in some cases. To address this problem, we propose AVM-BTB. The key observations behind AVM-BTB come from three aspects: 1) BTB requirements vary over different applications and even over different running stages of the same application. 2) Micro-operation Cache (Uop Cache) and ICache exhibit inefficiency when confronted with instruction footprints that greatly exceed their capacity. 3) In specific scenarios of frontend overload, reducing cache capacity and increasing BTB size can effectively mitigate expensive branch prediction errors. Simultaneously, the implementation of Fetch Directed Instruction Prefetching (FDIP) can offset the limitations in cache capacity to some extent. These observations reveal the feasibility of dynamically borrowing cache capacity as temporary BTB and returning these BTB to cache when they are not needed, further resulting in an adaptive and virtualized multi-level BTB scheme. However, such a BTB structure is non-trivial. In this work, from the perspective of instructions, the cache hierarchy stores instruction data, while the BTB stores metadata used for branch prediction and instruction prefetch. Targeting high performance, AVM-BTB maintains a dynamic balance between data and metadata by monitoring the BTB error rate and effective accesses. Evaluation with 1253 traces shows that AVM-BTB is suitable for both frontend-bound and frontend-friendly scenarios, without consuming additional SRAM and with reasonable implementation efforts. C

关键词： Power demand Program processors Prefetching Random access memory Metadata System-on-chip Timing

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TransFiLM: An Efficient and Lightweight Audio Enhancement Network for Low-Cost Wearable Sensors

TransFiLM: An Efficient and Lightweight Audio Enhancement Ne...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Shilin Chen Jianhao Weng Shicong Hong Yanbo He Yongpan Zou Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Information Hub Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Wearable devices such as headphones are increasingly popular in people's lives, and there is an increasing focus on how to achieve continuous and reliable information input using these devices. However, due to constraints in computing power, low power consumption, and low operating frequencies, such devices often record and transmit signals at lower sampling rates, the resultant lower-quality signals often have catastrophic implications for system performance. Efficient real-time conversion of low-resolution speech signals to full-resolution high-quality signals using low-cost wearable sensors on edge devices is a challenging research endeavor. To address this, this paper designs TransFiLM, a mobile deep learning network. It allows users to obtain full-resolution high-quality audio signals using low-cost wearable sensors on edge devices. TransFiLM integrates residual learning and super-resolution networks and employs effective signal processing strategies to achieve audio upscaling and noise reduction, significantly improving audio quality. We implement a prototype on commercial devices and conduct a series of experiments to evaluate its performance. Using signal-to-noise ratio (SNR) and log-spectral distance (LSD) as evaluation metrics, TransFiLM exhibits superior performance compared to other time-domain methods in cross-user, cross-corpus, and cross-noise environment testing. Additionally, our TransFiLM network handles 8192 samples with a response time of 181 ms, which meets the requirement to run in real-time on edge devices.

关键词： performance evaluation Deep learning Noise reduction Real-time systems Time factors Wearable devices Time-domain analysis Wearable sensors Intelligent sensors Signal to noise ratio

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A Model Based on First Principles for the Simulation of Partial Discharges 21st

A Model Based on First Principles for the Simulation of Part...

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21st international symposium on high Voltage Engineering (ISH)

作者： Villa, A. Barbieri, L. Malgesini, R. RSE I-20134 Milan Italy

ISBN: (纸本)9783030316761;9783030316754

Partial discharges represent one of the main mechanisms of ageing of dielectrics especially when an alternated current is used. This problem is particularly severe when partial discharges are associated with degenerative phenomena such as the electrical treeing. In this work, we present the latest development of our simulation codes, which are capable of simulating the evolution of discharges in complex three-dimensional geometries through parallel high-performance-computing technologies. The code is capable of both predicting the evolution of some macroscopic quantities, that can be measured directly, and estimating the progression of the internal ageing. For instance, it is possible to simulate the creation of chemically active species in the gas and their interactions with the surfaces of the branches. Some examples of the results obtained in a set of test cases will be discussed here.

关键词： Partial discharges Simulation Parallel computing

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SFTA: Spiking Neural Networks Vulnerable to Spiking Feature Transferable Attack

SFTA: Spiking Neural Networks Vulnerable to Spiking Feature ...

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IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xuanwei Lin Chen Dong Ximeng Liu College of Computer and Data Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Key Lab of Information Security of Network Systems (Fuzhou University)

Many recent works have shown that spiking neural networks (SNNs) are vulnerable to well-crafted adversarial attacks in the white-box setting, which may lead to misclassification of the trained model. However, the information about the model is in a black-box setting and is usually unknown to the attacker, e.g., the parameters and structure of the model. So it is more challenging to counter the attack in the black-box setting, and there is still little research on this aspect in SNNs. Therefore, this work describes a transfer-based black box adversarial attack against spike feature space representations, providing insights for robust spike features in SNNs models. The attack is explicitly designed to be transferable, and more robust feature gradients are obtained by accumulating the feature gradients of the intermediate layers. Then we can successfully attack the black-box model by suppressing robust positive features and enhancing negative features. Extensive experimental evaluation demonstrates the proposed method’s effectiveness and superior performance on static and neuromorphic datasets. i.e., The attack success is 89.72% and 90% on the static MNIst and CIFAR10 datasets, and 85.97% and 69.09% on the neuromorphic N-MNIst and CIFARO-DVS datasets.

关键词： Correlation Neuromorphics Neural networks Closed box Ubiquitous computing Optimization Glass box

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A Two-stage Deep Learning-Based Approach for Automatic Reading of Analog Meters 11

A Two-Stage Deep Learning-Based Approach for Automatic Readi...

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Joint 11th international Conference on Soft computing and Intelligent Systems and 21st international symposium on Advanced Intelligent Systems (SCIS-ISIS)

作者： Ueda, Shohei Suzuki, Kaito Kanno, Jumpei Zhao, Qiangfu Univ Aizu Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781728197326

In many factories in Japan, there are still thousands of analog meters to monitor and control the fabrication environments. Human inspectors must walk around, read the meters, record the values, and input them into the computer for further analysis. This process is prone to human errors. To solve the problem, we may consider to replace all analog meters with digital ones, but this is not practical due to the high replacement cost. In this study, we propose a system to automate the meter reading process. Using this system, an inspector just takes a short video of the meter using a smart phone, sends the video to a server, and the server can read the meter value and record the results automatically. To make the system practically useful, we introduce three techniques. The first one is a two-stage deep learning-based method for detecting the meter and its pointer;the second one is homography transform to normalize the view point for capturing the video;and the third is a multi-frame-based method for improving the reading accuracy. Experimental results with circular meters show that the accuracy of the proposed system is acceptable. This paves a way for us to automate the reading process of other types of analog meters.

关键词： computer vision image object detection error analysis

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Reconstruction and Digital Display Design of Women’s Clothing in Jiangnan Area of Yuan Dynasty under Digital

Reconstruction and Digital Display Design of Women’s Clothi...

引用

IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Yanzhen Liu Beibei Zhang College of Textile and Clothing Engineering Soochow University Suzhou China School of Art Soochow University Suzhou China

Digital clothing is a digital clothing model corresponding to the clothing in the real physical world, which makes the clothing image in the real physical world match and map with the clothing image in the digital virtual reality. Through the creation of highly similar clothing model, the digital Archaeology Research on clothing and the digital display and digital dissemination of clothing are realized. This paper selects the important unearthed clothing materials of women’s clothing in the southern part of the Yangtze River in the Yuan Dynasty. The material objects of clothing unearthed at the Qianyu Tomb in Wuxi, Jiangsu, and Cao Tomb of Zhang Shicheng, Suzhou, are used as objects for digital clothing reconstruction. Through computer interactive reverse engineering experiments, the digital clothing of women in the Southern part of the Yuan Dynasty is reconstructed. The hanging and wearability of digital clothing are tested by software, and an interactive interface of small programs for digital clothing display is designed. Experiments show that the digitally reconstructed clothing and unearthed clothing objects have a high similarity, and the reconstructed digital clothing through digital display and digital clothing platform applet, to achieve the dissemination of clothing cultural information and the sharing of clothing information resources, to study the value of digital protection and inheritance of ancient clothing.

关键词： Solid modeling Archeology Computational modeling Clothing Reverse engineering Virtual reality Ubiquitous computing

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An Effective Automatic Recognition Approach of Chinese Herbal Medicine by Micro Image Processing

An Effective Automatic Recognition Approach of Chinese Herba...

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IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Guangyao Pang Zhenming Yu Honglin Pan Chengwen Xie Ping Gong Xiaoying Zhu Zizhen Peng Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： Deep learning Image recognition Costs Microscopy Transfer learning Ubiquitous computing Raw materials

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21st international Conference on Parallel and Distributed computing, Applications, and Technologies, PDCAT 2020

21st International Conference on Parallel and Distributed Co...

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21st international Conference on Parallel and Distributed computing, Applications, and Technologies, PDCAT 2020

ISBN: (纸本)9783030692438

The proceedings contain 35 papers. The special focus in this conference is on Parallel and Distributed computing, Applications, and Technologies. The topics include: On the Non-ergodic Convergence Rate of the Directed Nonsmooth Composite Optimization;6D Pose Estimation Based on the Adaptive Weight of RGB-D Feature;blockchain-Based Secure Outsourcing of Fully Homomorphic Encryption Using Hidden Ideal Lattice;multiple Projections Learning for Dimensional Reduction;Preventing DDoS Attacks on Bitcoin Memory Pool by the Dynamic Fee Threshold Mechanism;The Compiler of DFC: A Source Code Converter that Transform the Dataflow Code to the Multi-threaded C Code;online Learning-Based Co-task Dispatching with Function Configuration in Edge computing;System-Level FPGA Routing for Logic Verification with Time-Division Multiplexing;protein Interresidue Contact Prediction Based on Deep Learning and Massive Features from Multi-sequence Alignment;heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder;see Fine Color from the Rough Black-and-White;data Aggregation Aware Routing for Distributed Training;a New Integer Programming Model for the File Transfer Scheduling Problem;approximation Algorithms for the General Cluster Routing Problem;maximizing Group Coverage in Social Networks;lightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification;the Hybrid Navigation Method in Face of Dynamic Obstacles;a Relaxed Balanced Lock-Free Binary Search Tree;A Dynamic Parameter Tuning Method for high performance SpMM;data Caching Based Transfer Optimization in Large Scale Networks;a Novel Distributed Reinforcement Learning Method for Classical Chinese Poetry Generation;second-Order Convolutional Neural Network Based on Cholesky Compression strategy;submodular Maximization with Bounded Marginal Values;the Prize-Collecting k-steiner Tree Problem.

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Graceful performance Degradation in Apache storm 21st

Graceful Performance Degradation in Apache Storm

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21st international Conference on Parallel and Distributed computing, Applications, and Technologies, PDCAT 2020

作者： HoseinyFarahabady, Mohammad Reza Taheri, Javid Zomaya, Albert Y. Tari, Zahir School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Science RMIT University MelbourneVIC Australia

ISBN: (纸本)9783030692438

The concept of stream data processing is becoming challenging in most business sectors where try to improve their operational efficiency by deriving valuable information from unstructured, yet, contentiously generated high volume raw data in an expected time spans. A modern streamlined data processing platform is required to execute analytical pipelines over a continues flow of data-items that might arrive in a high rate. In most cases, the platform is also expected to dynamically adapt to dynamic characteristics of the incoming traffic rates and the ever-changing condition of underlying computational resources while fulfill the tight latency constraints imposed by the end-users. Apache storm has emerged as an important open source technology for performing stream processing with very tight latency constraints over a cluster of computing nodes. To increase the overall resource utilization, however, the service provider might be tempted to use a consolidation strategy to pack as many applications as possible in a (cloud-centric) cluster with limited number of working nodes. However, collocated applications can negatively compete with each other, for obtaining the resource capacity in a shared platform that, in turn, the result may lead to a severe performance degradation among all running applications. The main objective of this work is to develop an elastic solution in a modern stream processing ecosystem, for addressing the shared resource contention problem among collocated applications. We propose a mechanism, based on design principles of Model Predictive Control theory, for coping with the extreme conditions in which the collocated analytical applications have different quality of service (QoS) levels while the shared-resource interference is considered as a key performance limiting parameter. Experimental results confirm that the proposed controller can successfully enhance the p -99 latency of high priority applications by 67%, compared to the default round r

关键词： storms

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