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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Rui Li Huaimin Wang Peichang Shi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of Complex & Critica Software Environment College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between *** this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： Training Scheduling algorithms Machine vision Heuristic algorithms Machine learning Feature extraction Remote working

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parallel Implementation of SHA256 on Multizone Heterogeneous Systems

Parallel Implementation of SHA256 on Multizone Heterogeneous...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yongtao Luo Jie Liu Tiaojie Xiao Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing program for Frontier Equipment National University of Defense Technology Changsha China National Supercomputer Center in Tianjin Tianjin China

SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based on CPUs or dedicated hardware, such as ASICs and FPGAs. Consequently, there is a need to explore whether new heterogeneous parallel framework can improve the computational performance of the hash function. To address this issue, we conducted a study on the MT-3000 platform, which is a special architecture processor for the next-generation exascale prototype supercomputer. We proposed MT-SHA256, a heterogeneous multistage parallel implementation for hashing multiple messages on the MT-3000. Combining the architectural features of this processor, we developed an effective solution that significantly improved the computational performance of SHA-256. As a result, MT-SHA256 achieved a maximum throughput of 1045.68 MB/s on a single acceleration core of MT-3000. This is 9.84x higher than the C code implementation on one CPU core of MT-3000. We also performed a scalability test and found that MT-SHA256 achieved a throughput of 98.04 GB/s on a computing node, and extended to 512 nodes (2048 acceleration clusters) on this system with good scalability.

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A Class of Fast and Accurate Multi-layer Block Summation and Dot Product Algorithms 18th

A Class of Fast and Accurate Multi-layer Block Summation a...

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18th IFIP WG 10.3 International Conference on Network and parallel Computing, NPC 2021

作者： He, Kang Barrio, Roberto Chen, Lin Jiang, Hao Liu, Jie Gu, Tongxiang Qi, Jin Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Department of Applied Mathematics University of Zaragoza ZaragozaE50009 Spain College of Computer National University of Defense Technology Changsha410073 China Institute of Applied Physics and Computational Mathematics Beijing100000 China

ISBN: (纸本)9783030935702

Basic recursive summation and common dot product algorithm have a backward error bound that grows linearly with the vector dimension. Blanchard [1] proposed a class of fast and accurate summation and dot product algorithms respectively called FABsum and FABdot, which trades off the calculation accuracy and speed by the block size. Castaldo [2] proposed a multi-layer block summation and dot product algorithm called SuperBlocksum and SuperBlockdot that can increase the accuracy while adding almost no additional calculations. We combine the idea of [1] with the multi-layer block structure to propose SuperFABsum (for "super fast and accurate block summation") and SuperFABdot (for "super fast and accurate block dot product"). Our algorithms have two variants, one is SuperFAB(within), the other is SuperFAB(outside). Our algorithms further improve accuracy and speed compared with FAB and SuperBlock. We conducted accuracy and speed tests on the high-performance FT2000+ processor. Experimental results show that SuperFABdot(within) algorithm is more accurate than FABdot and SuperBlockdot. Compared with FABdot, SuperFABdot(outside) algorithm can achieve up to 1.2 × performance speedup while ensuring similar accuracy. © 2022, IFIP International Federation for Information processing.

关键词： Artificial intelligence

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WBA: A Warping-based Approach to Generating Imperceptible Adversarial Examples

WBA: A Warping-based Approach to Generating Imperceptible Ad...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chengyao Hua Shigeng Zhang Weiping Wang Zhankai Li Jian Zhang School of Computer Science and Engineering Central South University Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL)

The human can easily recognize the incongruous parts of an image, for example, perturbations unrelated to the image itself, but are poor at spotting the small geometric transformations. However, in terms of the robustness of deep neural networks (DNNs), the ability to properly recognize objects with small geometric transformations is still a challenge. In this work, we investigate the problem from the perspective of adversarial attacks: does the performance of DNNs degrade even when small geometric transformations are applied to images? To this end, we propose a novel adversarial attack method, called WBA, a Warping-Based Adversarial attack method, which does not introduce information independent of the original images but manipulates the existing pixels of the images by elastic warping transformations to generate adversarial examples that are imperceptible to the human eye. At the same time, existing adversarial attacks typically generate adversarial examples by modifying pixels in the spatial domain of the image, the addition of such perturbations introduces extra information unrelated to the image itself and is easily detected by the naked eyes. We demonstrate the effectiveness of WBA by extensive experiments on commonly used datasets, including MNIST, CIFAR10, and ImageNet. The results show that WBA can quickly generate adversarial examples with the highest adversarial strength, consumes less time, and can be comparable to optimization-based adversarial attack methods in image perception evaluation metrics such as LPIPS, SSIM, and far more than gradient direction-based iterative methods.

关键词： Measurement Deep learning Privacy Image recognition Perturbation methods Neural networks Robustness

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AUXILIARY-TASKS LEARNING FOR PHYSICS-INFORMED NEURAL NETWORK-BASED PARTIAL DIFFERENTIAL EQUATIONS SOLVING

arXiv

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arXiv 2023年

作者： Yan, Junjun Chen, Xinhai Wang, Zhichao Zhou, Enqiang Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China

Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to capture solution-related features through neural networks. However, original PINNs often suffer from bottlenecks, such as low accuracy and non-convergence, limiting their applicability in complex physical contexts. To alleviate these issues, we proposed auxiliary-task learning-based physics-informed neural networks (ATL-PINNs), which provide four different auxiliary-task learning modes and investigate their performance compared with original PINNs. We also employ the gradient cosine similarity algorithm to integrate auxiliary problem loss with the primary problem loss in ATL-PINNs, which aims to enhance the effectiveness of the auxiliary-task learning modes. To the best of our knowledge, this is the first study to introduce auxiliary-task learning modes in the context of physics-informed learning. We conduct experiments on three PDE problems across different fields and scenarios. Our findings demonstrate that the proposed auxiliary-task learning modes can significantly improve solution accuracy, achieving a maximum performance boost of 96.62% (averaging 28.23%) compared to the original single-task PINNs. The code and dataset are open source at https://***/junjun-yan/ATL-PINN. Copyright © 2023, The Authors. All rights reserved.

关键词： Partial differential equations

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F3A: Fairness-Aware AI-Workloads Allocation Considering Multidimensional User Demands in JointCloud

F3A: Fairness-Aware AI-Workloads Allocation Considering Mult...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jiacheng Yang Guodong Yi Fei Gao Peichang Shi Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users’ demands while ensuring CSPs’ fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users’ multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

关键词： Industries Data centers Cloud computing Job shop scheduling Costs Scheduling algorithms Large language models

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A data-assisted physics-informed neural network(DA-PINN)for fretting fatigue lifetime prediction

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International Journal of Mechanical System Dynamics 2024年第3期4卷 361-373页

作者： Can Wang Qiqi Xiao Zhikun Zhou Yongyu Yang Gregor Kosec Lihua Wang Magd Abdel Wahab Soete Laboratory Department of Electrical EnergyMetalsMechanical Constructions and SystemsFaculty of Engineering and ArchitectureGhent UniversityGentBelgium Department of Engineering Mechanics School of Aerospace EngineeringHuazhong University of Science and TechnologyWuhanChina Key Laboratory for Mechanics Anhui Polytechnic UniversityWuhuChina Parallel and Distributed Systems Laboratory Jožef Stefan InstituteLjubljanaSlovenia School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghaiChina College of Engineering Yuan Ze UniversityTaiwanChina

In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue *** PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting *** propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation *** traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction *** DA-PINN method,employed in this study,consists of two main ***,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other ***,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue *** results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is ***,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.

关键词： crack initiation data-assisted fretting fatigue finite element method physics-informed neural network

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ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations

arXiv

引用

arXiv 2023年

作者： Yan, Junjun Chen, Xinhai Wang, Zhichao Zhoui, Enqiang Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China

Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x. The code of ST-PINN is available at GitHub: https://***/junjun-yan/ST-PINN. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation

arXiv

引用

arXiv 2022年

作者： Tang, Yu Wang, Chenyu Zhang, Yufan Liu, Yuliang Zhang, Xingcheng Qiao, Linbo Lai, Zhiquan Li, Dongsheng Science and Technology on Parallel and Distributed Processing Laboratory United States SenseTime Research Shanghai Artificial Intelligence Laboratory China

Training activations of deep neural networks occupy plenty of GPU memory, especially for large-scale deep neuralnetworks. However, the further development of deep neural networks is hampered by the limited GPU memory resource. Therefore, the optimal utilization of GPU memory resources is highly demanded. Swapping and recomputation are commonly applied to make better use of GPU memory in deep learning. As an emerging domain, several dilemmas remain: 1) The efficiency of recomputation is limited and swapping between GPU and CPU costs severe time delay;2) There still lacks a dynamic runtime memory manager of tensor swapping and tensor recomputation nowadays;3) Manually decisions for activations of training deep neural network require professional priors and experience. To remedy the above issues, we propose a novel memory manager named DELTA (Dynamic tEnsor offLoading and recompuTAtion). To the best of our knowledge, we are the first to propose a reasonable dynamic runtime manager on the combination of tensor swapping and tensor recomputation without user oversight. In DELTA, we firstly propose a filter algorithm to select the optimal tensors to be released out of GPU memory and secondly present a director algorithm to select a proper action for each of these tensors. Furthermore, prefetching and overlapping are deliberately considered to overcome the time cost caused by swapping and recomputing tensors. Experimental results show that DELTA not only saves 40%-70% of GPU memory, surpassing the state-of-the-art method to a great extent, but also gets comparable convergence results as the baseline with acceptable time delay. Also, DELTA gains 2.04× maximum batchsize when training ResNet-50 and 2.25× when training ResNet-101 compared with the baseline. Besides, comparisons between the swapping cost and recomputation cost in our experiments demonstrate the importance of making a reasonable decision on tensor swapping and tensor recomputation, which refutes the arguments in

关键词： Tensors

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DCSA: The Deployment Mechanism of Chained Serverless Applications in JointCloud Environment

DCSA: The Deployment Mechanism of Chained Serverless Applica...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

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