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SnuRHAC: A Runtime for Heterogeneous Accelerator Clusters with CUDA Unified Memory 21

SnuRHAC: A Runtime for Heterogeneous Accelerator Clusters wi...

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30th international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Jung, Jaehoon Park, Daeyoung Jo, Gangwon Park, Jungho Lee, Jaejin Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Moreh Inc Seoul South Korea

ISBN: (纸本)9781450382175

This paper proposes a framework called SnuRHAC, which provides an illusion of a single GPU for the multiple GPUs in a cluster. Under SnuRHAC, a CUDA program designed to use a single GPU can utilize multiple GPUs in a cluster without any source code modification. SnuRHAC automatically distributes workload to multiple GPUs in a cluster and manages data across the nodes. To manage data efficiently, SnuRHAC extends CUDA Unified Memory and exploits its page fault mechanism. We also propose two prefetching techniques to fully exploit UM and to maximize performance. Static prefetching allows SnuRHAC to prefetch data by statically analyzing CUDA kernels. Dynamic prefetching complements static prefetching. SnuRHAC enforces an application to run on a single GPU if it is not suitable for multiple GPUs. We evaluate the performance of SnuRHAC using 18 benchmark applications from various sources. The evaluation result shows that while SnuRHAC significantly improves ease-of-programming, it shows scalable performance for the cluster environment depending on the application characteristics.

关键词： Heterogeneous computing Single device image GPU CUDA Unified Memory Runtime system Device driver

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Single-stage Multi-scale Receptive Field Improvement Lightweight Object Detection Network Based on MobileNetV3

Single-stage Multi-scale Receptive Field Improvement Lightwe...

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international symposium on distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Zhenkai Tong Yefu Wu Yang Liu Hubei Key Laboratory of Transportation Internet of Things School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan China Chongqing Guoyuan Port Co. Ltd Chongqing China

ISBN: (纸本)9781665454636

The computing power of edge devices is difficult to keep up with the development of modern computer technology, and the computing power is not improved enough. In practical application environments, so there is the birth of lightweight models. Lightweight models specifically refer to some models with simple model architecture and low computational load. Although the lightweight model is fast and the model is relatively simple, the detection effect is not very good. This paper proposes a parallel convolution module, performs feature fusion through parallel processing of multi-scale convolution kernels, and then integrates the spatial channel attention mechanism into the module to implement a multi-scale target detection module on a single feature layer. Model fusion proposes anchor boxes to generate heads and become a single-stage object detection model.

关键词： Head Convolution Computational modeling Object detection Computer architecture parallel processing Feature extraction

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An Optical Transceiver Reliability Study based on SFP Monitoring and OS-level Metric Data

An Optical Transceiver Reliability Study based on SFP Monito...

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IEEE/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Paolo Notaro Qiao Yu Soroush Haeri Jorge Cardoso Michael Gerndt Huawei Technologies Duesseldorf GmbH Munich Germany Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany Chair of Distributed and Operating Systems Technical University of Berlin Germany Department of Informatics Engineering/CISUC University of Coimbra Portugal

The increasing demand for cloud computing drives the expansion in scale of datacenters and their internal optical network, in a strive for increasing bandwidth, high reliability, and lower latency. Optical transceivers are essential elements of optical networks, whose reliability has not been well-studied compared to other hardware components. In this paper, we leverage high quantities of monitoring data from optical transceivers and OS-level metrics to provide statistical insights about the occurrence of optical transceiver failures. We estimate transceiver failure rates and normal operating ranges for monitored attributes, correlate early-observable patterns to known failure symptoms, and finally develop failure prediction models based on our analyses. Our results enable network administrators to deploy early-warning systems and enact predictive maintenance strategies, such as replacement or traffic re-routing, reducing the number of incidents and their associated costs.

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LightDAG: A Low-latency DAG-based BFT Consensus through Lightweight Broadcast

LightDAG: A Low-latency DAG-based BFT Consensus through Ligh...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Xiaohai Dai Guanxiong Wang Jiang Xiao Zhengxuan Guo Rui Hao Xia Xie Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Computer Science and Technology Hainan University China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

To improve the throughput of Byzantine Fault Tolerance (BFT) consensus protocols, the Directed Acyclic Graph (DAG) topology has been introduced to parallel data processing, leading to the development of DAG-based BFT consensus. However, existing DAG-based works heavily rely on Reliable Broadcast (RBC) protocols for block broadcasting, which introduces significant latency due to the three communication steps involved in each RBC. For instance, DAGRider, a representative DAG-based protocol, exhibits the best latency of 12 steps, considerably higher than non-DAG protocols like PBFT, which only requires 3 steps. To tackle this issue, we propose LightDAG, which replaces RBC with lightweight broadcasting protocols such as Consistent Broadcast (CBC) and Plain Broadcast (PBC). Since CBC and PBC can be implemented in two and one communication steps, respectively, LightDAG achieves low *** our proposal, we present two variants of LightDAG, namely LightDAG1 and LightDAG2, each providing a trade-off between the best latency and the expected worst latency. In LightDAG1, every block is broadcast using CBC, which exhibits a best latency of 5 steps and an expected worst latency of 14 steps. Since CBC cannot guarantee the totality property, we design a block retrieval mechanism in LightDAG1 to assist replicas in retrieving missing blocks. LightDAG2 utilizes a combination of PBC and CBC for block broadcasting, resulting in the best latency of 4 steps and an expected worst latency of 12(t+1) steps, where t represents the number of actual Byzantine replicas. Since a Byzantine replica may equivocate through PBC, LightDAG2 prohibits blocks from directly referencing contradictory blocks. To ensure liveness, we propose a mechanism to identify and exclude Byzantine replicas if they engage in equivocation attacks. Extensive experiments have been conducted to evaluate LightDAG, and the results demonstrate its feasibility and efficiency.

关键词： Fault tolerance distributed processing Directed acyclic graph Fault tolerant systems Broadcasting Throughput Data processing

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DEAN: A Lightweight and Resource-efficient Blockchain Protocol for Reliable Edge computing

DEAN: A Lightweight and Resource-efficient Blockchain Protoc...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Abdullah Al-Mamun Haoting Shen Dongfang Zhao University of Nevada Reno

Edge computing draws a lot of recent research interests because of the performance improvement by offloading many workloads from the remote data center to nearby edge nodes. Nonetheless, one open challenge of this emerging paradigm lies in the potential security issues on edge nodes. This paper proposes a cooperative protocol, namely DEAN, equipped with a unique resource-efficient quorum building mechanism to adopt blockchain seamlessly in an edge computing infrastructure to prevent data manipulation and allow fair data sharing with quick recovery under resource constraints of limited storage, computing, and network capacity. Specifically, DEAN leverages a parallel mechanism equipped with three independent core components, effectively achieving low resource consumption while allowing secured parallel block processing on edge nodes. We have implemented a system prototype based on DEAN and experimentally verified its effectiveness with a comparison with four popular blockchain implementations: Ethereum, Parity, IOTA, and Hyperledger Fabric. Experimental results show that the system prototype exhibits high resilience to arbitrary failures. Performance-wise, DEAN-based blockchain implementation out-performs the state-of-the-art blockchain systems with up to 88.6 x higher throughput and 26 x lower latency.

关键词： Voting Prototypes Throughput Fabrics Blockchains Safety Security

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Online Dynamic Scaling of Microservices with Fair Probabilistic Routing

Online Dynamic Scaling of Microservices with Fair Probabilis...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yang Chen Shisheng Lin Yi Hu Liangyuan Wang Menglan Hu Pan Lai Yuanai Xie College of Computer Science South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission School of Electronic Information and Communications Huazhong University of Science and Technology Hubei Key Laboratory of Smart Internet Technology

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Microservice architecture, as an emerging network architecture, has gained widespread adoption in latency-sensitive applications within the realm of mobile edge computing (MEC). In MEC networks, these latency-sensitive applications necessitate the concurrent processing of numerous service requests, which are composed of microservices. The complex dependencies between microservices and frequent data communication between servers contribute to the intricacy of deploying and routing microservice instances within the network. Moreover, the dynamic and unpredictable nature of service request traffic significantly complicates the timeliness and efficiency of service deployment and request routing strategies. However, existing research predominantly focuses on static network environments and neglects the time-varying characteristics of service request traffic in realistic scenarios. Consequently, we address the joint optimization problem of service deployment and request routing in the presence of dynamic service request traffic. To model the inherent data dependencies and analyze service request response latency, we employ the open Jackson queuing network. We propose a fine-grained microservice dynamic scaling (FMDS) algorithm to capture the dynamic fluctuations in service request traffic within the network. This algorithm scales microservice instances based on the principle of equal proportional change, obtaining a service deployment scheme that minimizes costs while satisfying latency constraints. Furthermore, we introduce a recursive path search algorithm that explores the service deployment scheme to determine the node forwarding probability for the entire network, adhering to the principles of fair routing. Simulation results show that the proposed method effectively improves network latency stability by 75% and enhances the timeliness of the service deployment strategy.

关键词： Analytical models Heuristic algorithms Simulation Microservice architectures Routing Probabilistic logic Stability analysis Servers Optimization Queueing analysis

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MPLNet: Industrial Anomaly Detection with Memory Bank and Prompt Learning

MPLNet: Industrial Anomaly Detection with Memory Bank and Pr...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Hao Zhou Xuanru Guo Mingcheng Ji Jielin Jiang Haolong Xiang Shengjun Xue Nanjing University of Information Science and Technology Nanjing China University of Science and Technology Beijing Beijin China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China Silicon Lake College Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Anomaly detection is a critical aspect of industrial production processes. Most of the self-supervised training system utilise Convolutional Neural Networks (CNNs) for feature extraction. However, these methods often exhibit poor detection performance on small structural anomalies due to the limited receptive field of CNN. The prompt learning method has been demonstrated to be an effective means of enhancing the global information extraction abilities of the model. However, this approach necessitates the input of a significant number of manual prompts. To address these issues, we propose a Memory And Prompt Learning Based Network (MPLNet) for anomaly detection. MPLNet obtains the differences information between normal and detected images by comparing their features and then uses these differences to generate prompts. By automatically generating prompts, it reduces the workload of manually entering prompts in prompt learning. At the same time, it effectively enhances the model’s ability to extract and use global information. Extensive experiments have shown that the proposed MPLNet achieves state-of-the-art anomaly detection performance on the widely used and challenging MVTec AD dataset and MVTec AD-3D dataset. Index Terms—Anomaly detection, U-Net, Transformer, Prompt, artificial intelligence System

关键词： Training Location awareness Learning (artificial intelligence) Production Manuals Feature extraction Transformers Pins Convolutional neural networks Anomaly detection

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Sound-based Bee Colony State Analysis Using Compact MFCC Patterns

Sound-based Bee Colony State Analysis Using Compact MFCC Pat...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Weihai Huang Weize Yang Zhicong Luo Jun Qi Tingting Zhang Xiangzeng Kong College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China School of Future Technology Fujian Agriculture and Forestry University Fuzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Bees play an important role in agricultural production. However, beekeeping relies on experienced beekeepers to take time and effort to maintain the bee colonies. To lower the threshold of beekeeping and improve efficiency, we proposed a sound-based bee colony state analysis model using compact Mel frequency cepstral coefficient (MFCC) patterns. Facing high-dimensional bee colony sound signals, we obtained MFCCs from the signals and constructed a set of filters to extract MFCC-based compact features called compact MFCC patterns. After extracting compact features, the feature set was given to the support vector machine classifier. We recorded the sounds of bee colonies under normal conditions and in the absence of the queen bee to verify the proposed model. While significantly compressing the dimensionality of MFCCs, the model still achieved an accuracy score of 99.30% in distinguishing the presence of the queen bee. The extracted compact MFCC patterns effectively and compactly characterize the information related to the bee colony states in the bee colony sound signals, giving the model an excellent ability to discriminate the state of the bee colony.

关键词： Support vector machines Analytical models Time-frequency analysis Filters Signal processing algorithms Production Feature extraction Mel frequency cepstral coefficient Time-domain analysis Monitoring

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OpenFFT-SME: An Efficient Outer Product Pattern FFT Library on ARM SME CPUs

OpenFFT-SME: An Efficient Outer Product Pattern FFT Library ...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Ruge Zhang Haipeng Jia Yunquan Zhang Baicheng Yan Penghao Ma Long Wang Wenxuan Zhao Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd Beijing China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Fast Fourier transform (FFT) is widely used in scientific and engineering computation. Recently developed matrix computation units for AI and high-performance computing provide new optimization opportunities for the FFT algorithm. Compared to dedicated matrix multiplication architectures like Intel AMXs, ARM’s Scalable Matrix Extension (SME) provides more flexible outer product instructions to construct matrix multiplications in software. To leverage ARM SME’s matrix multiplication capabilities, this paper proposes a novel optimized outer product pattern for the Cooley-Tukey FFT algorithm and presents OpenFFT-SME, the first FFT library for ARM SME based on this pattern. This pattern can reduce the number of outer product operations and memory accesses to the DFT matrix by exploiting the symmetric and periodic properties of twiddle factors in the DFT matrix. To further boost performance, OpenFFT-SME incorporates software pipelining to enhance execution pipelines in the assembly code kernels. Meanwhile, a butterfly network more suitable for this pattern is designed and integrated. Experiments demonstrate that OpenFFT-SME outperforms vectorization methods on ARM SME CPUs, achieving 3.60x (power of two) and 4.14x speedups (non-power of two) in double-precision compared to FFTW, 2.47x (power of two) and 3.21x (non-power of two) speedups in double-precision compared to FFTE, and speedups of 4.38x (power of two) and 7.02x (non-power of two) in single-precision compared to FFTW. Furthermore, we compare the advantages and disadvantages of our implementation against vectorization methods and analyze its performance characteristics through additional experiments.

关键词： Symmetric matrices Program processors Fast Fourier transforms Software algorithms Discrete Fourier transforms Software Libraries

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A Latency-Predictable Cloud-Native Network Architecture based on XDP

A Latency-Predictable Cloud-Native Network Architecture base...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jifei Wen Jingguo Ge Zheyuan Zhang Yuepeng E Bingzhen Wu School of Cyber Security UCAS Institute of Information Engineering Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Cloud computing and microservices are increasingly supplanting traditional network deployment strategies within data centers due to their inherent flexibility in infrastructure management and rapid scalability. Nevertheless, current approaches often fall short in addressing quality of service (QoS) for virtual networks, especially under conditions of intense resource competition. This shortfall prevents the fulfillment of critical services’ requirements for low latency and predictability. To mitigate this challenge, we propose a cloud-native network service architecture designed to deliver consistently low latency and predictable packet arrival times. This architecture dynamically coordinates and reserves computational resources even during high contention periods, thereby maintaining container network QoS and ensuring the stable availability of microservice applications under extreme conditions. We validated the effectiveness of our proposed solution by deploying multiple container nodes across two directly connected servers and establishing a Kubernetes cluster. By launching a large volume of tasks within a constrained time frame, we assessed the container network’s load, latency, and jitter during peak usage periods. Our results indicate that, although our solution exhibits marginally reduced performance compared to the open-source Kubernetes network plugin under low-load conditions, it significantly outperforms the plugin under high-load scenarios. Specifically, when host CPU usage surpasses 90% and memory usage exceeds 80%, the open-source plugin experiences notable packet loss and long-tail latency distributions. In contrast, our solution demonstrates an 84% reduction in jitter compared to the open-source CNI.

关键词： Cloud computing Scalability Microservice architectures Quality of service Computer architecture Containers Jitter Time factors Servers Low latency communication

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