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A Precision Mechanism for Simulations of Real- Time Multicore Energy-Aware Scheduling

A Precision Mechanism for Simulations of Real- Time Multicor...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Jiangyue Zhan Bing Guo Department of Software Engineering Sichuan University Chengdu China

In parallel and green computing, real-time multicore energy-aware scheduling is used to reduce the energy consumption of multicore processors. To evaluate such scheduling algorithms, theoretical simulation is a great approach in the early designing stage of the algorithm. However, numeral precision errors of the simulation progress can lead to unexpected results. this paper presents a precision mechanism for the temporal and energy-cost simulation of real-time multicore energy-aware scheduling algorithms, to limit the precision error of simulations. It replaces IEEE 754 Double with fixed-point, truncates time values and up-rounds speed values to their least significant bit. the mechanism was evaluated with a partitioned per-core DVFS algorithm and a partitioned per-task DVFS algorithm. the results indicate that the proposed mechanism is able to control and estimate the precision errors of the simulation progress.

关键词： Energy consumption Program processors Multicore processing Scheduling algorithms Smart cities Computational modeling Green computing

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Multi-View Attention Networks with Contrastive Predictive Coding for Sequential Recommendation

Multi-View Attention Networks with Contrastive Predictive Co...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Hongjun An Jinghua Zhu Heran Xi Shiyang Zhou School of Computer Science and Technology Heilongjiang University Harbin China

Attention-based models have achieved outstanding performance in sequential recommendation due to their ability to obtain better sequential representations. However, these models still suffer from over-fitting and over-parameterization issues. Regularization techniques such as dropout can alleviate the over-fitting problem, but an excessively high dropout rate means a greater probability of missing sequential critical relationships. this will lead to incomplete user representations and sub-optimal model performance. In addition, the single embedding of timestamps also leads to incomplete user representation. therefore, we introduce a Multi-view Attention Network with Contrastive Predictive Coding (MVACPC) to solve the above problems in this paper. Specifically, we design a multi-view attention network to alleviate the problem of dropouts losing critical information in the sequence, which can obtain more comprehensive user representations. the network is based on the idea of parallel computing and parameter sharing without adding too much training time and parameters. In order to make the model learn more temporal information, We use different temporal encoding methods for multiple positive sample sequences obtained through data augmentation. Finally, we devise an extended contrastive predictive coding loss to optimize the model. We validate the effectiveness of the MVACPC on public datasets, and the experimental results show that MVACPC outperforms various state-of-the-art sequential recommendation models on different evaluation metrics.

关键词： Training Measurement Costs Computational modeling Predictive coding Predictive models parallel processing

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Big Data-aided Study of the Physical Process of Volume Ignition 6

Big Data-aided Study of the Physical Process of Volume Ignit...

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2022 6th international conference on Mechanics, mathematics and applied Physics, ICMMAP 2022

作者： Liu, Pan Zhou, Yinzhao Xiong, Wenbin Liu, Chencheng Liu, Jie Liu, Gongjie Tang, Zhangchun Gao, Qiang Tianfu Innovation Energy Establishment Chengdu610000 China Nuclear and Radiation Safety Center MEE Beijing100000 China Central South University of Forestry and Technology Changsha410000 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Beijng PARATERA Technology Corp. Ltd Beijing100000 China University of Electronic Science and Technology of China Chengdu610000 China

Volume ignition is a method of igniting a fuel as a whole by simultaneously achieving ignition conditions throughout the fuel zone. the basic criterion for ignition is that the thermonuclear energy is greater than the energy leakage at the fuel boundary, resulting in self-sustaining heating and deep combustion. Deuterium-tritium fuels are wrapped in medium to high Z media to reduce radiative leakage and achieve lower-temperature holistic ignition and non-equilibrium combustion, ultimately allowing the fuel to achieve high combustion efficiency. Volume ignition is the use of energy balance relations under the local thermodynamic equilibrium approximation to establish the energy balance equation for thermonuclear systems, and the system ignition threshold is obtained by solving this equation. By understanding the physical process, we believe that the non-equilibrium process is universal to the volume ignition process. Changes in external factors (density, boundaries, albedo, etc.) at the moment of ignition can have a significant impact on the development direction of the system, and an ignition system with a large surface density can nevertheless withstand a large amount of reverse work and continue to burn. the design of the ignition target tries to avoid these factors through margin design, but conversely, the rational use of these laws can further improve the design margin of the capsule. With the aid of big data, the volume ignition method is easier to calculate and has a shorter iteration time. the traditional way is to propose a model, set the material, and then perform the calculation while using big data can set any model and material for calculation. In this paper, a simple comparison will be made to find out that the efficiency of the physical design of a volume ignition target will be effectively improved with the aid of big data. Volume ignition targets can be used not only in Z-pinch-driven systems but also for laser-driven volume ignition. © Published un

关键词： Ignition

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Research on Elastic Extension of Multi Type Resources for OpenMP Program

Research on Elastic Extension of Multi Type Resources for Op...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Junfeng Zhao Xuesong Gao Yuancong Li College of Computer Science Inner Mongolia University Hohhot China

OpenMP is one of the most popular programming models in the field of parallel computing, which is widely used in multi-threaded programming with shared memory. But with the continuous change of program computing scale, the problem of resource supply has become increasingly prominent. Making use of the cloud computing to dynamically provide resources for OpenMP program is a meaningful work. Based on the internal structure characteristics of OpenMP program, a flexible elastic resource allocation strategy is designed, and a load prediction method combining active and reactive prediction based on machine learning is proposed to realize the load prediction of OpenMP program. then, the fuzzy controller is used to coordinate the resources and infer the CPU and memory resources needed by the program in the next cycle. On this basis, the middleware OPEC is designed and implemented to realize the dynamic allocation of cloud resources during the operation of OpenMP program. Parsec, a benchmark test suite based on shared memory, is selected for experimental verification. the experimental results show that the solution proposed in this paper can dynamically provide a variety of resources for OpenMP program to meet program running requirements, effectively improve resource utilization efficiency and reduce program running costs.

关键词： Fuzzy logic Cloud computing Fuzzy control Machine learning Prediction methods Programming parallel processing

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Revolutionizing Business Intelligence with AI Insights and Strategies

Revolutionizing Business Intelligence with AI Insights and S...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Uzma Sarwar Narinder Kumar Bhasin Dibyahash Bordoloi Sunil Kadyan S. Kezia S Muthuperumal School of Education Huanggang Normal University Huanggang PR China Indian Institute of Banking and Finance India Department of CSE Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India School of Management and Commerce Manav Rachna University Faridabad Haryana India Dept of EEE Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Mathematics K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Artificial Intelligence (AI) in Business Intelligence (BI) is a significant milestone in the way BI is practiced today as it seeks to bring about change in the ways organizations use data to make decisions in their operations. It cast light on the facet of applying advanced machine learning models as well as natural language processing techniques to highly BI operations in this research work. In light of this, this research will spotlight on how NLP and XGBoost, a steep gradient boosting algorithm both work hand in hand in enhancing better prediction and insights from larger data sets. the research uses the NLP tools, such as word embedding and transformer to work with unstructured text data from different sources of businesses. Since NLP eludes useful patterns and trends from textual data, it works synergistically with XGBoost in matters concerning prediction models. XGBoost is used for its ability of high precise in analyze structured data for better prediction results and finding important features that influence business KPIs. Focusing on the qualitative and quantitative analysis of the case and using the NLP and XGBoost, the authors illustrate how the methods increase the accuracy of business forecast, improve the decision-making process, and provide a competitive advantage on the market. Moreover, the study makes efforts to include and elaborate the implementation issue, the data quality and integration issues as well as the possible ways of handling those problems. combining NLP techniques with XGBoost to enhance business intelligence by extracting actionable insights from both structured and unstructured data. It provides optimal decision making by feature engineering, hyperparameters tuning as well as continual learning. this study hence enhances the field of AI BI by revealing new applications and outcomes, which are helpful and informative for the practitioners and academics in the field.

关键词： Statistical analysis Decision making Organizations Predictive models Transformers Prediction algorithms Market research Natural language processing Business intelligence Tuning

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Accelerating small matrix multiplications by adaptive batching strategy on GPU

Accelerating small matrix multiplications by adaptive batchi...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Yaqing Zhang Yaobin Wang Zhangbin Mo Yong Zhou Tao Sun Guang Xu Chaojun Xing Liang Yang School of Computer Science and Technology Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education Southwest University of Science and Technology Mianyang China Huawei Technologies Co.Ltd Shenzhen China Loongson Technology Corporation Limited Peking China

General matrix multiplication (GEMM) is a key operator in a wide range of fields such as machine learning, scientific computing, and signal processing. In practice, the matrix sizes are usually too small to make full use of GPU resources in many applications. To this end, previous work has attempted to batch small GEMMs by designing a CUDA core to process multiple GEMMs simultaneously. It divides the GEMM into multiple tiles, and then process a tile in a thread block. However, the approach only processes one tile per thread block and allocates the number of thread blocks based on the size of the largest matrix in the batch. It will make some thread blocks and threads idle. In this paper, we propose a batching strategy to batch small GEMMs with the consideration of several factors, including tile number, block number, and block size. We process multiple tiles in a thread block. Different tiles may need different block size and block number, we select the optimal scheme adaptively according to the hardware resources occupancy. Our proposed strategy achieves the performance improvement of batched GEMM by improving GPU occupancy. the experimental results show that our batching strategy can achieve about 1.7X speedup on average over the MAGMA and about 1.2X performance improvement on average over the state-of-the-art L-gemm.

关键词： Chemistry Scientific computing Graphics processing units Machine learning Signal processing parallel processing Hardware

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PC-BNA: parallel Convolution Binary Neural Network Accelerator

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Haihui Liu Yuang Zhang Xiangwei Zheng Mengxin Yu Baoxu An Xiaojie Liu Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan China

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. the input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. the experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. the image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. the results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications.

关键词： Performance evaluation Convolution Neural networks Digital signal processors Speech recognition Logic gates Mobile handsets

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Direct GPU Compilation and Execution for Host Applications with OpenMP parallelism 8

Direct GPU Compilation and Execution for Host Applications w...

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8th IEEE/ACM Annual Workshop on the LLVM Compiler Infrastructure in HPC, LLVM-HPC 2022

作者： Tian, Shilei Huber, Joseph Parasyris, Konstantinos Chapman, Barbara Doerfert, Johannes Stony Brook University Department of Computer Science Stony Brook United States Advanced Micro Devices Austin United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing Livermore United States

ISBN: (纸本)9781665475587

Currently, offloading to accelerators requires users to identify which regions are to be executed on the device, what memory needs to be transferred, and how synchronization is to be resolved. On top of these manual tasks, many standard (C/C++ library) functions, such as file I/O or memory manipulation, cannot be directly executed on the device and need to be worked around by the user explicitly. this makes it challenging to port programs in the first place and hinders developers from testing features on the GPU and within the GPU compilation pipeline. Existing tests and test suites for the host are effectively unusable for accelerators and need to be manually ported to provide the same benefits for the devices as they do on the host. In this paper, we propose a direct GPU compilation scheme that leverages the portable target offloading interface provided by LLVM/OpenMP. Utilizing this infrastructure allows us to compile an existing host application for the GPU and execute it there with only a minimal wrapper layer for the user code, command line arguments, and a compiler provided GPU implementation of C/C++ standard library functions. the C/C++ library functions are partially implemented for direct device execution and otherwise fallback to remote procedure call (RPC) to call host functions transparently. Our proposed prototype will allow users to quickly compile for, and test on, the GPU without explicitly handling kernel launches, data mapping, or host-device synchronization. We evaluate our implementation using three proxy applications with host OpenMP parallelism and three microbenchmarks to test the correctness of our prototype GPU compilation. © 2022 IEEE.

关键词： Graphics processing unit

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Investigating the differences between atrial fibrillation and normal ECG signals using the EMD method

Investigating the differences between atrial fibrillation an...

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international conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Ali Ekhlasi Hessam Ahmadi Department of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran

ISBN: (纸本)9781665476249

this paper presents the application of empirical mode decomposition (EMD) for analyzing electrocardiograph (ECG) signals and detecting Atrial Fibrillation (AF) cases. EMD generates a limited and small number of Intrinsic Mode Functions (IMFs). the decomposition is based on the direct extraction of components related to various intrinsic time scales. In this paper, analytic functions corresponding to IMFs are obtained using the Hilbert transform. Using a Central Tendency Measure (CTM), the radius of a circle in which 95% of analytic function points are located within the circle is determined. ECG signals like other biological signals are generally non-stationary. Although traditional processing tools such as short-time Fourier or wavelet transforms are applied for studying biological signals, they are unable to probe completely the ECG signals. EMD is used as a proper technique for analyzing and decomposing non-stationary data. the area of this circle in the complex plane has been used as a feature in order to categorize normal ECG signals from the AF ECG signals. Our results indicate that the area criterion of IMFs in the complex plane in the normal cases are greater than those of the AF cases. In this way, the proposed method can be effective in differentiating the ECG signals of normal subjects and cases with AF.

关键词： Wavelet transforms Empirical mode decomposition Atrial fibrillation Electrocardiography Signal processing Biology Probes

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FlexSFC: Flexible Resource Allocation and VNF parallelism for Improved SFC Placement

FlexSFC: Flexible Resource Allocation and VNF Parallelism fo...

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IEEE conference on Network Softwarization (NetSoft)

作者： Sagar Agarwal Venkatarami Reddy Chintapalli Bheemarjuna Reddy Tamma Indian Institute of Technology Hyderabad India

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

To reduce the processing delay from the sequentially running virtual network functions (VNFs) in a service function chain (SFC), network function parallelism (NFP) is introduced that allows VNFs of the SFC to run in parallel. Existing NFP solutions only focused on improving parallelism benefits without paying much attention to resource utilization while deploying VNFs of SFCs. We take advantage of resource-delay dependency to propose a flexible and efficient parallelized SFC placement mechanism called FlexSFC which determines the optimal SFC placement while reducing resource usage and meeting end-to-end delay guarantees of the SFCs deployed. Initial results show that FlexSFC guarantees the end-to-end delay requirement with better resource utilization and SFC acceptance rate than the state-of-the-art approaches.

关键词： Service function chaining conferences parallel processing Dynamic scheduling Delays Resource management

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