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A Guaranteed Approximation Algorithm for Scheduling Fork-Joins with Communication Delay

A Guaranteed Approximation Algorithm for Scheduling Fork-Joi...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Pierre-François Dutot Yeu-Shin Fu Nikhil Prasad Oliver Sinnen CNRS Inria Grenoble INP* LIG Univ. Grenoble Alpes Grenoble France Dept. of Electrical Computer & Software Engineering Parallel and Reconfigurable Computing Lab University of Auckland Auckland New Zealand

Scheduling task graphs with communication delay is a widely studied NP-hard problem. Many heuristics have been proposed, but there is no constant approximation algorithm for this classic model. In this paper, we focus on the scheduling of the important class of fork-join task graphs (describing many types of common computations) on homogeneous processors. For this sub-case, we propose a guaranteed algorithm with a $\left( {1 + \frac{m}{{m - 1}}} \right)$-approximation factor, where m is the number of processors. The algorithm is not only the first constant approximation for an important sub-domain of the classic scheduling problem, it is also a practical algorithm that can obtain shorter makespans than known heuristics. To demonstrate this, we propose adaptations of known scheduling heuristic for the specific fork-join structure. In an extensive evaluation, we then implemented these algorithms and scheduled many fork-join graphs with up to thousands of tasks and various computation time distributions on up to hundreds of processors. Comparing the obtained results demonstrates the competitive nature of the proposed approximation algorithm.

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Machine Learning with Reconfigurable Privacy on Resource-Limited computing Devices 19

Machine Learning with Reconfigurable Privacy on Resource-Lim...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Imtiaz, Sana Tania, Zannatun N. Chaudhry, Hassan Nazeer Arsalan, Muhammad Sadre, Ramin Vlassov, Vladimir KTH Royal Inst Technol EECS SCS Stockholm Sweden Catholic Univ Louvain ICTEAM INGI Louvain La Neuve Belgium Politecn Milan DEIB Milan Italy Tech Univ Carolo Wilhelmina Braunschweig FK EITP Braunschweig Germany

ISBN: (纸本)9781665435741

Ensuring user privacy while learning from the acquired Internet of Things sensor data, using limited available compute resources on edge devices, is a challenging task. Ideally, it is desirable to make all the features of the collected data private but due to resource limitations, it is not always possible as it may cause overutilization of resources, which in turn affects the performance of the whole system. In this work, we use the generalization techniques for data anonymization and provide customized injective privacy encoder functions to make data features private. Regardless of the resource availability, some data features must be essentially private. All other data features that may pose low privacy threat are termed as nonessential features. We propose Dynamic Iterative Greedy Search (DIGS), a novel approach with corresponding algorithms to select the set of optimal data features to be private for machine learning applications provided device resource constraints. DIGS selects the necessary and the most private version of data for the application, where all essential and a subset of nonessential features are made private on the edge device without resource overutilization. We have implemented DIGS in Python and evaluated it on Raspberry Pi model A (an edge device with limited resources) for an SVM-based classification on real-life health care data. Our evaluation results show that, while providing the required level of privacy, DIGS allows to achieve up to 26.21% memory, 16.67% CPU instructions, and 30.5% of network bandwidth savings as compared to making all the data private. Moreover, our chosen privacy encoding method has a positive impact on the accuracy of the classification model for our chosen application.

关键词： Data privacy optimization greedy algorithms machine learning anonymization consumer-producer models edge devices IoT

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Qualitative QoS-aware Scheduling of Moldable parallel Jobs on HPC Clusters*

Qualitative QoS-aware Scheduling of Moldable Parallel Jobs o...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zhengxiong Hou Yubing Liu Hong Shen Jiyuan Liu Jianhua Gu School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In service oriented high-performance computing (HPC) clusters, end users have various Quality of Service (QoS) requirements. Most of the existing research work focuses on quantitative QoS requirements, such as deadlines, for rigid jobs. While, in many cases, it is more convenient for users to qualitatively state QoS requirements (such as performance-sensitive) at the submission of their jobs. Almost all kinds of QoS requirements will be greatly impacted by job scheduling, which determine the degree of job parallelism, execution time and waiting time, etc. Most modern parallel applications are moldable in the sense that they can choose a resource allocation before execution. Traditional sequential job scheduling mechanism with fixed resource allocation appears to be an obstacle to improve QoS for end users. To address this issue, we propose a novel qualitative QoS-aware sub-queue simultaneous scheduling method for moldable parallel jobs (with variable resource allocation) on HPC Clusters. We first define the qualitative QoS models for end users, then present our sub-queue simultaneous scheduling method, including job sequencing and resource allocation algorithms for a set of moldable parallel jobs on multi-core clusters. Our method can efficiently sequence queuing jobs and allocate appropriate resources for simultaneously running some performance-sensitive jobs in a sub-queue rather than running them one by one. Experimental results demonstrate the effectiveness of our method to improving QoS for end users.

关键词： Sequential analysis Schedules Costs Scheduling algorithms Clustering algorithms Quality of service parallel processing Scheduling Resource management Time factors

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SUPER: SUb-Graph parallelism for TransformERs 35

SUPER: SUb-Graph Parallelism for TransformERs

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

作者： Jain, Arpan Moon, Tim Benson, Tom Subramoni, Hari Jacobs, Sam Ade Panda, Dhabaleswar K. Van Essen, Brian Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA USA Lawrence Livermore Natl Lab Global Secur Comp Applicat Div Livermore CA USA

ISBN: (纸本)9781665440660

Transformer models have revolutionized the field of Natural Language Processing (NLP) and they achieve state-of-the-art performance in applications like machine translation, question answering, regression, and summarization. However, training Transformers is challenging because of their large memory and compute requirements. The literature contains several approaches to parallelize training, like layer parallelism and pipeline parallelism, but they are optimized to benefit outof-core models and they don't exploit the inherent parallelism in Transformer models. Other work uses model parallelism to achieve weak scaling by increasing the model size. In this paper, we propose sub-graph parallelism that provides a significant performance improvement over pure data parallelism with a fixed number of resources, and as an additional technique for strong- and weak-scaling without increasing model capacity. Our technique accelerates the training of Transformer models and we generalize the concept to any neural network with multiple branches. We optimize the communication for sub-graph parallelism and combine it with data parallelism to scale performance up to 1024 GPUs. To decrease communication overheads, we propose a topology-aware scheme that limits inter-node communication. Finally, we empirically compare sub-graph parallelism with pure data parallelism and demonstrate its performance benefits in end-to-end training.

关键词： scalable deep learning transformers sub-graph parallelism algorithms deep learning distributed training

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DLion: Decentralized distributed Deep Learning in Micro-Clouds 21

DLion: Decentralized Distributed Deep Learning in Micro-Clou...

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

作者： Hong, Rankyung Chandra, Abhishek Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781450382175

Deep learning (DL) is a popular technique for building models from large quantities of data such as pictures, videos, messages generated from edges devices at rapid pace all over the world. It is often infeasible to migrate large quantities of data from the edges to centralized data center(s) over WANs for training due to privacy, cost, and performance reasons. At the same time, training large DL models on edge devices is infeasible due to their limited resources. An attractive alternative for DL training distributed data is to use micro-clouds-small-scale clouds deployed near edge devices in multiple locations. However, micro-clouds present the challenges of both computation and network resource heterogeneity as well as dynamism. In this paper, we introduce DLion, a new and generic decentralized distributed DL system designed to address the key challenges in micro-cloud environments, in order to reduce overall training time and improve model accuracy. We present three key techniques in DLion: (1) Weighted dynamic batching to maximize data parallelism for dealing with heterogeneous and dynamic compute capacity, (2) Per-link prioritized gradient exchange to reduce communication overhead for model updates based on available network capacity, and (3) Direct knowledge transfer to improve model accuracy by merging the best performing model parameters. We build a prototype of DLion on top of TensorFlow and show that DLion achieves up to 4.2x speedup in an Amazon GPU cluster, and up to 2x speed up and 26% higher model accuracy in a CPU cluster over four state-of-the-art distributed DL systems.

关键词： Edge computing Deep learning Micro-clouds Resource allocation

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Message from the HCW General Chair

Message from the HCW General Chair

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

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ArkFS: A distributed File System on Object Storage for Archiving Data in HPC Environment

ArkFS: A Distributed File System on Object Storage for Archi...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Kyu-Jin Cho Injae Kang Jin-Soo Kim Seoul National University Seoul South Korea

As the burst buffer is being widely deployed in the HPC (High-Performance computing) systems, the distributed file system layer is taking the role of campaign storage where scalability and cost-effectiveness are of paramount importance. However, the centralized metadata management in the distributed file system layer poses a scalability challenge. The object storage system has emerged as an alternative thanks to its simplified interface and scale-out architecture. Despite this, the HPC communities are used to working with the POSIX interface to organize their files into a global directory hierarchy and control access through access control *** this paper, we present ArkFS, a near-POSIX compliant and scalable distributed file system implemented on top of the object storage system. ArkFS achieves high scalability without any centralized metadata servers. Instead, ArkFS lets each client manage a portion of the file system metadata on a per-directory basis. ArkFS supports any distributed object storage system such as Ceph RADOS or S3-compatible system with an appropriate API translation module. Our experimental results indicate that ArkFS shows significant performance improvement under metadata-intensive workloads while showing near-linear scalability. We also demonstrate that ArkFS is suitable for handling the bursty I/O traffic coming from the burst buffer layer to archive cold data.

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Cloud-Based Fine-Grained parallel Optimization on CPU-GPU Heterogeneous Hyperspectral Image Superpixel Space-Spectrum Fusion Classification Algorithms

Cloud-Based Fine-Grained Parallel Optimization on CPU-GPU He...

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IEEE international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hongfei Wang Zhigang Tao Zebin Wu Yi Zhang Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Nanjing Research Institute of Electronics Engineering (NrIEE) Nanjing China

To meet the need for efficient execution of hyperspectral remote sensing image classification algorithms, this paper proposes a fine-grained parallel optimization method for a CPU-GPU heterogeneous hyperspectral image superpixel spectral fusion classification algorithm based on cloud computing. Ray is used as the distributed computing engine to fully utilize the logical control ability and large-scale parallel computing ability of the CPU-GPU heterogeneous platform. We first decouple the superpixel spectral fusion classification algorithm, analyze the data dependence and computing characteristics of sub-tasks, use the GPU to accelerate the algorithm, and then further extend the algorithm to the CPU-GPU heterogeneous platform. At the same time, we establish a scheduling model for algorithm task scheduling problems, specifying the value of the parallelism degree for the algorithm in a fine-grained manner. It is verified by experiments that the parallelization method proposed in this paper can effectively improve the execution efficiency with the premise of the accuracy unreduced.

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An Efficient Cooperative Active Caching Strategy in Vehicular Edge Network Based on Asynchronous Federated Learning

An Efficient Cooperative Active Caching Strategy in Vehicula...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Pang Liu Zhuofan Liao Xiaoyong Tang School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Edge caching is a promising technique for effectively reducing backhaul pressure and content access latency in the Internet of Vehicles (IoV). However, the high mobility of vehicles and dynamic user requests often lead to outdated cached content. Expired cache wastes the limited storage space and transmission in vehicular edge computing. Improving the cache hit rate is an effective approach to address these issues. In this work, we propose a Cooperative Active Caching Strategy (CACS) which works as follows. First, user preferences are analyzed from the historical content data of vehicle users. After that, multiple vehicles cooperate to learn the global model under an asynchronous federated learning framework, and get the content popularity from user preferences and content features. To explore the problem of local models being outdated in asynchronous federated learning, CACS integrates model compression algorithms, enhancing system efficiency and prediction accuracy. Finally, a heuristic cooperative caching content placement algorithm is proposed based on a greedy policy to minimize average access latency. Simulation results show that the CACS can improve the cache hit rate by 15% at most compared to existing state-of-the-art caching strategies.

关键词： Federated learning Heuristic algorithms Computational modeling Simulation Predictive models Prediction algorithms Vehicle dynamics Optimization Internet of Vehicles Edge computing

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Federated Multi-Agent Reinforcement Learning for AoI Minimization in UAV-Enabled IoV-MEC Systems

Federated Multi-Agent Reinforcement Learning for AoI Minimiz...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Shoukun Xu Yu Zhang Xueyuan Wang M. Cenk Gursoy Academy of Computer Science and Artificial Intelligence Changzhou University Changzhou China Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY USA

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the advancement of Mobile Edge computing (MEC), effective solutions for communication scenarios, including the industrial Internet of Things (IoT) and the Internet of Vehicles (IoV), are becoming increasingly feasible. Unmanned aerial vehicles (UAVs) can further enhance flexibility in delivering computational services within MEC contexts. Addressing the urgent need for information freshness, we propose a three-tier IoV-MEC system, supported by multiple UAVs and a cloud center, aiming to minimize the system's average age of information (AoI). We propose a heterogeneous multi-agent reinforcement learning algorithm based on the actor-critic framework, where vehicles act as data sources, UAVs serve as edge devices, and the cloud acts as a control center. All three tiers learn interaction strategies cooperatively based on the observations. To further enhance system performance, we implement an efficient federated learning method, allowing same-tier agents to share learning parameters, thus improving system performance and convergence speed. Extensive simulation results demonstrate that the proposed algorithm outperforms baseline algorithms in terms of average AoI and convergence speed.

关键词： Cloud computing System performance Heuristic algorithms Soft sensors Simulation Reinforcement learning Robustness Real-time systems Vehicle dynamics Convergence

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