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Federated Feature Concatenate Method for heterogeneous computing in Federated Learning

计算机、材料和连续体（英文） 2023年第4期75卷 351-371页

作者： Wu-Chun Chung Yung-Chin Chang Ching-Hsien Hsu Chih-Hung Chang Che-Lun Hung Department of Information and Computer Engineering Chung Yuan Christian UniversityTaoyuanTaiwan Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichungTaiwan Department of Computer Science&Communication Engineering Providence UniversityTaichungTaiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung UniversityTaipeiTaiwan

Federated learning is an emerging machine learning techniquethat enables clients to collaboratively train a deep learning model withoutuploading raw data to the aggregation server. Each client may be equippedwith different computing resources for model training. The client equippedwith a lower computing capability requires more time for model training,resulting in a prolonged training time in federated learning. Moreover, it mayfail to train the entire model because of the out-of-memory issue. This studyaims to tackle these problems and propose the federated feature concatenate(FedFC) method for federated learning considering heterogeneous clients.FedFC leverages the model splitting and feature concatenate for offloadinga portion of the training loads from clients to the aggregation server. Eachclient in FedFC can collaboratively train a model with different cutting layers.Therefore, the specific features learned in the deeper layer of the serversidemodel are more identical for the data class classification. Accordingly,FedFC can reduce the computation loading for the resource-constrainedclient and accelerate the convergence time. The performance effectiveness isverified by considering different dataset scenarios, such as data and classimbalance for the participant clients in the experiments. The performanceimpacts of different cutting layers are evaluated during the model training.The experimental results show that the co-adapted features have a criticalimpact on the adequate classification of the deep learning model. Overall,FedFC not only shortens the convergence time, but also improves the bestaccuracy by up to 5.9% and 14.5% when compared to conventional federatedlearning and splitfed, respectively. In conclusion, the proposed approach isfeasible and effective for heterogeneous clients in federated learning.

关键词： Federated learning deep learning artificial intelligence heterogeneous computing

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Time Predictable Modeling Method for GPU Architecture with SIMT and Cache Miss Awareness

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Journal of Electronic Research and Application 2024年第2期8卷 109-115页

作者： Shaojie Zhang School of Software Engineering University of Science and Technology of China Hefei 230026China

Graphics Processing Units(GPUs)are used to accelerate computing-intensive tasks,such as neural networks,data analysis,high-performance computing,etc.In the past decade or so,researchers have done a lot of work on GPU architecture and proposed a variety of theories and methods to study the microarchitectural characteristics of various GPUs.In this study,the GPU serves as a co-processor and works together with the CPU in an embedded real-time system to handle computationally intensive tasks.It models the architecture of the GPU and further considers it based on some excellent work.The SIMT mechanism and Cache-miss situation provide a more detailed analysis of the GPU architecture.In order to verify the GPU architecture model proposed in this article,10 GPU kernel_task and an Nvidia GPU device were used to perform experiments.The experimental results showed that the minimum error between the kernel task execution time predicted by the GPU architecture model proposed in this article and the actual measured kernel task execution time was 3.80%,and the maximum error was 8.30%.

关键词： heterogeneous computing GPU Architecture modeling Time predictability

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A Scheduling Heuristic for Large-Scale heterogeneous computing Environments

A Scheduling Heuristic for Large-Scale Heterogeneous Computi...

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2007年国际电子商务、工程及科学领域的分布式计算和应...

作者： Xiao Li Du1 2 Chang Jun Jiang2 3 Fei Yin1 2 1Electronics and Information Engineering School Tongji University Shanghai 201804 China 2Tonji Branch National Engineering &Technology Center of High Performance Computer Shanghai 201804 China 3Key Laboratory of Computer System and Architecture Institute of computing Technology Chinese Academy of Sciences Beijing 100080 China

A group of features is defined to describe the synthetic performance of processing cells and distinguish their heterogeneousness in large-scale heterogeneous computing environments. Three notable features that can effectively partition the processing cells of target system with fuzzy clustering are verified through several groups of experiments. Based on fuzzy clustering results of the target system, a Scheduling heuristic algorithm is presented. In the scheduling stage, the cluster with better synthetic performance will be chose first. The algorithm greatly reduces the time spent on the processor selection for large-scale system. The priority designation of tasks takes the influence of critical path nodes and heterogeneous resource into consideration. Experimental results show that it performs very well compared with other algorithm.

关键词： Task Scheduling heterogeneous computing Direct Acyclic Graph

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Research on DSP-GPU heterogeneous computing System

Research on DSP-GPU Heterogeneous Computing System

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2012 National Conference on Information Technology and Computer Science

作者： Li Xiangzhen Jiangsu Automation Research Institute of CSIC,Lianyungang,China Hu Jingying Jiangsu Automation Research Institute of CSIC,Lianyungang,China Lv Weiguo Jiangsu Automation Research Institute of CSIC,Lianyungang,China Wang Guiqiang Jiangsu Automation Research Institute of CSIC,Lianyungang,China Cai Xinrong Jiangsu Automation Research Institute of CSIC,Lianyungang,China

In this paper,DSP-GPU heterogeneous computing system is studied and the system architecture is designed.The task scheduling model is analyzed and the discrete particle swarm optimization algorithm is used for the DSP-GPU heterogeneous computing.The communication framework between DSP and GPU is designed for the heterogeneous computing.

关键词： DSP GPU heterogeneous computing Task Scheduling

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Resource scheduling techniques in cloud from a view of coordination:a holistic survey

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Frontiers of Information Technology & Electronic Engineering 2023年第1期24卷 1-40页

作者： Yuzhao WANG Junqing YU Zhibin YU School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Center of heterogeneous Intelligent Computer Architecture and Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nowadays,the management of resource contention in shared cloud remains a pending problem.The evolution and deployment of new application paradigms(e.g.,deep learning training and microservices)and custom hardware(e.g.,graphics processing unit(GPU)and tensor processing unit(TPU))have posed new challenges in resource management system design.Current solutions tend to trade cluster efficiency for guaranteed application performance,e.g.,resource over-allocation,leaving a lot of resources underutilized.Overcoming this dilemma is not easy,because different components across the software stack are involved.Nevertheless,massive efforts have been devoted to seeking effective performance isolation and highly efficient resource scheduling.The goal of this paper is to systematically cover related aspects to deliver the techniques from the coordination perspective,and to identify the corresponding trends they indicate.Briefly,four topics are involved.First,isolation mechanisms deployed at different levels(micro-architecture,system,and virtualization levels)are reviewed,including GPU multitasking methods.Second,resource scheduling techniques within an individual machine and at the cluster level are investigated,respectively.Particularly,GPU scheduling for deep learning applications is described in detail.Third,adaptive resource management including the latest microservice-related research is thoroughly explored.Finally,future research directions are discussed in the light of advanced work.We hope that this review paper will help researchers establish a global view of the landscape of resource management techniques in shared cloud,and see technology trends more clearly.

关键词： Coordination Co-location heterogeneous computing Microservice Resource scheduling techniques

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Programming bare-metal accelerators with heterogeneous threading models:a case study of Matrix-3000

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Frontiers of Information Technology & Electronic Engineering 2023年第4期24卷 509-520页

作者： Jianbin FANG Peng ZHANG Chun HUANG Tao TANG Kai LU Ruibo WANG Zheng WANG College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China School of computing University of LeedsLeeds LS29JTUK

As the hardware industry moves toward using specialized heterogeneous many-core processors to avoid the effects of the power wall,software developers are finding it hard to deal with the complexity of these systems.In this paper,we share our experience of developing a programming model and its supporting compiler and libraries for Matrix-3000,which is designed for next-generation exascale supercomputers but has a complex memory hierarchy and processor organization.To assist its software development,we have developed a software stack from scratch that includes a low-level programming interface and a high-level OpenCL compiler.Our low-level programming model offers native programming support for using the bare-metal accelerators of Matrix-3000,while the high-level model allows programmers to use the OpenCL programming standard.We detail our design choices and highlight the lessons learned from developing system software to enable the programming of bare-metal accelerators.Our programming models have been deployed in the production environment of an exascale prototype system.

关键词： heterogeneous computing Parallel programming models Programmability Compilers Runtime systems

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Resource Scheduling Strategy for Performance Optimization Based on heterogeneous CPU-GPU Platform

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Computers, Materials & Continua 2022年第10期73卷 1621-1635页

作者： Juan Fang Kuan Zhou Mengyuan Zhang Wei Xiang Faculty of Information Technology Beijing University of TechnologyBeijing100124China La Trobe University MelbourneVIC3086Australia James Cook University CainsQLD4878Australia

In recent years,with the development of processor architecture,heterogeneous processors including Center processing unit(CPU)and Graphics processing unit(GPU)have become the mainstream.However,due to the differences of heterogeneous core,the heterogeneous system is now facing many problems that need to be solved.In order to solve these problems,this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies.To improve the performance of the system,this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks.The combination strategy consists of two sub-strategies,the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure.The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems.The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples.Based on this information,an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme,which fully utilizes the characteristics of heterogeneous cores.The experimental results show that the combination strategy reduces task execution time by 29.13%on average.In the case of processing multiple tasks,the multi-task scheduling strategy reduces the execution time by up to 23.38%based on the combined strategy.Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems.

关键词： heterogeneous computing CPU-GPU Performance Workload balance

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THUBrachy:fast Monte Carlo dose calculation tool accelerated by heterogeneous hardware for high-dose-rate brachytherapy

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Nuclear Science and Techniques 2021年第3期32卷 107-119页

作者： An-Kang Hu Rui Qiu Huan Liu Zhen Wu Chun-Yan Li Hui Zhang Jun-Li Li Rui-Jie Yang Department of Engineering Physics Tsinghua UniversityBeijing 100084China Key Laboratory of Particle and Radiation Imaging Tsinghua UniversityMinistry of EducationBeijing 100084China Nuctech Company Limited Beijing 100084China Department of Radiation Oncology Peking University Third HospitalBeijing 100191China

The Monte Carlo(MC)simulation is regarded as the gold standard for dose calculation in brachytherapy,but it consumes a large amount of computing resources.The development of heterogeneous computing makes it possible to substantially accelerate calculations with hardware accelerators.Accordingly,this study develops a fast MC tool,called THUBrachy,which can be accelerated by several types of hardware accelerators.THUBrachy can simulate photons with energy less than 3 MeV and considers all photon interactions in the energy range.It was benchmarked against the American Association of Physicists in Medicine Task Group No.43 Report using a water phantom and validated with Geant4 using a clinical case.A performance test was conducted using the clinical case,showing that a multicore central processing unit,Intel Xeon Phi,and graphics processing unit(GPU)can efficiently accelerate the simulation.GPU-accelerated THUBrachy is the fastest version,which is 200 times faster than the serial version and approximately 500 times faster than Geant4.The proposed tool shows great potential for fast and accurate dose calculations in clinical applications.

关键词： High-dose-rate brachytherapy Monte Carlo heterogeneous computing Hardware accelerators

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computing infrastructure for big data processing

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Frontiers of Computer Science 2013年第2期7卷 165-170页

作者： Ling LIU Distributed Data Intensive Systems Lab School of Computer Science Georgia Institute of TechnologyUSA Atlanta 30332

With computing systems undergone a fundamen- tal transformation from single-processor devices at the turn of the century to the ubiquitous and networked devices and the warehouse-scale computing via the cloud, the parallelism has become ubiquitous at many levels. At micro level, par- allelisms are being explored from the underlying circuits, to pipelining and instruction level parallelism on multi-cores or many cores on a chip as well as in a machine. From macro level, parallelisms are being promoted from multiple ma- chines on a rack, many racks in a data center, to the glob- ally shared infrastructure of the Internet. With the push of big data, we are entering a new era of parallel computing driven by novel and ground breaking research innovation on elas- tic parallelism and scalability. In this paper, we will give an overview of computing infrastructure for big data processing, focusing on architectural, storage and networking challenges of supporting big data paper. We will briefly discuss emerging computing infrastructure and technologies that are promising for improving data parallelism, task parallelism and encour- aging vertical and horizontal computation parallelism.

关键词： big data cloud computing data analytics elas-tic scalability heterogeneous computing GPU PCM bigdata processing

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Fast Parallel Cutoff Pair Interactions for Molecular Dynamics on heterogeneous Systems

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Tsinghua Science and Technology 2012年第3期17卷 265-277页

作者： Qiang Wu Canqun Yang Tao Tang Kai Lu School of Computer Science National University of Defense TechnologyChangsha 410073China

heterogeneous systems with both Central Processing Units （CPUs） and Graphics Processing Units （GPUs） are frequently used to accelerate short-ranged Molecular Dynamics （MD） simulations. The most time-consuming task in short-ranged MD simulations is the computation of particle-to-particle interac- tions. Beyond a certain distance, these interactions decrease to zero. To minimize the operations to investi- gate distance, previous works have tiled interactions by employing the spatial attribute, which increases the memory access and GPU computations, hence decreasing performance. Other studies ignore the spatial attribute and construct an all-versus-all interaction matrix, which has poor scalability. This paper presents an improved algorithm. The algorithm first bins particles into voxels according to the spatial attributes, and then tiles the all-versus-all matrix into voxel-versus-voxel sub-matrixes. Only the sub-matrixes between neighbor- ing voxels are computed on the GPU. Therefore, the algorithm reduces the distance examine operations and limits additional memory access and GPU computations. This paper also adopts a multi-level program- ming model to implement the algorithm on multi-nodes of Tianhe-lA. By employing （1） a patch design to ex- ploit parallelism across the simulation domain, （2） a communication overlapping method to overlap the communications between CPUs and GPUs, and （3） a dynamic workload balancing method to adjust the workloads among compute nodes, the implementation achieves a speedup of 4.16x on one NVIDIA Tesla M2050 GPU compared to a 2.93 GHz six-core Intel Xeon X5670 CPU. In addition, it runs 2.41x faster on 256 compute nodes of Tianhe-lA （with two CPUs and one GPU inside a node） than on 256 GPU-excluded nodes.

关键词： cutoff pair interactions molecular dynamics heterogeneous computing GPU computing

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