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parallel Algorithm to find Integer k where a given Well-distributed Graph is k-Metric Dimensional 1

引用

3rd international symposium on Signal and Image Processing, ISSIP 2020

作者： Chelladurai, Xavier Kureethara, Joseph Varghese Department of Sciences and Humanities Christ University Bangalore India Department of Mathematics Christ University Bangalore India

ISBN: (数字)9789813369665

ISBN: (纸本)9789813369658

Networks are very important in the world. In signal processing, the towers are modeled as nodes (vertices) and if two towers communicate, then they have an arc (edge) between them or precisely, they are adjacent. The least number of nodes in a network that can uniquely locate every node in the network is known in the network theory as the resolving set of a network. One of the properties that is used in determining the resolving set is the distance between the nodes. Two nodes are at a distance one if there is a single arc can link them whereas the distance between any two random nodes in the network is the least number of distinct arcs that can link them. We propose two algorithms in this paper with the proofs of correctness. The first one is in lines with the BFS that find distance between a designated node to every other node in the network. This algorithm runs in O(log n). The second algorithm is to identify the integer k, such that the given graph is k-metric dimensional. This can be implemented in O(log n) time with O(n2) processors in a CRCW PRAM. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： parallel algorithms

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AI for Datacenter Optimization (ADOPT'22)

AI for Datacenter Optimization (ADOPT'22)

引用

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

作者： Stephanie Brink

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance computing (HPC) centers and commercial cloud systems. This has led to a new focus on approaches to optimized resource usage, allocations and deployment of new AI frameworks. With these changes, there is a need to better understand HPC/cloud/datacenter operations with the goal of developing improved scheduling policies, identifying inefficiencies in resource utilization, energy/power consumption, failure prediction, as well as identifying policy violations. Simultaneously, there is a growing interest in addressing the increasing power requirements for AI training and inference operations in HPC/Cloud environments with the goal of minimizing the accompanying effects on climate change. There are several publicly available datasets such as the Argonne Leadership computing Facility dataset, Blue Waters System Monitoring dataset, Philly traces by Microsoft Inc., the Google Cluster Usage traces and the Atlas cluster trace repository that can enable this research. In addition, the recently released MIT Supercloud Dataset includes monitoring logs from the MIT Supercloud system which include time series of CPU and GPU usage by jobs, memory usage, file system logs, and physical monitoring data. This workshop will focus on AI/ML approaches to datacenter operations, power/energy modeling, scheduling, optimization and monitoring tools. In addition to these topics, this workshop solicits papers that address any of the challenges in enabling large-scale AI in HPC/Cloud environments, including:

关键词： Artificial intelligence Conferences Monitoring Training Optimization distributed processing Resource management

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A Green(er) World for A.I.

A Green(er) World for A.I.

引用

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

作者： Dan Zhao Nathan C. Frey Joseph McDonald Matthew Hubbell David Bestor Michael Jones Andrew Prout Vijay Gadepally Siddharth Samsi MIT Lincoln Laboratory

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

As research and practice in artificial intelligence (A.I.) grow in leaps and bounds, the resources necessary to sustain and support their operations also grow at an increasing pace. While innovations and applications from A.I. have brought significant advances, from applications to vision and natural language to improvements to fields like medical imaging and materials engineering, their costs should not be neglected. As we embrace a world with ever-increasing amounts of data as well as research & development of A.I. applications, we are sure to face an ever-mounting energy footprint to sustain these computational budgets, data storage needs, and more. But, is this sustainable and, more importantly, what kind of setting is best positioned to nurture such sustainable A.I. in both research and practice? In this paper, we outline our outlook for Green A.I.—a more sustainable, energy-efficient and energy-aware ecosystem for developing A.I. across the research, computing, and practitioner communities alike—and the steps required to arrive there. We present a bird's eye view of various areas for potential changes and improvements from the ground floor of AI's operational and hardware optimizations for datacenter/HPCs to the current incentive structures in the world of A.I. research and practice, and more. We hope these points will spur further discussion, and action, on some of these issues and their potential solutions.

关键词： Technological innovation distributed processing Natural languages Ecosystems Memory Hardware Energy efficient computing

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Practical distributed Programming in C++ 20

Practical Distributed Programming in C++

引用

29th international symposium on High-Performance parallel and distributed computing, HPDC 2020

作者： Drocco, Maurizio Castellana, Vito Giovanni Minutoli, Marco IBM TJ Watson Yorktown HeightsNY United States Pacific Northwest National Laboratory RichlandWA United States

ISBN: (纸本)9781450370523

The need for coupling high performance with productivity is steering the recent evolution of the C++ language where low-level aspects of parallel and distributed computing are now part of the standard or under discussion for inclusion. The Standard Template Library (STL) includes containers and algorithms as primary notions, coupled with execution policies that allow exploiting parallel platforms (e.g., multi-cores) on top of a well-defined operational semantics. In this work, we discuss the design of a stack for STL-compliant containers, iterators, algorithms, and execution policies targeting distributed-memory systems. Finally, we evaluate the proposed approach by analyzing the performance of our proof-of-concept implementation over a set of STL algorithms. © 2020 ACM.

关键词： Containers

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Design and Comparison of Resilient Scheduling Heuristics for parallel Jobs 34

Design and Comparison of Resilient Scheduling Heuristics for...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Benoit, Anne Le Fevre, Valentin Raghavan, Padma Robert, Yves Sun, Hongyang ENS Lyon Lab LIP Lyon France Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA Univ Tennessee Knoxville TN 37996 USA

ISBN: (纸本)9781728174457

This paper focuses on the resilient scheduling of parallel jobs on high-performance computing (HPC) platforms to minimize the overall completion time, or makespan. We revisit the classical problem while assuming that jobs are subject to transient or silent errors, and hence may need to be re-executed each time they fail to complete successfully. This work generalizes the classical framework where jobs are known offline and do not fail: in the classical framework, list scheduling that gives priority to longest jobs is known to be a 3-approximation when imposing to use shelves, and a 2-approximation without this restriction. We show that when jobs can fail, using shelves can be arbitrarily bad, but unrestricted list scheduling remains a 2-approximation. The paper focuses on the design of several heuristics, some list-based and some shelf-based, along with different priority rules and backfilling strategies. We assess and compare their performance through an extensive set of simulations, using both synthetic jobs and log traces from the Mira supercomputer.

关键词： Resilient scheduling parallel jobs silent errors list schedules shelf schedules approximation ratios

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Proceedings - 2019 IEEE 33rd international parallel and distributed Processing symposium Workshops, IPDPSW 2019

Proceedings - 2019 IEEE 33rd International Parallel and Dist...

引用

33rd IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2019

ISBN: (纸本)9781728135106

The proceedings contain 112 papers. The topics discussed include: an accurate tool for modeling, fingerprinting, comparison, and clustering of parallel applications based on performance counters;SmarTmem: intelligent management of transcendent memory in a virtualized server;data reliability and redundancy optimization of a secure multi-cloud storage under uncertainty of errors and falsifications;a portable GPU framework for SNP comparisons;towards a methodology for benchmarking edge processing frameworks;a fast local algorithm for track reconstruction on parallel architectures;towards native execution of deep learning on a leadership-class hpc system;improving robustness of heterogeneous serverless computing systems via probabilistic task pruning;and influence of tasks duration variability on task-based runtime schedulers.

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CSC: Collaborative System Configuration for I/O-Intensive Applications in Multi-Tenant Clouds

CSC: Collaborative System Configuration for I/O-Intensive Ap...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Haowei Huang Pu Pang Quan Chen Jieru Zhao Wenli Zheng Minyi Guo Department of Computer Science and Engineering Shanghai Jiao Tong University China

I/O-intensive applications are important workloads of public clouds. Multiple cloud applications co-run on the same physical machine in different virtual machines (VMs), and the shared resources (e.g., disk bandwidth) are often isolated for fairness. Our investigation shows that the performance of an I/O-intensive application is impacted by both disk bandwidth allocation and the page cache settings in the guest operating system. However, none of prior work considers adjusting the page cache settings for better performance, when the disk bandwidth allocation is adjusted. We therefore propose CSC, a system that collaboratively identifies the appropriate disk bandwidth allocation and page cache settings in the guest operating system of each VM. CSC aims to improve the system-wide I/O throughput of the physical machine, while also improve the I/O throughput of each individual I/O-intensive application in VMs. CSC comprises an online disk bandwidth allocator and an adaptive dirty page setting optimizer. The bandwidth allocator monitors the disk bandwidth utilization and re-allocates some bandwidth from free VMs to busy VMs periodically. After the re-allocation, the opti-mizer identifies the appropriate dirty page settings in the guest operating system of the VMs using Bayesian Optimization. The experimental results show that CSC improves the performance of I/O-intensive applications by 9.5 % on average (up to 17.29 %) when 5 VMs are co-located while fairness is guaranteed.

关键词： Cloud computing Operating systems Bandwidth Channel allocation Throughput Virtual machining Bayes methods

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parallel Minimum Spanning Tree Algorithms via Lattice Linear Predicate Detection

Parallel Minimum Spanning Tree Algorithms via Lattice Linear...

引用

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

作者： David R. Alves Vijay K. Garg Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

We show that the problem of computing the minimum spanning tree can be formulated as special case of detecting Lattice Linear Predicate (LLP). In general, formulating problems as LLP presents two main advantages: 1) Different problems are formulated under a single, general framework, which defines the problem in terms of simple local predicates that must hold for the all the elements of a lattice, making the problem (and the solution) compact and easy to understand. 2) improvements on one set of problems can be transferable to other sets of problems; 3 $\bigcirc\!\!\!\!\!{6}$ since the problems are stated as a set of local predicates, which can be often tested with little or no synchronization it is often the case that new opportunities for parallelism present themselves. In this paper we introduce two parallel algorithms LLP-Prim and LLP-Boruvka that improve on the non-LLP counterparts in several ways. LLP-Prim reduces the number of heap operations required by Prim by allowing edges to be selected without entering the heap thus allowing for parallelism. LLP-Boruvka improves on Boruvka by reducing synchronization and thus once more improving parallelism opportunities. Our experimental evaluation shows that LLP-Prim is faster than Prim's algorithm in both single threaded and multithreaded scenarios and that it provides a good tradeoff between parallelism and efficiency at low core counts. For higher core count scenarios we show how LLP-Boruvka improves on an efficient implementation of a parallel version of Boruvka.

关键词： distributed processing Instruction sets Conferences Lattices Synchronization parallel algorithms

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APENAS: An Asynchronous parallel Evolution Based Multi-objective Neural Architecture Search 18

APENAS: An Asynchronous Parallel Evolution Based Multi-objec...

引用

18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Hu, Mengtao Liu, Li Wang, Wei Liu, Yao East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781665414852

Machine learning is widely used in pattern classification, image processing and speech recognition. Neural architecture search (NAS) could reduce the dependence of human experts on machine learning effectively. Due to the high complexity of NAS, the tradeoff between time consumption and classification accuracy is vital. This paper presents APENAS, an asynchronous parallel evolution based multi-objective neural architecture search, using the classification accuracy and the number of parameters as objectives, encoding the network architectures as individuals. To make full use of computing resource, we propose a multi-generation undifferentiated fusion scheme to achieve asynchronous parallel evolution on multiple GPUs or CPUs, which speeds up the process of NAS. Accordingly, we propose an election pool and a buffer pool for two-layer filtration of individuals. The individuals are sorted in the election pool by non-dominated sorting and filtered in the buffer pool by the roulette algorithm to improve the elitism of the Pareto front. APENAS is evaluated on the CIFAR-10 and CIFAR-100 datasets [25]. The experimental results demonstrate that APENAS achieves 90.05% accuracy on CIFAR-10 with only 0.07 million parameters, which is comparable to state of the art. Especially, APENAS has high parallel scalability, achieving 92.5% parallel efficiency on 64 nodes.

关键词： automated machine learning neural architecture search multi-objective asynchronous parallel evolution

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Brief announcement: Memory efficient massively parallel algorithms for LCL problems on trees 35

Brief announcement: Memory efficient massively parallel algo...

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35th international symposium on distributed computing, DISC 2021

作者： Brandt, Sebastian Latypov, Rustam Uitto, Jara ETH Zürich Switzerland Aalto University Finland

ISBN: (纸本)9783959772105

We establish scalable Massively parallel Computation (MPC) algorithms for a family of fundamental graph problems on trees. We give a general method that, for a wide range of LCL problems, turns their message passing counterparts into exponentially faster algorithms in the sublinear MPC model. In particular, we show that any LCL on trees that has a deterministic complexity of O(n) in the LOCAL model can be sped up to O(log n) (high-complexity regime) in the sublinear MPC model and similarly no(1) to O(log log n) (intermediate-complexity regime). We emphasize, that we work on bounded degree trees and all of our algorithms work in the sublinear MPC model, where local memory is O(nδ) for δ o(1) requires Θ(log log n) deterministic time in the sublinear MPC model when using a natural family of component-stable algorithms. © Sebastian Brandt, Rustam Latypov, and Jara Uitto;licensed under Creative Commons License CC-BY 4.0

关键词： Message passing

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