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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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Enriching Big Data Intrusion Detection and Service Through Mapping and parallel Computation

Enriching Big Data Intrusion Detection and Service Through M...

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international conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Roy, Koyel Deshmukh, Rushali JSPM Rajarshi Shahu Coll Engn Pune Maharashtra India

ISBN: (纸本)9789819984756;9789819984763

Large-scale analysis of data requires a significant amount of computing power and resources, so the vast and flexible interpreting, preservation, and remote servers that cloud computing companies provide are very attractive. MongoDB is a widely used database management system for big data that is known for its fast performance and ability to execute activities within the MapReduce framework. The notion of big data relies on the conceptualization of resource collaboration, where applications and related infrastructure are frequently distributed among multiple users or organizations through isolation techniques such as virtualization. Big data is vulnerable to various types of assaults owing to its collaborative assets and extensive system software. The lack of efficacy and utility in improving the efficiency and privacy of big data is a significant challenge. The mission of securing big data is of utmost importance, as any discrepancies in this regard can have significant detrimental effects. The complex and voluminous characteristics of big data pose significant challenges in the application of traditional security methodologies, thereby leading to potential issues. Therefore, this research approach utilizes effective big data query classification and partitioning along with mapping and parallel computation for improving efficiency, as well as a bilinear pairing, avalanche effect, and tamper detection for forensic analysis and report generation. The approach has been effectively tested and compared with conventional approaches to achieve improved outcomes.

关键词： Data mapping Avalanche effect Bilinear pairing parallel computation Big data

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Hermes: Memory-Efficient Pipeline Inference for Large Models on Edge Devices 42

Hermes: Memory-Efficient Pipeline Inference for Large Models...

引用

42nd international conference on Computer Design

作者： Han, Xueyuan Cai, Zinuo Zhang, Yichu Fan, Chongxin Liu, Junhan Ma, Ruhui Buyya, Rajkumar Shanghai Jiao Tong Univ ParisTech Elite Inst Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai Peoples R China Shanghai Aerosp Syst Engn Inst Shanghai Peoples R China Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Melbourne Vic Australia

ISBN: (纸本)9798350380415;9798350380408

The application of Transformer-based large models has achieved numerous success in recent years. However, the exponential growth in the parameters of large models introduces formidable memory challenge for edge deployment. Prior works to address this challenge mainly focus on optimizing the model structure and adopting memory swapping methods. However, the former reduces the inference accuracy, and the latter raises the inference latency. This paper introduces PIPELOAD, a novel memory-efficient pipeline execution mechanism. It reduces memory usage by incorporating dynamic memory management and minimizes inference latency by employing parallel model loading. Based on PIPELOAD mechanism, we present Hermes, a framework optimized for large model inference on edge devices. We evaluate Hermes on Transformer-based models of different sizes. Our experiments illustrate that Hermes achieves up to 4.24x increase in inference speed and 86.7% lower memory consumption than the state-of-the-art pipeline mechanism for BERT and ViT models, 2.58x increase in inference speed and 90.3% lower memory consumption for GPT-style models.

关键词： Edge computing Memory optimisation Large model inference Pipeline execution.

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A Float-Point Arithmetic Logic Unit for High Performance RISC-V Processor 6

A Float-Point Arithmetic Logic Unit for High Performance RIS...

引用

6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Wei, Chunyuan Yuan, Chuan Qi, Yu Xu, Bangjian Niu, Xin College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical and Information Engineering Hunan University Changsha China College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Laboratory Changsha China

ISBN: (纸本)9798331541750

With the rapid advancement of artificial intelligence, chips have become increasingly important. The emerging RISC-V instruction set gradually provides powerful computing support for this field. In this context, along with the computing requirements of deep learning, this paper presents the design of a high-performance floating-point arithmetic logic unit (FALU) that facilitates calculations with double-precision, single-precision, half-precision, and Bfloat16 precision data. This design is based on a single-channel algorithm with merged rounding. It improves and implements a composite adder that combines high and low bits. It also proposes a tree-like floating-point comparator based on the Kogge-Stone parallel prefix network. To ensure that the FALU components meet performance requirements, we undergo functional verification in the Vivado simulation environment. Operating at 1.47GHz under the 28nm CMOS process, the components achieve the predetermined performance indicators. © 2024 IEEE.

关键词： Computer circuits

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Towards Accelerating k-NN with MPI and Near-Memory Processing

Towards Accelerating k-NN with MPI and Near-Memory Processin...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Ahn, Hooyoung Kim, Seonyoung Park, Yoomi Han, Woojong Contini, Nick Ramesh, Bharath Abduljabbar, Mustafa Panda, Dhabaleswar K. Elect & Telecommun Res Inst Supercomp Technol Res Ctr Daejeon South Korea Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9798350364613;9798350364606

Message Passing Interface (MPI) is a common parallel programming model in High-Performance computing fields. Recently, it has been widely used in Artificial Intelligence (Al) applications. However, the performance of those applications is limited by the memory wall problem, which is the performance gap between the processor and memory. To address these problems, we propose a novel computing architecture for accelerating k-NN query, which is the key operation of AI applications. The proposed computing architecture involves two or more computing nodes within a rack using the memory of an NMP device as a communication buffer and the accelerator of the NMP device for MPI collective communication. The superiority of the proposed method is that it reduces data copying during local computation and eliminates the network cost of gathering local computation results for global computation. Furthermore, it can enhance performance by processing data in the device's memory through near-memory processing. However, since these NMP devices are still in development, with no commercially available products currently, we have undertaken the task of developing them ourselves. Despite these circumstances, to demonstrate the feasibility of our proposed approach, we have implemented it in a single-node environment without making any unreasonable assumptions and have compared it with conventional approaches. Although we are using a single-node environment, we have designed a more sophisticated architecture, analyzed the time complexity, and compared the estimated performance of both the conventional and proposed approaches. We can conclude that the proposed computing architecture is feasible, and we anticipate an even more performance enhancement in a multi-node environment.

关键词： k-Nearest Neighbor Message Passing Interface Near-Memory Processing Heterogeneous computing

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Optimization of Federated Learning Algorithm for Non-lID Data: Improvements to the FedDyn Algorithm

Optimization of Federated Learning Algorithm for Non-lID Dat...

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2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Bai, Wenrui International School Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798331511296

This paper investigates the optimization of the FedDyn algorithm in Federated Learning (FL). Federated Learning is a distributed machine learning framework that enables model training on local data across multiple devices while ensuring data privacy. However, the existing FedDyn algorithm, while performing well in handling non-independent and identically distributed (non-IID) data and partial client participation, still faces challenges in large-scale scenarios, such as slow convergence, low training efficiency, and high memory overhead. To address these challenges, this paper systematically optimizes the FedDyn algorithm and proposes several improvements. First, a pre-trained model initialization is introduced, where the global model is pre-trained on a public dataset and then distributed to clients, accelerating early-stage convergence. Second, parallel processing and multi-threading techniques are employed to enhance local training efficiency, and mixed-precision training is used to reduce memory usage and accelerate computation. Additionally, a data volume-weighted hierarchical aggregation method is proposed, ensuring that clients with larger datasets have a greater influence on global model updates, thus improving the model's generalization capability and stability. The effectiveness of the optimized algorithm is validated through experiments on the CIFAR-10 and CIFAR-100 datasets. Compared to FedAvg, SCAFFOLD, and the original FedDyn, the optimized FedDyn shows significant improvements in test accuracy, convergence speed, and computational efficiency, particularly in handling highly heterogeneous non-IID data. Future work could explore more advanced quantization methods, such as low-bit quantization, to further reduce computational resource consumption, and incorporate techniques like differential privacy to enhance the application in privacy-sensitive domains. © 2024 IEEE.

关键词： Federated learning

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Decentralized Federated Learning with Data Feature Transmission and Neighbor Selection 28

Decentralized Federated Learning with Data Feature Transmiss...

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IEEE 28th international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Lou, Wenxiao Xu, Yang Xu, Hongli Liao, Yunming Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou Peoples R China

ISBN: (纸本)9781665473156

In edge computing (EC), federated learning (FL) has been widely used to cooperatively train machine learning models and protect privacy over the traditional approaches. In order to avoid the possible communication bottleneck and single point failure in parameter server (PS) framework, we mainly focus on decentralized federated learning (DFL) in which each worker disseminates information through peer-to-peer (P2P) communication network. However, the high communication cost and the non-IID local data hinder the efficient training of DFL. In this paper, we propose a mechanism (termed DFL-DF) to achieve efficient communication and improve the performance on non-IID data. Specifically, each worker will exchange data features with neighbors rather than models (or gradients). Different from the traditional fixed topology methods, each worker will communicate with its neighbors selected at each round. Extensive experiment results show the high effectiveness of DFL-DF. Specifically, DFL-DF can reduce communication cost by 29.4%-82.06% and improve test accuracy 2.5%-9.1% under communication cost constraints, compared with the traditional model exchanging methods.

关键词： Edge computing Neighbor Selection Decentralized Federated Learning Data Feature Transmission

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Research on Information Dissemination and Digital Art 26

Research on Information Dissemination and Digital Art

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26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Winter 2023

作者： Song, Jinbao Sun, Xiangyu He, Yu Li, Ran Communication University of China State Key Laboratory of Media Convergence and Communication Beijing China China Radio and Tv Co. for International Techno-Economic Cooperation Beijing China

ISBN: (纸本)9798350345865

With the acceleration of technological progress and industrial transformation, the new generation of information technology represented by human intelligence, big data and other technologies is the cutting-edge technology that first penetrates into various areas of economic and social life. At the same time, combining digital information technology to deepen media integration and enhancing the effectiveness and influence of international communication has also become an important research and development direction in the field of media integration. In view of the fact that the current media education practice platform still needs to be developed, this paper proposes a construction method of the form and structure of the education practice platform based on the new trend of media integration, and divides the overall construction of the education practice platform into three parts: information dissemination, digital art and technical support. Aspects, and on this basis, an evaluation index system for digital art platforms is proposed. With cloud computing and big data as the core, it helps the development of metaverse, virtual image and other related digital art fields, controls the quality of the media education practice platform, and improves the international communication ability. © 2023 IEEE.

关键词： Integration

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MPI-RICAL: Data-Driven MPI distributed parallelism Assistance with Transformers

MPI-RICAL: Data-Driven MPI Distributed Parallelism Assistanc...

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2023 international conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Schneider, Nadav Kadosh, Tal Hasabnis, Niranjan Mattson, Timothy Pinter, Yuval Oren, Gal Ben-Gurion University Iaec Israel Intel Labs United States Ben-Gurion University Israel Technion Iaec Israel

ISBN: (纸本)9798400707858

Computational science has made rapid progress in recent years, leading to ever increasing demand for supercomputing resources. For scientific applications that leverage such resources, Message Passing Interface (MPI) plays a crucial role in enabling distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, performing domain decomposition, is a challenging and error-prone task. In this paper, we address this problem by developing MPI-rical, a novel data-driven, programming-assistance tool that assists programmers in writing domain decomposition based distributed memory parallelization code using MPI. Specifically, we leverage Transformer architecture - the invention that led to advancements in the field of natural language processing (NLP) - with a supervised language model to suggest MPI functions and their proper locations in the code on the fly. In addition to the novel model for MPI-based parallel programming, in this paper, we also introduce MPICodeCorpus, the first publicly-available corpus of MPI-based parallel programs that is created by mining more than 15,000 open-source repositories on GitHub. Experimental results demonstrate the effectiveness of MPI-rical on both dataset from MPICodeCorpus and more importantly, on a compiled benchmark of MPI-based parallel programs for numerical computations that represent real-world scientific applications. Specifically, MPI-rical achieves F1 scores between 0.87-0.91 on these programs, demonstrating its accuracy in suggesting correct MPI functions at appropriate code locations. The source code used in this work, as well as other relevant sources, are available at: https://***/Scientific-computing-Lab-NRCN/MPI-rical. © 2023 ACM.

关键词： Message passing

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parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively parallel Simulation 40

Parallel Q-Learning: Scaling Off-policy Reinforcement Learni...

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40th international conference on Machine Learning

作者： Li, Zechu Chen, Tao Hong, Zhang-Wei Ajay, Anurag Agrawal, Pulkit MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

Reinforcement learning is time-consuming for complex tasks due to the need for large amounts of training data. Recent advances in GPU-based simulation, such as Isaac Gym, have sped up data collection thousands of times on a commodity GPU. Most prior works have used onpolicy methods like PPO due to their simplicity and easy-to-scale nature. Off-policy methods are more sample-efficient, but challenging to scale, resulting in a longer wall-clock training time. This paper presents a novel parallel Q-Learning (PQL) scheme that outperforms PPO in terms of wall-clock time and maintains superior sample efficiency. The driving force lies in the parallelization of data collection, policy function learning, and value function learning. Different from prior works on distributed off-policy learning, such as Apex, our scheme is designed specifically for massively parallel GPU-based simulation and optimized to work on a single workstation. In experiments, we demonstrate the capability of scaling up Q-learning methods to tens of thousands of parallel environments and investigate important factors that can affect learning speed, including the number of parallel environments, exploration strategies, batch size, GPU models, etc. The code is available at https://***/Improbable-AI/pql.

关键词： Reinforcement learning

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Investigation on the parallel computing Technique Using on the Numerical Simulation of Carbon Free Fuel 4

Investigation on the Parallel Computing Technique Using on t...

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4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Wang, Weibin Wei, Xutao Xi'an Fanyi University Department of Computer Science Xi'an China Xi'an Jiaotong University State Key Laboratory of Multiphase Flow in Power Engineering Xi'an China

ISBN: (纸本)9798350337181

The numerical simulation of the turbulent combustion is identified as one of the most promising methods on the designs of the combustion devices, for example: the internal combustion engine and gas turbine. This mainly depends on the development of parallel computing technique. However, the computational efficiency for different cases on different compute nodes and number of cores still needs to be studied. On the other hand, due to the requirements from the aim of peaking carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060, modern energy system is requesting low carbon or carbon free fuels, such as ammonia (NH3). Therefore, the simulations of ammonia/air flame were performed in present investigation. The comparison between simulation cases on different compute nodes and number of cores illustrates that different cases requires different number of cores to achieve the best computational efficiency. The computational efficiency of each compute nodes are dissimilar, which can decrease the efficiency. Meanwhile, the methods in present study can predict the ammonia/air flames. The initial condition of velocity fluctuation is necessary for simulating turbulent flame. The wrinkles of the flames can be well predicted by the velocity fluctuation initial boundary condition. © 2023 IEEE.

关键词： Computational efficiency

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