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An Efficient Scheduling Algorithm for distributed Heterogeneous systems with Task Duplication Allowed 19

An Efficient Scheduling Algorithm for Distributed Heterogene...

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

作者： Shi, Hao Chen, Yixiang Xu, Jinyi East China Normal Univ Dept Embedded Software & Syst Software Engn Inst Shanghai Peoples R China MOE Engn Res Ctr Software Hardware Codesign Techn Shanghai Peoples R China

ISBN: (纸本)9781665435741

Due to the restriction of data transmission speed and bandwidth between processing units, communication delays are significantly impacting the complete time of applications in distributed heterogeneous systems. Most of the existing methods considering communication delays are approximate. The performance of solutions given by these heuristic methods are depends on the input instance. Another common choice is optimization methods, such as genetic algorithms and branch-and-bound search. However, these algorithms solve general situations with low efficiency. This paper proposes an efficient scheduling algorithm with task duplication allowed, called DHSA. Task duplication allows tasks executing multiple times to trade extra computation for communication delays, which is widely used in parallel system scheduling approximately. To address the complexity explosion caused by allowing all-task duplication, we decompose the scheduling into allocation and ordering stages and optimize them separately with solving optimization model and slack-interval aware ordering approach. Extensive experiments show that DHSA gives more optimal solutions within feasible time comparing to two state-of-the-art methods, particularly for applications with medium to high communication load.

关键词： Duplication-based Scheduling distributed Heterogeneous systems Optimization Model Slack-interval Aware Ordering

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A Dynamic distributed Scheduler for Computing on the Edge

A Dynamic Distributed Scheduler for Computing on the Edge

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Fei Hu Kunal Mehta Shivakant Mishra Mohammad AlMutawa Dept. of Computer Science University of Colorado Dept. of Computer Science Kuwait University

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Edge computing is crucial for IoT applications, especially those needing quick, private data handling. However, these applications are resource-intensive, and edge computing resources are limited compared to cloud capabilities. Efficiently using these limited resources is essential to meet all application demands, including latency, privacy, and cost. Due to the dynamic and hybrid nature of IoT environments, static scheduling systems often falls short in meeting these diverse constraints. This paper describes the design, implementation, and evaluation of a dynamic distributed scheduler for the edge. This scheduler schedules the execution of various computing tasks across all computing resources available at the edge in order to satisfy the diverse constraints of an IoT application. The key characteristic of this scheduler is that it constantly monitors the current state of the IoT infrastructure and dynamically adjusts the scheduling of various computing tasks based on the current environmental context as well as the current computing and network conditions. The scheduler utilizes a predictive profiling method to manage the hybrid and variable nature of edge computing resources and tasks. A prototype of this scheduler has been implemented and the paper demonstrates its practicality via an augmented reality application.

关键词： Schedules Privacy Processor scheduling Prototypes parallel processing Dynamic scheduling Real-time systems Internet of Things Resource management Edge computing

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21st international symposium on Automated Technology for Verification and Analysis, ATVA 2023

21st International Symposium on Automated Technology for Ver...

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21st international symposium on Automated Technology for Verification and Analysis, ATVA 2023

ISBN: (纸本)9783031453311

The proceedings contain 38 papers. The special focus in this conference is on Automated Technology for Verification and Analysis. The topics include: Model Checking Strategies from Synthesis over Finite Traces;reactive Synthesis of Smart Contract Control Flows;synthesis of distributed Protocols by Enumeration Modulo Isomorphisms;controller Synthesis for Reactive systems with Communication Delay by Formula Translation;statistical Approach to Efficient and Deterministic Schedule Synthesis for Cyber-Physical systems;compositional High-Quality Synthesis;learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic systems;an Automata-Theoretic Approach to Synthesizing Binarized Neural Networks;syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks;learning Nonlinear Hybrid Automata from Input–Output Time-Series Data;using Counterexamples to Improve Robustness Verification in Neural Networks;a Novel Family of Finite Automata for Recognizing and Learning ω -Regular Languages;on the Containment Problem for Deterministic Multicounter Machine Models;parallel and Incremental Verification of Hybrid Automata with Ray and Verse;an Automata Theoretic Characterization of Weighted First-Order Logic;Graph-Based Reductions for Parametric and Weighted MDPs;scenario Approach for Parametric Markov Models;Fast Verified SCCs for Probabilistic Model Checking.

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21st international symposium on Automated Technology for Verification and Analysis, ATVA 2023

21st International Symposium on Automated Technology for Ver...

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21st international symposium on Automated Technology for Verification and Analysis, ATVA 2023

ISBN: (纸本)9783031453281

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Mathematics of Digital Hyperspace

Mathematics of Digital Hyperspace

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

作者： Kepner, Jeremy Davis, Timothy Gadepally, Vijay Jananthan, Hayden Milechin, Lauren MIT Lincoln Lab Supercomp Ctr Cambridge MA 02139 USA MIT Comp Sci & AI Lab Cambridge MA 02139 USA MIT Math Dept Cambridge MA 02139 USA Texas A&M College Stn TX USA Vanderbilt Nashville TN USA MIT Dept Earth Atmospher & Planetary Sci Cambridge MA 02139 USA

ISBN: (纸本)9781665435772

Social media, e-commerce, streaming video, e-mail, cloud documents, web pages, traffic flows, and network packets fill vast digital lakes, rivers, and oceans that we each navigate daily. This digital hyperspace is an amorphous flow of data supported by continuous streams that stretch standard concepts of type and dimension. The unstructured data of digital hyperspace can be elegantly represented, traversed, and transformed via the mathematics of hypergraphs, hypersparse matrices, and associative array algebra. This paper explores a novel mathematical concept, the semilink, that combines pairs of semirings to provide the essential operations for graph analytics, database operations, and machine learning. The GraphBLAS standard currently supports hypergraphs, hypersparse matrices, the mathematics required for semilinks, and seamlessly performs graph, network, and matrix operations. With the addition of key based indices (such as pointers to strings) and semilinks, GraphBLAS can become a richer associative array algebra and be a plug-in replacement for spreadsheets, database tables, and data centric operating systems, enhancing the navigation of unstructured data found in digital hyperspace.

关键词： graphs hypergraphs hypersparse networks polystore databases algebra

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An Optical Transceiver Reliability Study based on SFP Monitoring and OS-level Metric Data 23

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

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23rd IEEE/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Notaro, Paolo Yu, Qiao Haeri, Soroush Cardoso, Jorge Gerndt, Michael Huawei Technol Duesseldorf GmbH D-80992 Munich Germany Tech Univ Munich Chair Comp Architecture & Parallel Syst Munich Germany Tech Univ Berlin Chair Distributed & Operating Syst Berlin Germany Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9798350301199

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.

关键词： hardware reliability optical network optical transceiver failure study datacenters cloud computing

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A Contribution Assessment Method Based on Model Performance Gains in Federated Learning

A Contribution Assessment Method Based on Model Performance ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zixuan Zhao Jun Liu Yuyin Li Gaoyang Liu Yamin Li Yang Yang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Federated Learning is a distributed model training system that uses the computing resources and private data of participants to collaboratively train Machine Learning models. In order to incentivize data holders to actively participate in Federated Learning, a crucial issue is how to fairly assess each participant’s contribution. One class of the mainstream methods estimates client contributions by calculating the correlation between local and global model parameters, substantially reducing the reliance on large computing resources and test datasets. However, these methods overlook an implicit key factor, leading to inaccuracy in assessing the contribution of the participants. We propose FedCA, which provides a fairness assessment of the client’s contribution based on the performance gain of the global model in each communication round. The experimental results demonstrate that FedCA accurately identifies the contributions of clients with varying data qualities and effectively approximates the optimal fairness method of Shapley values. The source code for this paper is available at https://***/paper-liu/***.

关键词： Training distributed processing Correlation Federated learning Computational modeling Source coding Data integrity distributed databases Performance gain Data models

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Plaster: an Embedded FPGA-based Cluster Orchestrator for Accelerated distributed Algorithms

Plaster: an Embedded FPGA-based Cluster Orchestrator for Acc...

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

作者： Farinelli, Lorenzo De Vincenti, Daniele Valentino Damiani, Andrea Stornaiuolo, Luca Brondolin, Rolando Santambrogio, Marco D. Sciuto, Donatella Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB Milan Italy

ISBN: (纸本)9781665435772

The increasing use of real-time data-intensive applications and the growing interest in Heterogeneous Architectures have led to the need for increasingly complex embedded computing systems. An example of this is the research carried out by both the scientific community and companies toward embedded multi-FPGA systems for the implementation of the inference phase of Convolutional Neural Networks. In this paper, we focus on optimizing the management system of these embedded FPGA-based distributed systems. We extend the state-of-the-art FARD framework to data-intensive applications in an embedded scenario. Our orchestration and management infrastructure benefits from compiled language and is accessible to end-users by the means of Python APIs, which provides a simple way to interact with the cluster and design apps to run on the embedded nodes. The proposed prototype system consists of a PYNQ-based cluster of multiple FPGAs and has been evaluated by running an FPGA-based You Only Look Once (YOLO) image classification algorithm.

关键词： multi-FPGAs Cluster Orchestrator Embedded

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distributed Multi-GPU Community Detection on Exascale Computing Platforms

Distributed Multi-GPU Community Detection on Exascale Comput...

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

作者： Naw Safrin Sattar Hao Lu Feiyi Wang Mahantesh Halappanavar Oak Ridge National Laboratory Oak Ridge TN USA Pacific Northwest National Laboratory Richland WA USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

关键词： Epidemics distributed processing Exascale computing Image edge detection Conferences Graphics processing units Partitioning algorithms

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Access Patterns and Performance Behaviors of Multi-layer Supercomputer I/O Subsystems under Production Load 22

Access Patterns and Performance Behaviors of Multi-layer Sup...

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31st international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Bez, Jean Luca Karimi, Ahmad Maroof Paul, Arnab K. Xie, Bing Byna, Suren Carns, Philip Oral, Sarp Wang, Feiyi Hanley, Jesse Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Oak Ridge Natl Lab Oak Ridge TN USA Birla Inst Technol Sci Pilani Rajasthan India Argonne Natl Lab Argonne IL USA BITS Pilani Pilani Goa India

ISBN: (纸本)9781450391993

Scientific computing workloads at HPC facilities have been shifting from traditional numerical simulations to AI/ML applications for training and inference while processing and producing ever-increasing amounts of scientific data. To address the growing need for increased storage capacity, lower access latency, and higher bandwidth, emerging technologies such as non-volatile memory are integrated into supercomputer I/O subsystems. With these emerging trends, we need a better understanding of the multilayer supercomputer I/O systems and ways to use these subsystems efficiently. In this work, we study the I/O access patterns and performance characteristics of two representative supercomputer I/O subsystems. Through an extensive analysis of year-long I/O logs on each system, we report new observations in I/O reads and writes, unbalanced use of storage system layers, and new trends in user behaviors at the HPC I/O middleware stack.

关键词： Access Patterns parallel File systems In-System Storage High-Performance Computing Production System

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