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检索条件"任意字段=2nd International Symposium on Parallel and Distributed Processing and Applications"

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Autonomous Load Balancing in distributed Hash Tables Using Churn and the Sybil Attack

Autonomous Load Balancing in Distributed Hash Tables Using C...

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

作者： Rosen, Andrew Levin, Benjamin Bourgeois, Anu G. Temple Univ Dept Comp Sci Philadelphia PA 19122 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9781665435772

distributed Hash Tables (DHTs) are an integral foundation for a variety of modern internet applications. In previous work, we have shown that DHTs can also be used as a means of organizing a large number of workers to tackle large-scale computing problems in a fault tolerant context. Whether a DHT is being used for file access or distributing a large-scale computing job, a cryptographic hash function is used to assign keys for nodes and data. Ideally, these would be uniformly distributed across the available range, thus evenly distributing the nodes and tasks. However, this is rarely the case in practice and as a result, the workload can become highly unbalanced. To address this issue, there have been numerous methods proposed for load balancing DHTs, but often they are a centralized approach. In this paper, we present four methods to autonomously balance the load of DHTs: 1) induced churn;2) random injection of Sybil Nodes;3) neighbor injection;and 4) invitation of nodes with low workloads. Each approach is completely decentralized, requiring minimal overhead, with individual nodes making decisions based only upon local information. What makes our approach unique is that the strategies rely on using the inherent churn in a DHT or by a variation of the Sybil attack to balance the workload. We simulate the four strategies on a Chord DHT and show they significantly rebalance the workload in a DHT. The strategy of randomly injecting virtual "Sybil" nodes performed the best in terms of balance and speedup.

关键词： distributed Computing Decentralized Systems Peer-to-Peer Networks distributed Hash Tables Self-Organizing Networks Load-Balancing Sybil Attack

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ZSMILES: An Approach for Efficient SMILES Storage for Random Access in Virtual Screening

ZSMILES: An Approach for Efficient SMILES Storage for Random...

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IEEE international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Gianmarco Accordi Davide Gadioli Giorgio Seguini Andrea R. Beccari Gianluca Palermo Dipartimento di Elettronica Informatica e Bioingegneria Politecnico di Milano Milano Italy EXSCALATE - Dompé Farmaceutici SpA Naples Italy

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Virtual screening is a technique used in drug discovery to select the most promising molecules to test in a lab. To perform virtual screening, we need a large set of molecules as input, and storing these molecules can become an issue. In fact, extreme-scale high-throughput virtual screening applications require a big dataset of input molecules and produce an even bigger dataset as output. These molecules' databases occupy tens of TB of storage space, and domain experts frequently sample a small portion of this data. In this context, SMILES is a popular data format for storing large sets of molecules since it requires significantly less space to represent molecules than other formats (e.g., MOL2, SDF). This paper proposes an efficient dictionary-based approach to compress SMILES-based datasets. This approach takes advan-tage of domain knowledge to provide a readable output with separable SMILES, enabling random access. We examine the benefits of storing these datasets using ZSMILES to reduce the cold storage footprint in HPC systems. The main contributions concern a custom dictionary-based approach and a data preprocessing step. From experimental results, we can notice how ZSMILES leverage domain knowledge to compress ×1.13 more than state of the art in similar scenarios and up to 0.29 compression ratio. We tested a CUDA version of ZSMILES targetting NVIDIA's GPUs, showing a potential speedup of 7×.

关键词： distributed processing Dictionaries Conferences Data preprocessing Graphics processing units distributed databases C++ languages

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The Self-adaptive and Topology-aware MPI_Bcast leveraging Collective offload on Tianhe Express Interconnect

The Self-adaptive and Topology-aware MPI_Bcast leveraging Co...

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international symposium on parallel and distributed processing (IPDPS)

作者： Chongshan Liang Yi Dai Jun Xia Jinbo Xu Jintao Peng Weixia Xu Ming Xie Jie Liu Zhiquan Lai Sheng Ma Qi Zhu Department of Computer Science National University of Defense Technology Changsha China Nanhu Lab. Jiaxing China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Large parallel applications have heavily used MPI (Massage Passing Interface) collectives that support portable and efficient group communication operations. MPI_Bcast is one of the most commonly used MPI collectives that broadcast data to all processes of the communication domain. However, traditional software-based broadcast algorithms fail to fully utilize modern interconnection networks’ advanced features such as offloading collectives to the network hardware for efficient group communications. Besides, the semantic gap between MPI_Bcast and hardware multicast of underlying interconnects presents challenges for offload-based algorithms to accelerate MPI_Bcast for a wide range of message *** this paper, we propose a hardware-software co-design MPI_Bcast by efficiently leveraging the NIC-based collective offload provided by Tianhe-express interconnect, which completely precludes the involvement of CPU to accelerate message broadcast. We detail this broadcast mechanism that can be adaptively tuned to offload MPI_Bcast operations from the CPU to the NIC for various message and system sizes. In addition, we further propose a topology-aware broadcast design in conjunction with this offload method to significantly reduce the broadcast latency by constructing the optimal global inter-node communication tree. We implement and evaluate the proposed Tianhe-Express Offload-based Broadcast (TOB) design on Tianhe-2A and Tianhe-EP supercomputers. Extensive experiments have been conducted to evaluate TOB performance at both microbenchmark and application levels. Our solution offers up to 4.94x significant performance speedup at the microbenchmark level over state-of-the-art MPI libraries. For the application-level evaluation, our technique accelerates scientific applications by a maximum speedup of 1.34x.

关键词： distributed processing Multicast algorithms Semantics Switches Reliability engineering Data transfer Hardware

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Quantization improvements for LU Decomposition to Solve Linear Equations 19

Quantization improvements for LU Decomposition to Solve Line...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Chen, Lin He, Kang Jiang, Hao Barrio, Roberto Liu, Jie Li, Tiejun Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Lab Software Engn Complex Syst Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China Univ Zaragoza Dept Appl Math E-50009 Zaragoza Spain

ISBN: (纸本)9781665435741

Quantization is an effective method for compressing Deep Neural Networks. Now, it is considered to accelerate the traditional HPC applications. In this article, we present a quantization method (q8gemm) using integer 8-bits(INT8) for matrix multiplication, which is implemented by block techniques to overcome the overflow problem. We choose different scales for one matrix's each row and those for the other matrix's each column to keep the numbers in range. With the intel advanced matrix extension cblas_gemm_s8u8s32, our q8gemm can accelerate panel-matrix multiplication in LU factorization. Furthermore, we propose a new mixed-precision solver (q8gesv(1)) for the linear equations. Using q8gemm in INT8, q8gesv modifies LU factorization in FP32 and then utilizes GMRES iterative refinement in FP 64 to bring the solution back to 64 bits accuracy. Numerical tests show that on two Intel CPU platforms with avx2 and avx512, q8gemm achieves a speedup of 1.313-1.621 over cblas_sgemm of MKL. The execution time of cblas_gemm_s8u8s32 makes up 86.78%-0.85% of the q8gemm. By means of q8gemm, our q8gesv can achieve a speedup of 1.176-1.493 over the optimized Higham's mixed-precision algorithm utilized in the HPL-AI benchmark and a speedup of 1.1-1.459 over the LAPACKE_dsgesv function from MKL LAPACK. Meanwhile, q8gesv runs up to 2.859X faster on Intel(R) Core(TM) i5-10600KF and to 2.224X faster on Intel(R) Xeon(R) Gold 6130 than the LAPACKE_dgesv function from MKL LAPACK.

关键词： GMRES-IR LU Factorization Matrix Multiplication Mixed-precision Quantization

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Improved Bat Algorithms for Solving Flowtime Minimization Task Scheduling Problems in D2D-Enabled MEC

Improved Bat Algorithms for Solving Flowtime Minimization Ta...

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international symposium on parallel and distributed processing with applications, ISPA

作者： Linhua Ma Qingran Yan Yi Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Device-to-device (D2D)-enabled mobile edge computing (MEC) is a promising communication network in which computation tasks on mobile devices (MDs) can not only be processed locally but also can be scheduled to the MEC server or nearby MDs via D2D links in order to improve computational and energy efficiency. Due to the explosive growth of information and consequent large-scale data, the conflict between reducing task response time and the limited resources of MEC server and MDs has become an essential bottleneck. In this paper, we consider a D2D-enabled MEC system and study a task scheduling problem for minimizing the flowtime of all computation tasks under the energy constraints of MDs. To solve this problem, we develop a family of metaheuristic algorithms based upon our proposed framework of improved bat algorithm (IBA), which incorporates multiple best bat individuals. Also, we introduce a new linear updating scheme to update the bat positions and facilitate the evolution process of the metaheuristics. More importantly, we provide a rigorous theoretical analysis to prove the convergence of IBA by using this new updating scheme. Furthermore, we employ an improved initial solution generation strategy for enhancing the effectiveness of IBA. Numerical experiments demonstrate that the proposed algorithms can achieve high-quality scheduling solutions, in terms of shorter flowtimes, more effectively and more efficiently over baseline algorithms.

关键词： Multi-access edge computing Processor scheduling NP-hard problem Metaheuristics Scheduling Mobile handsets Computational efficiency Servers Time factors Convergence

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parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection applications

Parallel Approximations for High-Dimensional Multivariate No...

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international symposium on parallel and distributed processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

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Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy applications in Public Cloud

Alioth: A Machine Learning Based Interference-Aware Performa...

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international symposium on parallel and distributed processing (IPDPS)

作者： Tianyao Shi Yingxuan Yang Yunlong Cheng Xiaofeng Gao Zhen Fang Yongqiang Yang Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Huawei Technologies Cloud BU Beijing China

Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware *** propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth’s interpretability, providing insights to benefit the decision-making of cloud operators. The dataset and code of Alioth have been released on GitHub.

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Multi-Hop D2D Cluster Formation and Resource Allocation for Scalable Video Multicast

Multi-Hop D2D Cluster Formation and Resource Allocation for ...

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international symposium on parallel and distributed processing with applications, ISPA

作者： Hao Nie Wei Wang Rui Dai Chenyan Liu Zhixing Wang Peng Xu Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati USA Centrin Data Vally Technology Co. Ltd Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Scalable video coding (SVC) associated with adaptive modulation and coding (AMC) has provided an excellent solution for transmitting real-time videos. However, some users have bad channel conditions with the base station (BS) and need help getting a good quality of service (QoS). With the fifth generation (5G) of the cellular network, these users can also receive most of the data by device-to-device (D2D) transmissions. In this paper, we focus on live video delivery scenarios. We adopt the multicast Multi-Hop D2D and reuse spectrum spatially to help broadcast the scalable videos. We aim to maximize all users’ average quality of service (QoS) by jointly optimizing D2D cluster heads (CHs) selection and channel reuse. The problem is an integer non-linear programming problem, and we decompose the problem into two subproblems: the D2D cluster formation problem and the resource allocation problem. To solve the first subproblem, we propose a greedy algorithm to form D2D clusters that can broadcast more efficiently. We convert the second subproblem to a combination of two partial graph coloring problems in two conflict graphs. An algorithm similar to the recursive largest first (RLF) algorithm but considering the conflicts in both graphs is provided for the second subproblem. We execute numerical simulations to validate the performance of our proposal.

关键词： Video coding Adaptation models Multicast algorithms Heuristic algorithms Clustering algorithms Modulation Quality of service Encoding Device-to-device communication Resource management

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STRec: Social-Augmented Time-Aware Cross-Domain Sequential Recommendation

STRec: Social-Augmented Time-Aware Cross-Domain Sequential R...

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international symposium on parallel and distributed processing with applications, ISPA

作者： Xiangyun Gao Dan Meng Xianlan Sun Wei Wang Haibei Zhu Chao Kong School of Information and Computer Anhui Polytechnic University Wuhu China OPPO Research Institute Shenzhen China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Cross-domain sequential recommendation leverages a user’s historical interaction behavior across multiple domains to forecast the subsequent item with which they are likely to engage. Despite the efforts made by previous methods in representing users across domains, we believe they still have three inherent limitations: 1) Ignoring the importance of the relations between items among various domains, leading to suboptimal performance. 2) Most existing cross-domain sequential recommendation models are incapable of making full use of timestamp information, merely representing the sequence of user-item interaction sequence through positional embeddings. 3) Existing cross-domain recommendation methods consider only individual users, failing to recognize that social relations may influence user preferences. To overcome these problems, we propose an innovative approach called Social-Augmented Time-Aware Cross-Domain Sequential Recommendation (STRec). The method leverages the integration of multi-domain data with temporal information, further enriched by social influence, to afford a holistic understanding of user preferences. STRec consists of three main components: Initially, embedding modules are employed to convert timestamps and interaction information into embedding vectors respectively. Subsequently, graph neural network modules to uncover collaborative characteristics among inter-sequence items, while sequence attention encoders extract sequential features within item sequences and uncover the potential dependencies of temporal information in different contexts. Conclusively, a social aware attention network is designed to consider users’ social neighbor information and capture the dynamic patterns of social relationships. Through extensive experimentation on real-world datasets across multiple scenarios, we have demonstrated that STRec surpasses several state-of-the-art approaches.

关键词： distributed processing Collaboration Feature extraction Vectors Graph neural networks Data mining

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Improving INT Inference Resilience of CNNs by Vulnerability-Aware Mixed-Precision Quantization

Improving INT Inference Resilience of CNNs by Vulnerability-...

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international symposium on parallel and distributed processing with applications, ISPA

作者： Xiaohui Wei Pei Liu Chenyang Wang Zeyu Guan Qi Wu Hengshan Yue College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The application of INT quantization allows CNN model size and computation to be reduced exponentially, making extreme quantization a hot research topic. However, firstly the decrease in bitwidth increases the probability of error in critical bits, which is unacceptable for safety-critical scenarios that require high resilience of CNNs. Secondly, extreme quantization often requires retraining with high overhead. Considering the higher tolerance of errors in high-bitwidth INTs and the local resilience differences of CNNs, we use mixed precision quantization (MPQ) to trade off resilience and compression effects. Existing MPQ methods mostly focus on compression as the first goal and lack indicators of resilience or vulnerability. Based on a large number of vulnerability analyses, we propose to use the maximum bit average offset and the approximation parameter average quantization error to measure layer vulnerability. And we measure the vulnerability of the entire CNN in terms of the accumulation of layer offsets. Accordingly, we design a complete vulnerability-aware MPQ (VulnA-MPQ) framework. From the perspective of minimizing vulnerability and compression ratio, VulnA-MPQ is performed on multiple CNNs using genetic algorithm (GA) with low overhead. The CNNs only need to be fine-tuned to restore accuracy after VulnA-MPQ. Experiments demonstrate that VulnA-MPQ not only maintains accuracy and compression ratio better but also improves CNNs fault tolerance. Within the 2% accuracy threshold, the random error data tolerated by VGG16 weights and activations are improved by 33%. The number of random errors tolerated by MobileNetV3-large weights is improved by 8.87 times, and that of activations is improved by 4.64 times.

关键词： Fault tolerance distributed processing Quantization (signal) Accuracy Computational modeling Fault tolerant systems Measurement uncertainty Hardware Resilience Genetic algorithms

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