当前期刊: arXiv - CS - Distributed, Parallel, and Cluster Computing Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
我的关注
我的收藏
您暂时未登录!
登录
  • TaskTorrent: a Lightweight Distributed Task-Based Runtime System in C++
    arXiv.cs.DC Pub Date : 2020-09-22
    Léopold Cambier; Yizhou Qian; Eric Darve

    We present TaskTorrent, a lightweight distributed task-based runtime in C++. TaskTorrent uses a parametrized task graph to express the task DAG, and one-sided active messages to trigger remote tasks asynchronously. As a result the task DAG is completely distributed and discovered in parallel. It is a C++14 library and only depends on MPI. We explain the API and the implementation. We perform a series

    更新日期:2020-09-23
  • A Formally Verified Protocol for Log Replication with Byzantine Fault Tolerance
    arXiv.cs.DC Pub Date : 2020-09-22
    Joel Wanner; Laurent Chuat; Adrian Perrig

    Byzantine fault tolerant protocols enable state replication in the presence of crashed, malfunctioning, or actively malicious processes. Designing such protocols without the assistance of verification tools, however, is remarkably error-prone. In an adversarial environment, performance and flexibility come at the cost of complexity, making the verification of existing protocols extremely difficult

    更新日期:2020-09-23
  • E-BATCH: Energy-Efficient and High-Throughput RNN Batching
    arXiv.cs.DC Pub Date : 2020-09-22
    Franyell Silfa; Jose Maria Arnau; Antonio Gonzalez

    Recurrent Neural Network (RNN) inference exhibits low hardware utilization due to the strict data dependencies across time-steps. Batching multiple requests can increase throughput. However, RNN batching requires a large amount of padding since the batched input sequences may largely differ in length. Schemes that dynamically update the batch every few time-steps avoid padding. However, they require

    更新日期:2020-09-23
  • A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review
    arXiv.cs.DC Pub Date : 2020-09-14
    Fauzi Adi Rafrastara; Qi Deyu

    Context: Data Mining (DM) method has been evolving year by year and as of today there is also the enhancement of DM technique that can be run several times faster than the traditional one, called Distributed Data Mining (DDM). It is not a new field in data processing actually, but in the recent years many researchers have been paying more attention on this area. Problems: The number of publication

    更新日期:2020-09-23
  • MockFog 2.0: Automated Execution of Fog Application Experiments in the Cloud
    arXiv.cs.DC Pub Date : 2020-09-22
    Jonathan Hasenburg; Martin Grambow; David Bermbach

    Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing and benchmarking fog applications, however, is hard since runtime infrastructure will typically be in use or may not exist, yet. In this paper, we propose an approach that emulates such infrastructure in the cloud. Developers can freely design

    更新日期:2020-09-23
  • The Ultimate DataFlow for Ultimate SuperComputers-on-a-Chips
    arXiv.cs.DC Pub Date : 2020-09-20
    Veljko Milutinovic; Milos Kotlar; Ivan Ratkovic; Nenad Korolija; Miljan Djordjevic; Kristy Yoshimoto; Erik Klem; Mateo Valero

    This article starts from the assumption that near future 100BTransistor SuperComputers-on-a-Chip will include N big multi-core processors, 1000N small many-core processors, a TPU-like fixed-structure systolic array accelerator for the most frequently used Machine Learning algorithms needed in bandwidth-bound applications and a flexible-structure reprogrammable accelerator for less frequently used Machine

    更新日期:2020-09-23
  • A Fuzzy Logic Controller for Tasks Scheduling Using Unreliable Cloud Resources
    arXiv.cs.DC Pub Date : 2020-09-22
    Panagiotis Oikonomou; Kostas Kolomvatsos; Nikos Tziritas; Georgios Theodoropoulos; Thanasis Loukopoulos; Georgios Stamoulis

    The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great

    更新日期:2020-09-23
  • A reduced-precision streaming SpMV architecture for Personalized PageRank on FPGA
    arXiv.cs.DC Pub Date : 2020-09-22
    Alberto Parravicini; Francesco Sgherzi; Marco D. Santambrogio

    Sparse matrix-vector multiplication is often employed in many data-analytic workloads in which low latency and high throughput are more valuable than exact numerical convergence. FPGAs provide quick execution times while offering precise control over the accuracy of the results thanks to reduced-precision fixed-point arithmetic. In this work, we propose a novel streaming implementation of Coordinate

    更新日期:2020-09-23
  • Dynamic Fusion based Federated Learning for COVID-19 Detection
    arXiv.cs.DC Pub Date : 2020-09-22
    Weishan Zhang; Tao Zhou; Qinghua Lu; Xiao Wang; Chunsheng Zhu; Zhipeng Wang; Feiyue Wang

    Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is expected to be an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients' privacy, which causes the issue of insufficient datasets for training the image classification model. Federated learning is

    更新日期:2020-09-23
  • A Constraint Programming-based Job Dispatcher for Modern HPC Systems and Applications
    arXiv.cs.DC Pub Date : 2020-09-22
    Cristian Galleguillos; Zeynep Kiziltan; Ricardo Soto

    Constraint Programming (CP) is a well-established area in AI as a programming paradigm for modelling and solving discrete optimization problems, and it has been been successfully applied to tackle the on-line job dispatching problem in HPC systems including those running modern applications. The limitations of the available CP-based job dispatchers may hinder their practical use in today's systems

    更新日期:2020-09-23
  • Continuous Reasoning for Managing Next-Gen Distributed Applications
    arXiv.cs.DC Pub Date : 2020-09-22
    Stefano FortiDepartment of Computer Science, University of Pisa, Italy; Antonio BrogiDepartment of Computer Science, University of Pisa, Italy

    Continuous reasoning has proven effective in incrementally analysing changes in application codebases within Continuous Integration/Continuous Deployment (CI/CD) software release pipelines. In this article, we present a novel declarative continuous reasoning approach to support the management of multi-service applications over the Cloud-IoT continuum, in particular when infrastructure variations impede

    更新日期:2020-09-23
  • Asynchronous Distributed Optimization with Randomized Delays
    arXiv.cs.DC Pub Date : 2020-09-22
    Margalit Glasgow; Mary Wootters

    In this work, we study asynchronous finite sum minimization in a distributed-data setting with a central parameter server. While asynchrony is well understood in parallel settings where the data is accessible by all machines, little is known for the distributed-data setting. We introduce a variant of SAGA called ADSAGA for the distributed-data setting where each machine stores a partition of the data

    更新日期:2020-09-23
  • Connecting Distributed Pockets of EnergyFlexibility through Federated Computations:Limitations and Possibilities
    arXiv.cs.DC Pub Date : 2020-09-21
    Javad Mohammadi; Jesse Thornburg

    Electric grids are traditionally operated as multi-entity systems with each entity managing a geographical region. Interest and demand for decarbonization and energy democratization is resulting in growing penetration of controllable energy resources. In turn, this process is increasing the number of grid entities. The paradigm shift is also fueled by increased adoption of intelligent sensors and actuators

    更新日期:2020-09-23
  • When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
    arXiv.cs.DC Pub Date : 2020-09-22
    Shuai Yu; Xu Chen; Zhi Zhou; Xiaowen Gong; Di Wu

    Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e.g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes

    更新日期:2020-09-23
  • Deep N-ary Error Correcting Output Codes
    arXiv.cs.DC Pub Date : 2020-09-22
    Hao Zhang; Joey Tianyi Zhou; Tianying Wang; Ivor W. Tsang; Rick Siow Mong Goh

    Ensemble learning consistently improves the performance of multi-class classification through aggregating a series of base classifiers. To this end, data-independent ensemble methods like Error Correcting Output Codes (ECOC) attract increasing attention due to its easiness of implementation and parallelization. Specifically, traditional ECOCs and its general extension N-ary ECOC decompose the original

    更新日期:2020-09-23
  • Resilient Cloud-based Replication with Low Latency
    arXiv.cs.DC Pub Date : 2020-09-21
    Michael Eischer; Tobias Distler

    Existing approaches to tolerate Byzantine faults in geo-replicated environments require systems to execute complex agreement protocols over wide-area links and consequently are often associated with high response times. In this paper we address this problem with Spider, a resilient replication architecture for geo-distributed systems that leverages the availability characteristics of today's public-cloud

    更新日期:2020-09-22
  • A FaaS File System for Serverless Computing
    arXiv.cs.DC Pub Date : 2020-09-16
    Johann Schleier-Smith; Leonhard Holz; Nathan Pemberton; Joseph M. Hellerstein

    Serverless computing with cloud functions is quickly gaining adoption, but constrains programmers with its limited support for state management. We introduce a shared file system for cloud functions. It offers familiar POSIX semantics while taking advantage of distinctive aspects of cloud functions to achieve scalability and performance beyond what traditional shared file systems can offer. We take

    更新日期:2020-09-22
  • ServiceNet: A P2P Service Network
    arXiv.cs.DC Pub Date : 2020-09-04
    Ji LiuSchool of Electrical & Information Engineering, University of Sydney, Australia; Hang ZhaoSchool of Electrical & Information Engineering, University of Sydney, Australia; Jiyuan YangSchool of Electrical & Information Engineering, University of Sydney, Australia; Yu ShiSchool of Electrical & Information Engineering, University of Sydney, Australia; Ruichang LiuSchool of Electrical & Information

    Given a large number of online services on the Internet, from time to time, people are still struggling to find out the services that they need. On the other hand, when there are considerable research and development on service discovery and service recommendation, most of the related work are centralized and thus suffers inherent shortages of the centralized systems, e.g., adv-driven, lack at trust

    更新日期:2020-09-22
  • Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles
    arXiv.cs.DC Pub Date : 2020-09-21
    Soohyun Park; Jeman Park; David Mohaisen; Joongheon Kim

    In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. The proposed algorithm is designed under the consideration of delay, energy, and overflow as optimizations to ensure real-time properties while striking a balance for other environment-related parameters. The merit of the proposed

    更新日期:2020-09-22
  • VirtualFlow: Decoupling Deep Learning Model Execution from Underlying Hardware
    arXiv.cs.DC Pub Date : 2020-09-20
    Andrew Or; Haoyu Zhang; Michael J. Freedman

    State-of-the-art deep learning systems tightly couple model execution with the underlying hardware. This coupling poses important challenges in a world where the scale of deep learning workloads is growing rapidly: workloads with high resource requirements are inaccessible to most users, experimentation on smaller test beds is impossible, and results are difficult to reproduce across different hardware

    更新日期:2020-09-22
  • A General Framework for the Security Analysis of Blockchain Protocols
    arXiv.cs.DC Pub Date : 2020-09-20
    Andrew Lewis-Pye; Tim Roughgarden

    Blockchain protocols differ in fundamental ways, including the mechanics of selecting users to produce blocks (e.g., proof-of-work vs. proof-of-stake) and the method to establish consensus (e.g., longest chain rules vs. Byzantine fault-tolerant (BFT) inspired protocols). These fundamental differences have hindered "apples-to-apples" comparisons between different categories of blockchain protocols and

    更新日期:2020-09-22
  • C-SAW: A Framework for Graph Sampling and Random Walk on GPUs
    arXiv.cs.DC Pub Date : 2020-09-18
    Santosh Pandey; Lingda Li; Adolfy Hoisie; Xiaoye S. Li; Hang Liu

    Many applications require to learn, mine, analyze and visualize large-scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing technologies. Many applications have requirements to analyze, transform, visualize and learn large scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing technologies

    更新日期:2020-09-22
  • Force2Vec: Parallel force-directed graph embedding
    arXiv.cs.DC Pub Date : 2020-09-17
    Md. Khaledur Rahman; Majedul Haque Sujon; Ariful Azad

    A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph. While graph embedding is fundamentally related to graph visualization, prior work did not exploit this connection explicitly. We develop Force2Vec that uses force-directed graph layout models in a graph embedding setting with an aim to excel in both machine

    更新日期:2020-09-22
  • Towards application-specific query processing systems
    arXiv.cs.DC Pub Date : 2020-09-21
    Dimitrios VasilasDELYS, SU; Marc ShapiroDELYS, SU; Bradley KingDELYS, SU; Sara HamoudaDELYS, SU

    Database systems use query processing subsystems for enabling efficient query-based data retrieval. An essential aspect of designing any query-intensive application is tuning the query system to fit the application's requirements and workload characteristics. However, the configuration parameters provided by traditional database systems do not cover the design decisions and trade-offs that arise from

    更新日期:2020-09-22
  • The Complexity Landscape of Distributed Locally Checkable Problems on Trees
    arXiv.cs.DC Pub Date : 2020-09-21
    Yi-Jun Chang

    Recent research revealed the existence of gaps in the complexity landscape of locally checkable labeling (LCL) problems in the LOCAL model of distributed computing. For example, the deterministic round complexity of any LCL problem on bounded-degree graphs is either $O(\log^\ast n)$ or $\Omega(\log n)$ [Chang, Kopelowitz, and Pettie, FOCS 2016]. The complexity landscape of LCL problems is now quite

    更新日期:2020-09-22
  • Resilient Composition of Drone Services for Delivery
    arXiv.cs.DC Pub Date : 2020-09-21
    Babar ShahzaadThe University of Sydney, Sydney NSW 2000, Australia; Athman BouguettayaThe University of Sydney, Sydney NSW 2000, Australia; Sajib MistryCurtin University, Perth WA 6102, Australia; Azadeh Ghari NeiatDeakin University, Geelong VIC 3220, Australia

    We propose a novel resilient drone service composition framework for delivery in dynamic weather conditions. We use a skyline approach to select an optimal set of candidate drone services at the source node in a skyway network. Drone services are initially composed using a novel constraint-aware deterministic lookahead algorithm using the multi-armed bandit tree exploration. We propose a heuristic-based

    更新日期:2020-09-22
  • Sparse Communication for Training Deep Networks
    arXiv.cs.DC Pub Date : 2020-09-19
    Negar Foroutan Eghlidi; Martin Jaggi

    Synchronous stochastic gradient descent (SGD) is the most common method used for distributed training of deep learning models. In this algorithm, each worker shares its local gradients with others and updates the parameters using the average gradients of all workers. Although distributed training reduces the computation time, the communication overhead associated with the gradient exchange forms a

    更新日期:2020-09-22
  • C-Balancer: A System for Container Profiling and Scheduling
    arXiv.cs.DC Pub Date : 2020-09-18
    Akshay DhumalIIT Madras; Dharanipragada JanakiramIIT Madras

    Linux containers have gained high popularity in recent times. This popularity is significantly due to various advantages of containers over Virtual Machines (VM). The containers are lightweight, occupy lesser storage, have fast boot-up time, easy to deploy and have faster auto-scaling. The key reason behind the popularity of containers is that they leverage the mechanism of micro-service style software

    更新日期:2020-09-21
  • Approximate Majority With Catalytic Inputs
    arXiv.cs.DC Pub Date : 2020-09-18
    Talley Amir; James Aspnes; John Lazarsfeld

    Third-state dynamics (Angluin et al. 2008; Perron et al. 2009) is a well-known process for quickly and robustly computing approximate majority through interactions between randomly-chosen pairs of agents. In this paper, we consider this process in a new model with persistent-state catalytic inputs, as well as in the presence of transient leak faults. Based on models considered in recent protocols for

    更新日期:2020-09-21
  • Prisoners, Rooms, and Lightswitches
    arXiv.cs.DC Pub Date : 2020-09-18
    Daniel M. Kane; Scott Duke Kominers

    We examine a new variant of the classic prisoners and lightswitches puzzle: A warden leads his $n$ prisoners in and out of $r$ rooms, one at a time, in some order, with each prisoner eventually visiting every room an arbitrarily large number of times. The rooms are indistinguishable, except that each one has $s$ lightswitches; the prisoners win their freedom if at some point a prisoner can correctly

    更新日期:2020-09-21
  • Building Containerized Environments for Reproducibility and Traceability of Scientific Workflows
    arXiv.cs.DC Pub Date : 2020-09-17
    Paula Olaya; Jay Lofstead; Michela Taufer

    Scientists rely on simulations to study natural phenomena. Trusting the simulation results is vital to develop sciences in any field. One approach to build trust is to ensure the reproducibility and traceability of the simulations through the annotation of executions at the system-level; by the generation of record trails of data moving through the simulation workflow. In this work, we present a system-level

    更新日期:2020-09-21
  • Federated Learning with Nesterov Accelerated Gradient Momentum Method
    arXiv.cs.DC Pub Date : 2020-09-18
    Zhengjie Yang; Wei Bao; Dong Yuan; Nguyen H. Tran; Albert Y. Zomaya

    Federated learning (FL) is a fast-developing technique that allows multiple workers to train a global model based on a distributed dataset. Conventional FL employs gradient descent algorithm, which may not be efficient enough. It is well known that Nesterov Accelerated Gradient (NAG) is more advantageous in centralized training environment, but it is not clear how to quantify the benefits of NAG in

    更新日期:2020-09-21
  • Extending SLURM for Dynamic Resource-Aware Adaptive Batch Scheduling
    arXiv.cs.DC Pub Date : 2020-09-16
    Mohak Chadha; Jophin John; Michael Gerndt

    With the growing constraints on power budget and increasing hardware failure rates, the operation of future exascale systems faces several challenges. Towards this, resource awareness and adaptivity by enabling malleable jobs has been actively researched in the HPC community. Malleable jobs can change their computing resources at runtime and can significantly improve HPC system performance. However

    更新日期:2020-09-20
  • Finding Subgraphs in Highly Dynamic Networks
    arXiv.cs.DC Pub Date : 2020-09-17
    Keren Censor-Hillel; Victor I. Kolobov; Gregory Schwartzman

    In this paper we consider the fundamental problem of finding subgraphs in highly dynamic distributed networks - networks which allow an arbitrary number of links to be inserted / deleted per round. We show that the problems of $k$-clique membership listing (for any $k\geq 3$), 4-cycle listing and 5-cycle listing can be deterministically solved in $O(1)$-amortized round complexity, even with limited

    更新日期:2020-09-20
  • Exploration of Fine-Grained Parallelism for Load Balancing Eager K-truss on GPU and CPU
    arXiv.cs.DC Pub Date : 2020-09-16
    Mark Blanco; Tze Meng Low; Kyungjoo Kim

    In this work we present a performance exploration on Eager K-truss, a linear-algebraic formulation of the K-truss graph algorithm. We address performance issues related to load imbalance of parallel tasks in symmetric, triangular graphs by presenting a fine-grained parallel approach to executing the support computation. This approach also increases available parallelism, making it amenable to GPU execution

    更新日期:2020-09-20
  • Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing
    arXiv.cs.DC Pub Date : 2020-09-17
    Tayyebeh Jahani-Nezhad; Mohammad Ali Maddah-Ali

    One of the major challenges in using distributed learning to train complicated models with large data sets is to deal with stragglers effect. As a solution, coded computation has been recently proposed to efficiently add redundancy to the computation tasks. In this technique, coding is used across data sets, and computation is done over coded data, such that the results of an arbitrary subset of worker

    更新日期:2020-09-20
  • Serverless Applications: Why, When, and How?
    arXiv.cs.DC Pub Date : 2020-09-17
    Simon Eismann; Joel Scheuner; Erwin van Eyk; Maximilian Schwinger; Johannes Grohmann; Cristina L. Abad; Alexandru Iosup

    Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as 'Why do so many companies adopt serverless?', 'When are serverless applications well suited?', and 'How are serverless applications currently implemented?' To address these questions, we analyze 89 serverless applications from open-source

    更新日期:2020-09-20
  • WarpCore: A Library for fast Hash Tables on GPUs
    arXiv.cs.DC Pub Date : 2020-09-16
    Daniel JüngerJohannes Gutenberg University; Robin KobusJohannes Gutenberg University; André MüllerJohannes Gutenberg University; Christian HundtNVIDIA AI Technology Center; Kai XuShandong University; Weiguo LiuShandong University; Bertil SchmidtJohannes Gutenberg University

    Hash tables are ubiquitous. Properties such as an amortized constant time complexity for insertion and querying as well as a compact memory layout make them versatile associative data structures with manifold applications. The rapidly growing amount of data emerging in many fields motivated the need for accelerated hash tables designed for modern parallel architectures. In this work, we exploit the

    更新日期:2020-09-20
  • Large-Scale Intelligent Microservices
    arXiv.cs.DC Pub Date : 2020-09-17
    Mark Hamilton; Nick Gonsalves; Christina Lee; Anand Raman; Brendan Walsh; Siddhartha Prasad; Dalitso Banda; Lucy Zhang; Lei Zhang; William T. Freeman

    Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with their own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services

    更新日期:2020-09-20
  • Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices
    arXiv.cs.DC Pub Date : 2020-09-16
    Boro Sofranac; Ambros Gleixner; Sebastian Pokutta

    Fast domain propagation of linear constraints has become a crucial component of today's best algorithms and solvers for mixed integer programming and pseudo-boolean optimization to achieve peak solving performance. Irregularities in the form of dynamic algorithmic behaviour, dependency structures, and sparsity patterns in the input data make efficient implementations of domain propagation on GPUs and

    更新日期:2020-09-18
  • PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems
    arXiv.cs.DC Pub Date : 2020-09-16
    Anirban Ghose; Siddharth Singh; Vivek Kulaharia; Lokesh Dokara; Srijeeta Maity; Soumyajit Dey

    In the past decade, high performance compute capabilities exhibited by heterogeneous GPGPU platforms have led to the popularity of data parallel programming languages such as CUDA and OpenCL. Such languages, however, involve a steep learning curve as well as developing an extensive understanding of the underlying architecture of the compute devices in heterogeneous platforms. This has led to the emergence

    更新日期:2020-09-18
  • A New Perspective of Graph Data and A Generic and Efficient Method for Large Scale Graph Data Traversal
    arXiv.cs.DC Pub Date : 2020-09-16
    Chenglong Zhang

    The BFS algorithm is a basic graph data processing algorithm and many other graph data processing algorithms have similar architectural features with BFS algorithm and can be built on the basis of BFS algorithm model. We analyze the differences between graph algorithms and traditional high-performance algorithms in detail, propose a new way of classifying algorithms into data independent algorithm

    更新日期:2020-09-18
  • High-Performance Mining of COVID-19 Open Research Datasets for Text Classification and Insights in Cloud Computing Environments
    arXiv.cs.DC Pub Date : 2020-09-16
    Jie Zhao; Maria A. Rodriguez; Rajkumar Buyya

    COVID-19 global pandemic is an unprecedented health crisis. Since the outbreak, many researchers around the world have produced an extensive collection of literatures. For the research community and the general public to digest, it is crucial to analyse the text and provide insights in a timely manner, which requires a considerable amount of computational power. Clouding computing has been widely adopted

    更新日期:2020-09-18
  • GPU Accelerated RIS-based Influence Maximization Algorithm
    arXiv.cs.DC Pub Date : 2020-09-15
    Soheil Shahrouz; Saber Salehkaleybar; Matin Hashemi

    Given a social network modeled as a weighted graph $G$, the influence maximization problem seeks $k$ vertices to become initially influenced, to maximize the expected number of influenced nodes under a particular diffusion model. The influence maximization problem has been proven to be NP-hard, and most proposed solutions to the problem are approximate greedy algorithms, which can guarantee a tunable

    更新日期:2020-09-18
  • Immutable Log Storage as a Service on Private and Public Blockchains
    arXiv.cs.DC Pub Date : 2020-09-16
    William Pourmajidi; Lei Zhang; John Steinbacher; Tony Erwin; Andriy Miranskyy

    During the normal operation of a Cloud solution, no one pays attention to the logs except the system reliability engineers, who may periodically check them to ensure that the Cloud platform's performance conforms to the Service Level Agreements (SLA). However, the moment a component fails, or a customer complains about a breach of SLA, the importance of logs increases significantly. All departments

    更新日期:2020-09-18
  • tinyMD: A Portable and Scalable Implementation for Pairwise Interactions Simulations
    arXiv.cs.DC Pub Date : 2020-09-16
    Rafael Ravedutti L. MachadoChair for System Simulation at University of Erlangen-Nürnberg; Jonas SchmittChair for System Simulation at University of Erlangen-Nürnberg; Sebastian EiblChair for System Simulation at University of Erlangen-Nürnberg; Jan EitzingerRegional Computer Center Erlangen at University of Erlangen-Nürnberg; Roland LeißaSaarland Informatics Campus at Saarland University; Sebastian

    This paper investigates the suitability of the AnyDSL partial evaluation framework to implement tinyMD: an efficient, scalable, and portable simulation of pairwise interactions among particles. We compare tinyMD with the miniMD proxy application that scales very well on parallel supercomputers. We discuss the differences between both implementations and contrast miniMD's performance for single-node

    更新日期:2020-09-18
  • Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes
    arXiv.cs.DC Pub Date : 2020-09-15
    Mert Hidayetoglu; Tekin Bicer; Simon Garcia de Gonzalo; Bin Ren; Vincent De Andrade; Doga Gursoy; Raj Kettimuthu; Ian T. Foster; Wen-mei W. Hwu

    X-ray computed tomography is a commonly used technique for noninvasive imaging at synchrotron facilities. Iterative tomographic reconstruction algorithms are often preferred for recovering high quality 3D volumetric images from 2D X-ray images, however, their use has been limited to small/medium datasets due to their computational requirements. In this paper, we propose a high-performance iterative

    更新日期:2020-09-16
  • Term Rewriting on GPUs
    arXiv.cs.DC Pub Date : 2020-09-15
    Johri van Eerd; Jan Friso Groote; Pieter Hijma; Jan Martens; Anton Wijs

    We present a way to implement term rewriting on a GPU. We do this by letting the GPU repeatedly perform a massively parallel evaluation of all subterms. We find that if the term rewrite systems exhibit sufficient internal parallelism, GPU rewriting substantially outperforms the CPU. Since we expect that our implementation can be further optimized, and because in any case GPUs will become much more

    更新日期:2020-09-16
  • TardiS: Migrating Containers with RDMA Networks
    arXiv.cs.DC Pub Date : 2020-09-15
    Maksym PlanetaTU Dresden; Jan BierbaumTU Dresden; Leo Sahaya Daphne AntonyAMOLF; Torsten HoeflerETH Zurich; Hermann HärtigTU Dresden

    Major data centre providers are introducing RDMA-based networks for their tenants, as well as for operating the underlying infrastructure. In comparison to traditional socket-based network stacks, RDMA-based networks offer higher throughput, lower latency and reduced CPU overhead. However, transparent checkpoint and migration operations become much more difficult. The key reason is that the OS is removed

    更新日期:2020-09-16
  • NextDoor: GPU-Based Graph Sampling for GraphMachine Learning
    arXiv.cs.DC Pub Date : 2020-09-14
    Abhinav Jangda; Sandeep Polisetty; Arjun Guha; Marco Serafini

    Representation learning is a fundamental task in machine learning. It consists of learning the features of data items automatically, typically using a deep neural network (DNN), instead of selecting hand-engineered features that typically have worse performance. Graph data requires specific algorithms for representation learning such as DeepWalk, node2vec, and GraphSAGE. These algorithms first sample

    更新日期:2020-09-16
  • Performance Evaluation of Linear Regression Algorithm in Cluster Environment
    arXiv.cs.DC Pub Date : 2020-09-14
    Cinantya Paramita; Fauzi Adi Rafrastara; Usman Sudibyo; R. I. W. Agung Wibowo

    Cluster computing was introduced to replace the superiority of super computers. Cluster computing is able to overcome the problems that cannot be effectively dealt with supercomputers. In this paper, we are going to evaluate the performance of cluster computing by executing one of data mining techniques in the cluster environment. The experiment will attempt to predict the flight delay by using linear

    更新日期:2020-09-15
  • OneStopTuner: An End to End Architecture for JVM Tuning of Spark Applications
    arXiv.cs.DC Pub Date : 2020-09-07
    Venktesh V; Pooja B Bindal; Devesh Singhal; A V Subramanyam; Vivek Kumar

    Java is the backbone of widely used big data frameworks, such as Apache Spark, due to its productivity, portability from JVM-based execution, and support for a rich set of libraries. However, the performance of these applications can widely vary depending on the runtime flags chosen out of all existing JVM flags. Manually tuning these flags is both cumbersome and error-prone. Automated tuning approaches

    更新日期:2020-09-15
  • Four Shades of Deterministic Leader Election in Anonymous Networks
    arXiv.cs.DC Pub Date : 2020-09-14
    Barun Gorain; Avery Miller; Andrzej Pelc

    Leader election is one of the fundamental problems in distributed computing: a single node, called the leader, must be specified. This task can be formulated either in a weak way, where one node outputs 'leader' and all other nodes output 'non-leader', or in a strong way, where all nodes must also learn which node is the leader. If the nodes of the network have distinct identifiers, then such an agreement

    更新日期:2020-09-15
  • Analyzing Performance Properties Collected by the PerSyst Scalable HPC Monitoring Tool
    arXiv.cs.DC Pub Date : 2020-09-13
    David Brayford; Christoph Bernau; Wolfram Hesse; Carla Guillen

    The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify: execution patterns, possible code optimizations and improvements to the system monitoring. We also identify candidates for employing machine learning techniques

    更新日期:2020-09-15
  • From Federated Learning to Federated Neural Architecture Search: A Survey
    arXiv.cs.DC Pub Date : 2020-09-12
    Hangyu Zhu; Haoyu Zhang; Yaochu Jin

    Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture

    更新日期:2020-09-15
  • Terminating cases of flooding
    arXiv.cs.DC Pub Date : 2020-09-12
    Walter Hussak; Amitabh Trehan

    Basic synchronous flooding proceeds in rounds. Given a finite undirected (network) graph $G$, a set of sources $I \subseteq G$ initiate flooding in the first round by every node in $I$ sending the same message to all of its neighbours. In each subsequent round, nodes send the message to all of their neighbours from which they did not receive the message in the previous round. Flooding terminates when

    更新日期:2020-09-15
  • Statically Verified Refinements for Multiparty Protocols
    arXiv.cs.DC Pub Date : 2020-09-14
    Fangyi ZhouImperial College London; Francisco FerreiraImperial College London; Raymond HuUniversity of Hertfordshire; Rumyana NeykovaBrunel University London; Nobuko YoshidaImperial College London

    With distributed computing becoming ubiquitous in the modern era, safe distributed programming is an open challenge. To address this, multiparty session types (MPST) provide a typing discipline for message-passing concurrency, guaranteeing communication safety properties such as deadlock freedom. While originally MPST focus on the communication aspects, and employ a simple typing system for communication

    更新日期:2020-09-15
  • Communication-efficient Decentralized Machine Learning over Heterogeneous Networks
    arXiv.cs.DC Pub Date : 2020-09-12
    Pan Zhou; Qian Lin; Dumitrel Loghin; Beng Chin Ooi; Yuncheng Wu; Hongfang Yu

    In the last few years, distributed machine learning has been usually executed over heterogeneous networks such as a local area network within a multi-tenant cluster or a wide area network connecting data centers and edge clusters. In these heterogeneous networks, the link speeds among worker nodes vary significantly, making it challenging for state-of-the-art machine learning approaches to perform

    更新日期:2020-09-15
  • Computer-Aided Generation of N-shift RWS
    arXiv.cs.DC Pub Date : 2020-09-11
    Benjamin Bolling

    Generating schedules for shift workers is essential for many employers, whether the employer is a small or a large industrial complex, research laboratory, or other businesses involving shift works. Previous methods for creating rotational workforce schedules included interactions between the schedule maker and the algorithm, including defining the length of sequences of consecutive days of working

    更新日期:2020-09-15
  • EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition
    arXiv.cs.DC Pub Date : 2020-09-14
    Chengyu Wang; Mengli Cheng; Xu Hu; Jun Huang

    We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing audio data at scale. Our platform is built upon the Machine Learning Platform for AI of Alibaba Cloud. Its main functionality is to support efficient learning and inference for end-to-end ASR models on distributed GPU clusters

    更新日期:2020-09-15
Contents have been reproduced by permission of the publishers.
导出
全部期刊列表>>
物理学研究前沿热点精选期刊推荐
chemistry
《自然》编辑与您分享如何成为优质审稿人-信息流
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
自然职场线上招聘会
ACS ES&T Engineering
ACS ES&T Water
ACS Publications填问卷
屿渡论文,编辑服务
阿拉丁试剂right
南昌大学
王辉
南方科技大学
刘天飞
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
X-MOL
苏州大学
廖矿标
深圳湾
试剂库存
down
wechat
bug