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Reducing Latency of DAG-based Consensus in the Asynchronous Setting via the UTXO Model 21

Reducing Latency of DAG-based Consensus in the Asynchronous ...

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21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liu, Keyang Jourenko, Maxim Larangeira, Mario The University of Tokyo Japan Tokyo Institute of Technology Japan Tokyo Institute of Technology/IOG Japan

ISBN: (纸本)9798350329223

DAG-based consensus has attracted significant interest due to its high throughput in asynchronous network settings. However, existing protocols such as DAG-rider (Keidar et al., PODC 2021) and 'Narwhal and Tusk' (Danezis et al., Eurosys 2022) face two undesired practical issues: (1) high transaction latency and (2) high cost to verify transaction outcomes. To address (1), this work introduces a novel commit rule based on the Unspent Transaction Output (UTXO) Data Model, which allows a node to predict the transaction results before triggering the commitment. We propose a new consensus algorithm named 'Board and Clerk', which reduces the transaction latency by half for roughly 50% of transactions according to our experiments. As the tolerance for faults escalates, more transactions can partake in this latency reduction. In addition, we also propose the Hyper-Block Model with two flexible proposing strategies to tackle (2): blocking and non-blocking. Using our proposed strategies, each node first predicts the transaction results if its proposal is committed and packs this result as a commitment in its proposal. The hyper-block packs the proposal's signature, and the consensus layer outputs to prove the transaction results. © 2023 IEEE.

关键词： Byzantine Fault Tolerant Consensus UTXO

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Acceleration of Radiofrequency Ablation Process for Liver Cancer Using GPU 28

Acceleration of Radiofrequency Ablation Process for Liver Ca...

引用

28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Schepke, Claudio Miletto, Marcelo Cogo Fed Univ Pampa UNIPAMPA Lab Estudos Avancados LEA Campus Alegrete BR-97546550 Alegrete Brazil Fed Univ Rio Grande Sul UFRGS Grp Proc Paralelo & Distribuido GPPD Campus Vale BR-91509900 Porto Alegre RS Brazil

ISBN: (纸本)9781728165820

Computational simulation is a technique used in several research areas. In medicine, the Radiofrequency Ablation Finite Element Method (RAFEM) application was developed to simulate the RadioFrequency Ablation (RFA) process, which is a medical procedure to treat hepatic cancer. This application presents a high computational time to perform a simulation, taking up to 20 hours for simulation while the RFA procedure itself lasts from four to six minutes. Some efforts have already been carried out to obtain better performance for the application. However, none of them considered the use of GPUs, which is an architecture that can be used to accelerate finite element method applications. This work aims to propose a parallelization approach to explore ways to obtain better performance for the application through the use of GPUs to reduce the execution time under a few minutes. We adopted an iterative development process to coordinate and to generate parallel versions of the code. Through this process allied to the use of GPU-accelerated code libraries, it was possible to create different versions of the application, adding improvements to each one. The results obtained with the best version showed a reduction of the computation time by up to 18 times. Therefore, this improvement makes the use of RAFEM application more feasible in health treatment by reducing the waiting time for the results.

关键词： Graphics processing unit

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Off-Chip Congestion Management for GPU-based Non-Uniform processing-in-Memory networks 28

Off-Chip Congestion Management for GPU-based Non-Uniform Pro...

引用

28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Punniyamurthy, Kishore Gerstlauer, Andreas Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781728165820

Advancements in packaging and 3D-stacking technology have enabled novel non-uniform processing-in-memory (NUPIM) architectures integrating a network of hybrid modules each with varying sizes and proportions of compute and memory. In such systems, network congestion can have a significant impact on performance and energy-efficiency scaling. At the same time, NUPIM systems often have strict power and thermal constraints, where traditional routing/redundancy or buffering based approaches to reduce congestion cannot be applied. In this paper, we propose a low-overhead yet scalable scheme for congestion management in off-chip networks of emerging GPU-based NUPIM systems. We note that congestion in NUPIM networks exhibits a form of tree saturation in which individually congested links form congested paths, where congestion effects due to back pressure vary across paths. Additionally, memory responses can have different performance impact due to memory divergence. Our approach exploits differences in performance impact of NUPIM traffic to mitigate congestion effects. We dynamically track congested paths and divergent data to manage priorities and accelerate performance-critical traffic. We further use critical paths knowledge to dynamically manage link widths and save I/O energy. Results show that our congestion management outperforms a configuration with double the number of virtual channels and buffers indicating its effectiveness in managing congestion under stringent constraints. It improves performance by 15% (and up to 31%) over baseline and 10% (and up to 29%) against a memory distance based approach for congestion-affected benchmarks. Our scheme reduces 78% of I/0 energy on average and up to 29% of system energy across all benchmarks.

关键词： Energy efficiency

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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Sketch-based Real-time Intrusion Detection Framework for Industrial Internet of Things 29

Sketch-based Real-time Intrusion Detection Framework for Ind...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Lyu, Mengda Peng, Lizhi Li, Hui Yang, Bo Chen, Xiang University of Jinan School of Information Science and Engineering Jinan China Quan Cheng Laboratory Jinan China Inspur Electronic Information Industry Co. Ltd Jinan China

ISBN: (纸本)9798350330717

Intrusion detection technology is of great significance to enhance the network security protection of industrial Internet of Things (IIoT) and ensure the efficient implementation of the production process. Aiming at the problems of poor real-time performance and high false positive rate (FPR) of existing intrusion detection methods in IIoT, a sketch-based intrusion detection framework is proposed. The framework employs an improved sketch algorithm as a primary classification module, which is able to process raw traffic in real-time, and perform traffic splitting and filtering efficiently. To improve accuracy, the filtered traffic is fed into a machine learning (ML) based secondary classification module for further analysis. Compared to direct analysis, the sketch can filter out a portion of the benign traffic, thus improving the efficiency of the secondary module. Our framework can work as a pre-module for existing ML based intrusion detection methods, giving them the ability to process traffic in real-time and reduce FPRs. We validate the performance of the framework by testing it on the latest publicly available dataset, Modbus 2023. © 2023 IEEE.

关键词： data streaming algorithm IIoT intrusion detection system sketch

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Towards Adaptive Adjusting and Efficient Scheduling Coflows based on Deep Reinforcement Learning 29

Towards Adaptive Adjusting and Efficient Scheduling Coflows ...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Zichao Yan, Kedong Chang, Guanxin Huang, Chanying Xiao, Shan Yang, Yuxin Nanjing University of Science and Technology School of Cyber Science and Engineering Jiangyin China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Fiberhome Communication Technology Co. LTD Fiberhome R & D Center Wuhan China Nanjing Univ. of Science & Technology School of Cyber Science & Engineering Jiangyin China

ISBN: (纸本)9798350330717

The rapid development of current data centers and Industrial Internet of Things has brought about the explosive growth of information, which leads to the need for better performance of cluster communication systems. Coflow scheduling has the potential to enhance communication performance among applications in data parallel clusters. However, current coflow scheduling techniques that lack preliminary knowledge often depend on a fixed set of threshold parameters within a multilevel feedback queue (MLFQ), disregarding network variability. Though manually tweaking threshold settings may support network flexibility, it negatively impacts real-time performance and increases workload. Furthermore, manual threshold adjustments often fail to react promptly and adaptively to network environmental changes. To address the issues highlighted above, this paper presents D-MLFQ, a novel approach that leverages deep reinforcement learning to dynamically and autonomously regulate the threshold of MLFQ. As a result, D-MLFQ offers enhanced scheduling and communication optimization capabilities. Furthermore, the study performs trace-driven simulations to assess the efficacy of D-MLFQ. Empirical data indicate that D-MLFQ outperforms Aalo, which utilizes fixed threshold, by up to 1.39× in terms of coflow's completion time. Compared to other common scheduling algorithms such as per-flow fairness, D-MLFQ achieves up to 2.13× faster completion time. © 2023 IEEE.

关键词： coflow scheduling deep reinforcement learning dynamic threshold efficiency MLFQ

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Modeling Ledger Dynamics in IOTA Blockchain

Modeling Ledger Dynamics in IOTA Blockchain

引用

IEEE Global Communications conference (GLOBECOM)

作者： Guo, Fengyang Xiao, Xun Hecker, Artur Dustdar, Schahram Huawei Technol Munich Res Ctr Munich Germany TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9781665435406

IOTA blockchain is a new type of distributed ledger systems that is lightweight without mining and feeless-of-using. Rather than using a chain structure as in traditional blockchains, IOTA organizes ledger records with a directed acyclic graph (DAG), called Tangle. When message entries are committed into the ledger, the ledger tangle grows in a special way where multiple messages could be attached by different processing nodes in parallel. Such a unique evolution process motivates us to study the ledger tangle dynamics, which is unexplored so far. In this paper, we present the first generative modeling for IOTA tangle based on stochastic analysis. A key finding is that IOTA tangle renders a double Pareto Lognormal (dPLN) distribution, rather not typical network models (e.g., Power-Law and Exponential distributions). Quantitative comparisons show that the fitting quality of our model outperforms existing popular models on official realworld datasets published by IOTA Foundation. Estimated model parameters are provided, which is immediately instrumental for a more realistic IOTA network generator design. The proposed generative model also provides a deeper understanding of the internal mechanics of IOTA network.

关键词： IOTA Blockchain network network Modeling network Dynamics Degree Distribution

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Efficient NAS parallel Benchmark Kernels with CUDA 28

Efficient NAS Parallel Benchmark Kernels with CUDA

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： de Araujo, Gabriell Alves Griebler, Dalvan Danelutto, Marco Fernandes, Luiz Gustavo Pontifical Catholic Univ Rio Grande Sul PUCRS Sch Technol Porto Alegre RS Brazil Tres De Maio Fac SETREM Lab Adv Res Cloud Comp LARCC Tres De Maio Brazil Univ Pisa UNIPI Comp Sci Dept Pisa Italy

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

NAS parallel Benchmarks (NPB) are one of the standard benchmark suites used to evaluate parallel hardware and software. There are many research efforts trying to provide different parallel versions apart from the original OpenMP and MPI. Concerning GPU accelerators, there are only the OpenCL and OpenACC available as consolidated versions. Our goal is to provide an efficient parallel implementation of the five NPB kernels with CUDA. Our contribution covers different aspects. First, best parallel programming practices were followed to implement NPB kernels using CUDA. Second, the support of larger workloads (class B and C) allow to stress and investigate the memory of robust GPUs. Third, we show that it is possible to make NPB efficient and suitable for GPUs although the benchmarks were designed for CPUs in the past. We succeed in achieving double performance with respect to the state-of-theart in some cases as well as implementing efficient memory usage. Fourth, we discuss new experiments comparing performance and memory usage against OpenACC and OpenCL state-of-the-art versions using a relative new GPU architecture. The experimental results also revealed that our version is the best one for all the NPB kernels compared to OpenACC and OpenCL. The greatest differences were observed for the FT and EP kernels.

关键词： Graphics processing units Kernel Benchmark testing Programming Computer architecture Hardware Standards

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HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detection in High-Speed networks 28

HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detect...

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

作者： Yu, Jiakun Sun, Yu E. Huangt, He Du, Yang Gao, Guoju Xu, Hongli Chen, Shigang Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Soochow Univ Sch Rail Transportat Suzhou Peoples R China Tongji Univ Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Univ Florida IiDepartment Comp & Informat Sci & Engn Gainesville FL USA

ISBN: (纸本)9781665473156

Detecting heavy hitters that constitute the majority of network traffic is a critical task in network measurement. However, the highly skewed network traffic and the size-limited on-chip memory pose great challenges for heavy-hitter detection. The prior arts either use sketches to track all flows or maintain a fix-sized tracking list for recording elephant flows, resulting in limited detection precision or time-consuming tracking list exchanges. This paper presents HeavyTracker, an efficient algorithm that detects heavy hitters based on a count-with-threshold strategy. A two-dimensional tracker unit array is employed in our design to capture the heavy hitters, where each flow is randomly mapped to one unit in all rows. The tracker unit is designed to accept many flows and precisely report the largest two with frequencies that reach a predefined threshold. This design eliminates the frequent and time-consuming exchanges for maintaining the tracking list, allowing us to use a hash table to track the reported elephant flows. Experimental results based on real Internet traces show that HeavyTracker achieves a high F ss-Score of threshold- t detection (0.97) and over 99% precision of top-k detection under a tight memory size. Besides, it reduces the frequency estimation error by 97.4% compared to the stateof-the-art.

关键词： Traffic measurement Heavy hitters Stream processing algorithms

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Reachability Labeling for distributed Graphs 38

Reachability Labeling for Distributed Graphs

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38th IEEE International conference on Data Engineering (ICDE)

作者： Zhang, Junhua Li, Wentao Qin, Lu Zhang, Ying Wen, Dong Cui, Lizhen Lin, Xuemin Univ Technol Sydney FEIT AAII Ultimo Australia Univ New South Wales Sydney NSW Australia Shandong Univ Jinan Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Real-world graphs are typically distributed across multiple data centers. When performing reachability queries on these distributed graphs, reachability labeling methods ensure fast query processing by using lightweight indexes. One of the best-known labeling methods is TOL;however, TOL is a serial algorithm and cannot handle distributed graphs. The main goal of this paper is to design new labeling methods that can work in parallel while producing the same index as TOL. To this end, we investigate the limitation of TOL and thus propose a filtering-and-refinement framework for index creation. This framework first obtains a super-set of each vertex's label sets and then eliminates the invalid elements. based on this framework, we design distributed labeling algorithms and then use batch processing to improve efficiency. Experimental results on real-world graphs show that the proposed algorithms can index distributed graphs efficiently.

关键词： Data centers Heuristic algorithms Query processing Design methodology conferences Filtering algorithms Data engineering

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