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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

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Dynamic Checkpointing: Fault Tolerance in High-Performance Computing

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

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Communication, Control and Intelligent Systems (CCIS)

作者： Biswajit Bhowmik Tushar Verma Navdeep Dadhania Dineshbhai Manideep Raya VSRS Reddy Girish K K Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Bharat Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Bharat Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical.

关键词： Checkpointing Fault tolerance Program processors High performance computing Fault tolerant systems parallel processing Load management Hardware Intelligent systems Resilience

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Application of LLM techniques for Data Insights in DHP

Application of LLM Techniques for Data Insights in DHP

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Digital Twins and parallel Intelligence (DTPI), IEEE international conference on

作者： Jun Luo Chao Ouyang Yinggang Jing Huajian Fang Yunlong Xiao Qing Zhang Rongjun Li China Nuclear Power Operation Technology Corporation Ltd. Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

With the rapid development of China's nuclear power industry, data insights play an increasingly crucial role in enhancing the safety and operational efficiency of nuclear power plants. Traditional data analysis methods face challenges in handling massive, complex, and heterogeneous industrial data from multiple sources. The emergence of LLM (large language model) offers new solutions for industrial data insights. This article proposes a digital intelligent platform architecture for nuclear power LLM and thoroughly explores its applications in the nuclear power industry. The architecture includes data layer, model layer, analysis layer, and application layer, responsible respectively for data collection, storage, processing, analysis, and application. Finally, based on the architecture proposed in this article, prototypes for applications such as nuclear power industry statement generation and intelligent analysis assistant for patrol inspection data were developed, with a view towards future development directions.

关键词： Data analysis Decision making Data visualization Inspection Data collection Power industry Vectors Data models Visual databases Power generation

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From parallelization to Customization - Challenges and Opportunities

From Parallelization to Customization - Challenges and Oppor...

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35th IEEE international parallel and distributed processing Symposium (IPDPS)

作者： Cong, Jason Univ Calif Los Angeles Comp Sci Dept Ctr Domain Specif Comp CDSC Los Angeles CA 90024 USA

ISBN: (纸本)9781665440660

With large-scale deployment of FPGAs in both private and public clouds in the past a few years, customizable computing is transitioning from advanced research into mainstream computing. In this talk, I shall first showcase a few big data and machine learning applications that benefit significantly from customization. Next, I shall discuss the challenges of FPGA programming for the efficient accelerator designs, which presents a significant barrier to many software programmers, despite the recent advances in high-level synthesis. Then, I shall highlight our recent progress on automated compilation for customized archictectures, such as systolic arrays, stencils, and more general CPPs (composable parallel and pipelined) architectures. I shall also present our ongoing work on HeteroCL, a highly productive multi-paradigm programming framework targeting accelerator-rich heterogeneous architectures, and is being used as a focal point to integrate various optimizaiton techniques and support high-level domain-specific languages (DSL) such as Halide and Pytorch. Our goal is to “demacratize customizable computing” so that most (if not all) software programmers can design optimized accelerators on FPGAs.

关键词： distributed processing Cloud computing Computer architecture Machine learning Programming Big Data Software

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Topology Identification Technology for Low-Voltage distributed Photovoltaic Distribution Network Lines

Topology Identification Technology for Low-Voltage Distribut...

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Energy Engineering and Power Systems (EEPS), international conference on

作者： Guoquan Zheng Enguo Zhu Hailong Zhang Yan Liu Junlong Wang Zhe Liu China Electric Power Research Institute Beijing China State Grid Hebei Electric Power Beijing China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

The large-scale penetration of distributed photovoltaic (PV) power generation systems has brought new challenges to the topology identification and detection of traditional distribution networks. This article mainly studies the topology identification technology (TIT) of distributed PV low-voltage (LV) distribution network lines, aiming to design a topology identification method that can adapt to the dynamic changes of the power grid, have large-scale capacity, and improve system accuracy under the same conditions. At the same time, the data processing speed has been improved. This article first constructs a system model, including node model, edge model, and parameter model. It mathematically represents the topology structure of the power grid using graph theory and designs a topology recognition algorithm based on optimization techniques and state estimation. This algorithm is used to solve the distributed characteristics of power grid topology recognition, the difficulty of data collection, the dynamic diversity of power grid structure, the uncertainty of equipment parameters, the high computational complexity of data processing, and the communication constraints of power grid topology recognition. This algorithm adopts modern programming languages and parallel computing frameworks, making it easy to implement efficiently. The results on the simulation platform show that the highest recall rate for 22 test cases is 93.8%, and the response time for test cases is 425 ms to 980 ms, providing a fast response to the information space of the grid.

关键词： Photovoltaic systems Adaptation models Low voltage Network topology Heuristic algorithms Power system dynamics Distribution networks Power grids Mathematical models Topology

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Application-Attuned Memory Management for Containerized HPC Workflows

Application-Attuned Memory Management for Containerized HPC ...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Moiz Arif Avinash Maurya M. Mustafa Rafique Dimitrios S. Nikolopoulos Ali R. Butt Rochester Institute of Technology Virginia Tech

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

High-Performance Computing (HPC) jobs consist of data and memory-intensive tasks often executed as workflows or ensembles to facilitate efficient and coordinated execution. These workflows are traditionally executed on HPC systems and have unique memory requirements based on the data size, computational complexity, and I/O activity. Recently containerized execution of these workflows has been extensively explored. Containerized workflow execution of HPC jobs requires several terabytes of memory that exceed node capacity, resulting in excessive data swapping to slower storage, degraded job performance, and failures. Similarly, colocated bandwidth-intensive, latency-sensitive, or short-lived workflows suffer from degraded performance due to contention, memory exhaustion, and higher access latency due to suboptimal memory allocation. Recently, tiered memory systems comprising persistent memory and compute express link (CXL) have been explored to provide additional memory capacity and bandwidth to memory-constrained systems and applications. However, current memory allocation and management techniques for tiered memory subsystems are inadequate to meet the diverse needs of colocated containerized jobs in HPC systems that concurrently run workflows and ensembles at scale. This paper leverages tiered memory systems for containerized HPC workflows and proposes efficient memory management policies including intelligent page placement and eviction policies to improve memory access performance. Our page allocation and replacement policies incorporate task characteristics and enable efficient memory sharing between workflows. We integrate our policies with the popular HPC scheduler, SLURM, and container runtime, Singularity, to show that our approach improves tiered memory utilization and application performance and reduces workflow execution times by up to 51%, 87%, and 35% as compared to the ideal, realistic, and optimized tiered execution environments, respectively.

关键词： Concurrent computing distributed processing Runtime Scalability High performance computing Memory management Bandwidth

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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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Computing, Robotics and System Sciences (ICRSS), international conference on

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

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

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.

关键词： Training Quantization (signal) Accuracy Federated learning Computational modeling Memory management Data models Computational efficiency Optimization Convergence

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Enhancing Software-Defined Networks (SDN) Environment to Detect DDoS Attack Using KNN Classifier Algorithm

Enhancing Software-Defined Networks (SDN) Environment to Det...

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Signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), international conference on

作者： Dhanush Pakala Priya L Murali Krishna Vantsala Harshavardhan Raju Konduru Narendran Rajagopalan CSE dept. National Institute of Technology Puducherry Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

A new paradigm in network management called Software-Defined Networking (SDN) offers improved control and operational capabilities over network infrastructures. At the core of an SDN-based network lies the SDN controller, akin to an operating system, tasked with executing diverse network applications and ensuring the smooth operation of network services and functionalities. Because of its design's inherent complexity, SDN presents several security concerns despite its great potential. One prevalent threat is distributed Denial of Service (DDoS) attacks, which are particularly impactful due to the centralized nature of SDN, especially at its control layer, causing network-wide disruptions. Machine learning techniques are being used more to spot risks. By utilizing the NSL-KDD dataset and Linear SVM, Quadratic SVM, and K-Nearest Neighbours (KNN) algorithms, this paper assesses several feature selection techniques for machine learning in DDoS detection. It highlights the crucial role that optimal feature selection plays in improving classification accuracy and SDN controller performance. According to the testing findings, the KNN classifier provides a 98.41% detection rate for DDoS attacks.

关键词： Support vector machines Signal processing algorithms Machine learning Nearest neighbor methods Denial-of-service attack Feature extraction Classification algorithms

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Prototyping of Low-Cost Configurable Sparse Neural processing Unit with Buffer and Mixed-Precision Reshapeable MAC Array

Prototyping of Low-Cost Configurable Sparse Neural Processin...

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international conference on parallel and distributed Systems (ICPADS)

作者： Binyi Wu Wolfgang Furtner Bernd Waschneck Christian Mayr Faculty of Electrical and Computer Engineering Technische Universit¨at Dresden Dresden Germany Division of Power and Sensor Systems Infineon Technologies Munich Germany Centre for Tactile Internet with Human-in-the-loop (CeTI) Technische Universität Dresden Dresden Germany

More recently, it has become possible to run deep learning algorithms on edge devices such as microcontrollers due to continuous improvements in neural network optimization algorithms such as quantization and neural architecture search. Nonetheless, most of the embedded hardware available today still falls short of the requirements of running deep neural networks. As a result, specialized processors have emerged to improve the inference efficiency of deep learning algorithms. However, most are not for edge applications that require efficient and low-cost hardware. Therefore, we design and prototype a low-cost configurable sparse Neural processing Unit (NPU). The NPU has a built-in buffer and a reshapable mixed-precision multiply-accumulator (MAC) array. The computing and memory resources of the NPU are parameterized, and different NPUs can be derived. Besides, users can also conFigure the NPU at runtime to fully utilize the resources. In our experiments, the 200MHz NPU with only 32 MACs is more than 32 times faster than the 400MHzSTM32H7 when inferring MobileNet-Vl. Besides, the yielded NPUs can achieve roofline or even beyond roofline performance. The buffer and reshapeable MAC array push the NPU’s attainable performance to the roofline, while the feature of supporting sparsity allows the NPU to obtain performance beyond the roofline.

关键词： Deep learning Runtime Quantization (signal) Program processors Neural networks Random access memory Prototypes

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Cloud Resource Allocation using Deep Learning techniques –A Study

Cloud Resource Allocation using Deep Learning Techniques –A...

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Soft Computing for Security applications (ICSCSA), international conference on

作者： V.Mahalakshmi V. Poornima Department of Computer Science Vel’s Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

The current era of information processing, communication, and technological advances provides resources to clients on demand. The client and service provider determine how much need there is for the resources. The fitness function and resource management strategy determine the best way to allocate cloud resources. Cloud Computing (CC) is a popular technology these days with the use of centralised computing facilities. Clients can utilize these resources to finish calculations, and the CC center will provide the user with the program's processed results. CC is useful for both consumer and business users. Users can save computing expenditures by avoiding the need to buy numerous PCs by investing in a cloud server. In huge-scale distributed computing, allocation of resources is essential as networks of machines face challenging optimization issues. This study is to analyse and evaluate the cloud resource optimization techniques such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), (Long Short term memory (LSTM) and Reinforcement learning (RL) for the allocation of resources in cloud.

关键词： Cloud computing Recurrent neural networks Reinforcement learning Throughput Resource management Convolutional neural networks Servers Security Optimization Long short term memory

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StreamBucket: In-Network Adaptation for Late-Binding Stream processing Systems

StreamBucket: In-Network Adaptation for Late-Binding Stream ...

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IEEE international conference on Cloud Networking (CLOUDNET)

作者： Brian Ramprasad Pritish Mishra Maycon L. M. Peixoto Eyal De Lara Dept. of Computer Science University of Toronto Canada Institute of Computing Federal University of Bahia Brazil

ISBN: (数字)9798350376562

ISBN: (纸本)9798350376579

Stream processing applications are increasingly de-ployed on hierarchical geo-distributed edge-cloud environments. applications in such environments require frequent deployment reconfigurations to balance performance and cost by optimally utilizing the diverse resources. Recently, late-binding stream processing frameworks using a hierarchical network of routers have been introduced to seamlessly execute such reconfigurations with minimal impact on application performance. However, late-binding frameworks face scalability challenges as the routers can become a bottleneck. This restricts the maximum throughput that can be achieved by the network. We propose StreamBucket, a novel protocol that increases the capacity of late-binding routers by intelligently batching the tuples at various levels of the routing network and employs header compression techniques to reduce bandwidth. We design a model to predict the optimal batch size for unseen workloads, which reduces the amount of costly profiling. Our evaluations show that StreamBucket achieves up to 5x improvement in throughput using multi-tier batching and between 15% to 85% in bandwidth savings when using our protocol's compressed header.

关键词： Adaptation models Costs Scalability Bandwidth Predictive models Throughput Routing Routing protocols Faces Edge computing

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