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Modeling Memory Contention between Communications and Computations in distributed HPC systems

Modeling Memory Contention between Communications and Comput...

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

作者： Alexandre DENIS Emmanuel JEANNOT Philippe SWARTVAGHER Inria Bordeaux - Sud-Ouest Bordeaux France

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

To amortize the cost of MPI communications, distributed parallel HPC applications can overlap network communications with computations in the hope that it improves global application performance. When using this technique, both computations and communications are running at the same time. But computation usually also performs some data movements. Since data for computations and for communications use the same memory system, memory contention may occur when computations are memory-bound and large messages are transmitted through the network at the same time. In this paper we propose a model to predict memory band-width for computations and for communications when they are executed side by side, according to data locality and taking contention into account. Elaboration of the model allowed to better understand locations of bottleneck in the memory system and what are the strategies of the memory system in case of contention. The model was evaluated on many platforms with different characteristics, and showed a prediction error in average lower than 4 %.

关键词： distributed processing Runtime Computational modeling distributed databases Bandwidth Predictive models Data models

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GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient parallel Processing of Massive Datasets

GraphTensor: Comprehensive GNN-Acceleration Framework for Ef...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Junhyeok Jang Miryeong Kwon Donghyun Gouk Hanyeoreum Bae Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology (KAIST)

We present GraphTensor, a comprehensive open-source framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use programming primitives that appreciate both graph and neural network execution behaviors from the beginning (graph sampling) to the end (dense data processing). Our framework runs diverse graph neural network (GNN) models in a destination-centric, feature-wise manner, which can significantly shorten training execution times in a GPU. In addition, GraphTensor rearranges multiple GNN kernels based on their system hyperparameters in a self-governing manner, thereby reducing the processing dimensionality and the latencies further. From the end-to-end execution viewpoint, GraphTensor significantly shortens the service-level GNN latency by applying pipeline parallelism for efficient graph dataset preprocessing. Our evaluation shows that GraphTensor exhibits 1.4× better training performance than emerging GNN frameworks under the execution of large-scale, real-world graph workloads. For the end-to-end services, GraphTensor reduces training latencies of an advanced version of the GNN frameworks (optimized for multi-threaded graph sampling) by 2.4×, on average.

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Improving Federated Learning on Heterogeneous Data via Serial Pipeline Training and Global Knowledge Regularization

Improving Federated Learning on Heterogeneous Data via Seria...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yiyang Luo Ting Lu Shan Chang Bingyue Wang School of Computer Science and Technology Donghua University Shanghai China School of Computer Science and Technology Donghua University Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China

Federated learning is a distributed machine learning paradigm that resolves the conflict between training requirements and client data privacy. There are some challenges for federated learning, such as data heterogeneity and communication load, which lead to the global model bias and slow convergence. In this work, we address the problem of data heterogeneity and communication load from a novel perspective, which is named FedSPARK. 1) We propose a new federated learning interaction training strategy, serial pipeline training (SPT). SPT changes the local training of a single client to serial training of multiple clients, which improves the performance of the global model on heterogeneous data. 2) We propose the global knowledge regularization (GKR) which is inspired by continuous learning. GKR can reduce the bias of the client local model by building global knowledge. Through theoretical analysis and experiments on multiple datasets, we show that our approaches greatly reduce the computation of clients and the amount of communication between clients and server, and improve the efficiency of federated learning compared to existing methods.

关键词： Training Data privacy Federated learning Computational modeling Pipelines distributed databases Data models

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TagTree: Global Tagging Index with Efficient Querying for Time Series databases

TagTree: Global Tagging Index with Efficient Querying for Ti...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jin Xue Zhiqi Wang Tianyu Wang Zili Shao The Chinese University of Hong Kong Shatin Hong Kong N.T.

Modern time series databases come with a tag-based query interface that allows users to select time series, which are essentially sequences of timestamped data values, based on a set of specific tags. A tagging index is an important component that can efficiently provide such tag-based services. However, existing methods store tag information in external databases or time-partitioned data structures, which has a negative impact on query performance. In this paper, we present a novel abstraction for efficient queries of tag information in time series databases: a hybrid tagging index that manages all tags in one place. By managing tag information globally in a single disk-based data structure, we can fundamentally relieve memory pressure and eliminate I/O overhead of duplicate metadata from existing methods. Furthermore, the tagging index is internally partitioned by time to support time range based queries and data retention which are essential to time series databases. We implement the proposed tagging index as a standalone module which can be integrated with time series storage engines. Experiments on the TSBS benchmark show our proposed method can significantly speed up queries by on average 84.0% and 87.2% compared to Prometheus (using a time-partitioned segment method) and Graphite (using an external database for tag management), respectively.

关键词： databases Time series analysis Graphite Tagging Metadata Data structures Throughput

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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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A Multi-Path Orchestration Method for Cloud-Edge Data Transmission Based on DRL

A Multi-Path Orchestration Method for Cloud-Edge Data Transm...

引用

international Conference on parallel and distributed systems (ICPADS)

作者： Zhihui Wang Xubing Dou Shuyun Li Huan Ma Xinchang Zhang Maoli Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China QuFu normal university Jining China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In addressing the multi-path problem of cloud-edge data transmission, this paper introduces a multi-path orchestration method based on deep reinforcement learning (DRL) within the context of software-defined networks (SDN), termed Deep Q-Routing (DQR). This method establishes optimal data transmission paths between the cloud and multiple edges, based on available bandwidth and transmission delay, while avoiding path congestion to improve bandwidth utilization and reduce transmission delay. The optimal routing in SDN is defined through the design of state space sets, reward functions, and neural networks. Simulation experiments were conducted to evaluate the DQR method. Compared to existing routing methods, the proposed DQR method, which includes a reward function with weighted parameters, demonstrates superior overall network performance.

关键词： Cloud computing Scalability Neural networks distributed databases Bandwidth Routing Deep reinforcement learning Delays Data communication Software defined networking

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Memory-Aware Scheduling of Tasks Sharing Data on Multiple GPUs with Dynamic Runtime systems

Memory-Aware Scheduling of Tasks Sharing Data on Multiple GP...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Maxime Gonthier Loris Marchal Samuel Thibault ENS-Lyon France University of Bordeaux France

The use of accelerators such as GPUs has become mainstream to achieve high performance on modern computing systems. GPUs come with their own (limited) memory and are connected to the main memory of the machine through a bus (with limited bandwidth). When a computation is started on a GPU, the corresponding data needs to be transferred to the GPU before the computation starts. Such data movements may become a bottleneck for performance, especially when several GPUs have to share the communication bus. Task-based runtime schedulers have emerged as a convenient and efficient way to use such heterogeneous platforms. When processing an application, the scheduler has the knowledge of all tasks available for processing on a GPU, as well as their input data dependencies. Hence, it is able to choose which task to allocate to which GPU and to reorder tasks so as to minimize data movements. We focus on this problem of partitioning and ordering tasks that share some of their input data. We present a novel dynamic strategy based on data selection to efficiently allocate tasks to GPUs and a custom eviction policy, and compare them to existing strategies using either a well-known graph partitioner or standard scheduling techniques in runtime systems. We also improved an offline scheduler recently proposed for a single GPU, by adding load balancing and task stealing capabilities. All strategies have been implemented on top of the STARPU runtime, and we show that our dynamic strategy achieves better performance when scheduling tasks on multiple GPU s with limited memory.

关键词： distributed processing Runtime Graphics processing units distributed databases Bandwidth Dynamic scheduling Load management

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Robust Quickest Change Detection in Statistically Periodic Processes

Robust Quickest Change Detection in Statistically Periodic P...

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IEEE international symposium on Information Theory (ISIT)

作者： Banerjee, Taposh Taha, Ahmad John, Eugene Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (纸本)9781538682098

The problem of detecting a change in the distribution of a statistically periodic process is investigated. The problem is posed in the framework of independent and periodically identically distributed (i.p.i.d.) processes, a recently introduced class of processes to model statistically periodic data. An algorithm is proposed that is shown to be robust against an uncertainty in the post-change law. The motivation for the problem comes from event detection problems in traffic data, social network data, electrocardiogram data, and neural data, where periodic statistical behavior has been observed.

关键词： Uncertainty Social networking (online) Event detection Change detection algorithms Stochastic processes distributed databases Robustness

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Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks

Deception Detection from Linguistic and Physiological Data S...

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Panfeng Li Mohamed Abouelenien Rada Mihalcea Zhicheng Ding Qikai Yang Yiming Zhou Department of Electrical and Computer Engineering University of Michigan Ann Arbor USA Department of Computer Science University of Michigan Ann Arbor USA Fu Foundation School of Engineering and Applied Science Columbia University New York USA Department of Computer Science University of Illinois Urbana-Champaign Urbana USA Department of Engineer Sciences Saarland University of Applied Science Saarland Germany

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Deception detection is gaining increasing interest due to ethical and security concerns. This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection. We use a dataset built by interviewing 104 subjects about two topics, with one truthful and one falsified response from each subject about each topic. In particular, we make three main contributions. First, we extract linguistic and physiological features from this data to train and construct the neural network models. Second, we propose a fused convolutional neural network model using both modalities in order to achieve an improved overall performance. Third, we compare our new approach with earlier methods designed for multimodal deception detection. We find that our system outperforms regular classification methods; our results indicate the feasibility of using neural networks for deception detection even in the presence of limited amounts of data.

关键词： Information science Ethics Neural networks distributed databases Linguistics Feature extraction Physiology

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Spatial Data Quality in GIS Data: A Review

Spatial Data Quality in GIS Data: A Review

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international Conference on parallel distributed and Grid Computing (PDGC)

作者： Punit Gupta Gavin McArdle UCD School of Computer Science University College Dublin Ireland Department of Computer Science And Engineering Pandit Deendayal Energy University Gujarat India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Understanding spatial data quality is important in Geographic Information Science (GIS) applications. Spatial data are used in a variety of critical areas, including urban planning, environmental management, emergency response, and natural resource management where the accuracy and precision of spatial data can have a significant impact on decision-making, especially when used with predictive analysis. A review of the importance of spatial data quality in GIS data is necessary to understand the factors that affect the quality of spatial data and strategies used to interrogate and maintain spatial data quality. While there is no standard definition for spatial data quality, typically the term refers to the accuracy, completeness, consistency, and currency of the data. In this work, a review of various applications of spatial data quality in GIS is carried out. The goal is to provide a generalized SDQ (Spatial Data Quality) benchmark to reduce errors in spatial data across various domains.

关键词： Accuracy Reviews Data integrity Urban planning Time series analysis Benchmark testing Spatial databases Vectors Standards Geographic information systems

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