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  • Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity
    arXiv.cs.DC Pub Date : 2020-06-30
    Min Li; Yulong Ao; Chao Yang

    Despite numerous efforts for optimizing the performance of Sparse Matrix and Vector Multiplication (SpMV) on modern hardware architectures, few works are done to its sparse counterpart, Sparse Matrix and Sparse Vector Multiplication (SpMSpV), not to mention dealing with input vectors of varied sparsity. The key challenge is that depending on the sparsity levels, distribution of data, and compute platform

    更新日期:2020-07-01
  • Revisiting Asynchronous Fault Tolerant Computation with Optimal Resilience
    arXiv.cs.DC Pub Date : 2020-06-30
    Ittai Abraham; Danny Dolev; Gilad Stern

    The celebrated result of Fischer, Lynch and Paterson is the fundamental lower bound for asynchronous fault tolerant computation: any 1-crash resilient asynchronous agreement protocol must have some (possibly measure zero) probability of not terminating. In 1994, Ben-Or, Kelmer and Rabin published a \textit{proof-sketch} of a lesser known lower bound for asynchronous fault tolerant computation with

    更新日期:2020-07-01
  • Extending the OpenCHK Model with Advanced Checkpoint Features
    arXiv.cs.DC Pub Date : 2020-06-30
    Marcos Maroñas; Sergi Mateo; Kai Keller; Leonardo Bautista-Gomez; Eduard Ayguadé; Vicenç Beltran

    One of the major challenges in using extreme scale systems efficiently is to mitigate the impact of faults. Application-level checkpoint/restart (CR) methods provide the best trade-off between productivity, robustness, and performance. There are many solutions implementing CR at the application level. They all provide advanced I/O capabilities to minimize the overhead introduced by CR. Nevertheless

    更新日期:2020-07-01
  • Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs
    arXiv.cs.DC Pub Date : 2020-06-30
    Ang Li; Simon Su

    Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1%) when executing BNNs. Consequently

    更新日期:2020-07-01
  • Parallel Betweenness Computation in Graph Database for Contingency Selection
    arXiv.cs.DC Pub Date : 2020-06-29
    Yongli Zhu; Renchang Dai; Guangyi Liu

    Parallel betweenness computation algorithms are proposed and implemented in a graph database for power system contingency selection. Principles of the graph database and graph computing are investigated for both node and edge betweenness computation. Experiments on the 118-bus system and a real power system show that speed-up can be achieved for both node and edge betweenness computation while the

    更新日期:2020-07-01
  • Transactions on Red-black and AVL trees in NVRAM
    arXiv.cs.DC Pub Date : 2020-06-29
    Thorsten Schütt; Florian Schintke; Jan Skrzypczak

    Byte-addressable non-volatile memory (NVRAM) supports persistent storage with low latency and high bandwidth. Complex data structures in it ought to be updated transactionally, so that they remain recoverable at all times. Traditional database technologies such as keeping a separate log, a journal, or shadow data work on a coarse-grained level, where the whole transaction is made visible using a final

    更新日期:2020-07-01
  • Optimal Rates of Distributed Regression with Imperfect Kernels
    arXiv.cs.DC Pub Date : 2020-06-30
    Hongwei SunUniversity of Jinan; Qiang WuMiddle Tennessee State University

    Distributed machine learning systems have been receiving increasing attentions for their efficiency to process large scale data. Many distributed frameworks have been proposed for different machine learning tasks. In this paper, we study the distributed kernel regression via the divide and conquer approach. This approach has been proved asymptotically minimax optimal if the kernel is perfectly selected

    更新日期:2020-07-01
  • Spatiotemporal Multi-Resolution Approximations for Analyzing Global Environmental Data
    arXiv.cs.DC Pub Date : 2020-06-30
    Marius Appel; Edzer Pebesma

    Technological developments and open data policies have made large, global environmental datasets accessible to everyone. For analysing such datasets, including spatiotemporal correlations using traditional models based on Gaussian processes does not scale with data volume and requires strong assumptions about stationarity, separability, and distance measures of covariance functions that are often unrealistic

    更新日期:2020-07-01
  • Lachesis: Automated Generation of Persistent Partitionings for Big Data Applications
    arXiv.cs.DC Pub Date : 2020-06-30
    Jia Zou; Pratik Barhate; Amitabh Das; Arun Iyengar; Binhang Yuan; Dimitrije Jankov; Chis Jermaine

    Persistent partitioning is effective in improving the performance by avoiding the expensive shuffling operation, while incurring relatively small overhead. However it remains a significant challenge to automate this process for UDF-centric analytics workloads, which is closely integrated with a high-level programming language such asPython, Scala, Java. That is because user defined functions (UDFs)in

    更新日期:2020-07-01
  • Investigating the Effects of Mobility Metrics in Mobile Ad Hoc Networks
    arXiv.cs.DC Pub Date : 2020-06-30
    Mohsin Ur Rahman

    Mobile Ad Hoc Networks (MANETs) are formed by a collection of mobile nodes (MNs) that are capable of moving from one location to another location. These networks are widely identified by their unique characteristics such as lack of infrastructure, mobility and multi-hop communication. Unlike traditional (wired) networks, MNs in MANETs do not rely on any infrastructure or central management. Mobility

    更新日期:2020-07-01
  • Efficient Algorithms for Device Placement of DNN Graph Operators
    arXiv.cs.DC Pub Date : 2020-06-29
    Jakub Tarnawski; Amar Phanishayee; Nikhil R. Devanur; Divya Mahajan; Fanny Nina Paravecino

    Modern machine learning workloads use large models, with complex structures, that are very expensive to execute. The devices that execute complex models are becoming increasingly heterogeneous as we see a flourishing of domain-specific accelerators being offered as hardware accelerators in addition to CPUs. These trends necessitate distributing the workload across multiple devices. Recent work has

    更新日期:2020-07-01
  • Shared vs Private Randomness in Distributed Interactive Proofs
    arXiv.cs.DC Pub Date : 2020-06-29
    Pedro Montealegre; Diego Ramírez-Romero; Ivan Rapaport

    In distributed interactive proofs, the nodes of a graph G interact with a powerful but untrustable prover who tries to convince them, in a small number of rounds and through short messages, that G satisfies some property. This series of interactions is followed by a phase of distributed verification, which may be either deterministic or randomized, where nodes exchange messages with their neighbors

    更新日期:2020-06-30
  • Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems
    arXiv.cs.DC Pub Date : 2020-06-24
    Yuanhang Yu; Dong Wen; Ying Zhang; Xiaoyang Wang; Wenjie Zhang; Xuemin Lin

    Matrix Factorization (MF) has been widely applied in machine learning and data mining. A large number of algorithms have been studied to factorize matrices. Among them, stochastic gradient descent (SGD) is a commonly used method. Heterogeneous systems with multi-core CPUs and GPUs have become more and more promising recently due to the prevalence of GPUs in general-purpose data-parallel applications

    更新日期:2020-06-30
  • The Interblockchain Communication Protocol: An Overview
    arXiv.cs.DC Pub Date : 2020-06-29
    Christopher Goes

    The interblockchain communication protocol (IBC) is an end-to-end, connection-oriented, stateful protocol for reliable, ordered, and authenticated communication between modules on separate distributed ledgers. IBC is designed for interoperation between heterogenous ledgers arranged in an unknown, dynamic topology, operating with varied consensus algorithms and state machines. The protocol realises

    更新日期:2020-06-30
  • Smart Contract-based Computing ResourcesTrading in Edge Computing
    arXiv.cs.DC Pub Date : 2020-06-29
    Jinyue Song; Tianbo Gu; Yunjie Ge; Prasant Mohapatra

    In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more security. The massive idle computing resources closing to the edge also enhance the deployment of edge services. Instead of using cloud services from some primary providers

    更新日期:2020-06-30
  • PyTorch Distributed: Experiences on Accelerating Data Parallel Training
    arXiv.cs.DC Pub Date : 2020-06-28
    Shen Li; Yanli Zhao; Rohan Varma; Omkar Salpekar; Pieter Noordhuis; Teng Li; Adam Paszke; Jeff Smith; Brian Vaughan; Pritam Damania; Soumith Chintala

    This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module. PyTorch is a widely-adopted scientific computing package used in deep learning research and applications. Recent advances in deep learning argue for the value of large datasets and large models, which necessitates the ability to scale out model training to more computational resources. Data

    更新日期:2020-06-30
  • EdgeKV: Decentralized, scalable, and consistent storage for the edge
    arXiv.cs.DC Pub Date : 2020-06-28
    Karim Sonbol; Öznur Özkasap; Ibrahim Al-Oqily; Moayad Aloqaily

    Edge computing moves the computation closer to the data and the data closer to the user to overcome the high latency communication of cloud computing. Storage at the edge allows data access with high speeds that enable latency-sensitive applications in areas such as autonomous driving and smart grid. However, several distributed services are typically designed for the cloud and building an efficient

    更新日期:2020-06-30
  • Fast and Low-cost Search for Efficient Cloud Configurations for HPC Workloads
    arXiv.cs.DC Pub Date : 2020-06-28
    Vanderson Martins Do Rosario; Thais A. Silva Camacho; Otávio O. Napoli; Edson Borin

    The use of cloud computational resources has become increasingly important for companies and researchers to access on-demand and at any moment high-performance resources. However, given the wide variety of virtual machine types, network configurations, number of instances, among others, finding the best configuration that reduces costs and resource waste while achieving acceptable performance is a

    更新日期:2020-06-30
  • High Performance Evaluation of Helmholtz Potentials usingthe Multi-Level Fast Multipole Algorithm
    arXiv.cs.DC Pub Date : 2020-06-27
    Michael P. Lingg; Stephen M. Hughey; Hasan Metin Aktulga; Balasubramaniam Shanker

    Evaluation of pair potentials is critical in a number of areas of physics. The classicalN-body problem has its root in evaluating the Laplace potential, and has spawned tree-algorithms, the fast multipole method (FMM), as well as kernel independent approaches. Over the years, FMM for Laplace potential has had a profound impact on a number of disciplines as it has been possible to develop highly scalable

    更新日期:2020-06-30
  • A Blockchain-based Approach for Assessing Compliance with SLA-guaranteed IoT Services
    arXiv.cs.DC Pub Date : 2020-06-27
    A. Alzubaidi; K. Mitra; P. Patel; E. Solaiman

    Within cloud-based internet of things (IoT) applications, typically cloud providers employ Service Level Agreements (SLAs) to ensure the quality of their provisioned services. Similar to any other contractual method, an SLA is not immune to breaches. Ideally, an SLA stipulates consequences (e.g. penalties) imposed on cloud providers when they fail to conform to SLA terms. The current practice assumes

    更新日期:2020-06-30
  • Optimizing Cuckoo Filter for high burst tolerance,low latency, and high throughput
    arXiv.cs.DC Pub Date : 2020-06-27
    Aman Khalid

    In this paper, we present an implementation of a cuckoo filter for membership testing, optimized for distributed data stores operating in high workloads. In large databases, querying becomes inefficient using traditional search methods. To achieve optimal performance it is necessary to use probabilistic data structures to test the membership of a given key, at the cost of getting false positives while

    更新日期:2020-06-30
  • Efficient 2D Tensor Network Simulation of Quantum Systems
    arXiv.cs.DC Pub Date : 2020-06-26
    Yuchen Pang; Tianyi Hao; Annika Dugad; Yiqing Zhou; Edgar Solomonik

    Simulation of quantum systems is challenging due to the exponential size of the state space. Tensor networks provide a systematically improvable approximation for quantum states. 2D tensor networks such as Projected Entangled Pair States (PEPS) are well-suited for key classes of physical systems and quantum circuits. However, direct contraction of PEPS networks has exponential cost, while approximate

    更新日期:2020-06-30
  • A Fast Distributed Algorithm for $(Δ+ 1)$-Edge-Coloring
    arXiv.cs.DC Pub Date : 2020-06-28
    Anton Bernshteyn

    We present a deterministic distributed algorithm in the LOCAL model that finds a proper $(\Delta + 1)$-edge-coloring of an $n$-vertex graph of maximum degree $\Delta$ in $\mathrm{poly}(\Delta, \log n)$ rounds. This is the first nontrivial distributed edge-coloring algorithm that uses only $\Delta+1$ colors (matching the bound given by Vizing's theorem). Our approach is inspired by the recent proof

    更新日期:2020-06-30
  • Simulating human interactions in supermarkets to measure the risk of COVID-19 contagion at scale
    arXiv.cs.DC Pub Date : 2020-06-26
    Serge Plata; Sumanas Sarma; Melvin Lancelot; Kristine Bagrova; David Romano-Critchley

    Taking the context of simulating a retail environment using agent based modelling, a theoretical model is presented that describes the probability distribution of customer "collisions" using a novel space transformation to the Torus $Tor^2$. A method for generating the distribution of customer paths based on historical basket data is developed. Finally a calculation of the number of simulations required

    更新日期:2020-06-30
  • GPU-Accelerated Discontinuous Galerkin Methods: 30x Speedup on 345 Billion Unknowns
    arXiv.cs.DC Pub Date : 2020-06-28
    Andrew C. Kirby; Dimitri J. Mavriplis

    A discontinuous Galerkin method for the discretization of the compressible Euler equations, the governing equations of inviscid fluid dynamics, on Cartesian meshes is developed for use of Graphical Processing Units via OCCA, a unified approach to performance portability on multi-threaded hardware architectures. A 30x speedup over CPU-only implementations using non-CUDA-Aware MPI communications is demonstrated

    更新日期:2020-06-30
  • Reconstructing Biological and Digital Phylogenetic Trees in Parallel
    arXiv.cs.DC Pub Date : 2020-06-27
    Ramtin Afshar; Michael T. Goodrich; Pedro Matias; Martha C. Osegueda

    In this paper, we study the parallel query complexity of reconstructing biological and digital phylogenetic trees from simple queries involving their nodes. This is motivated from computational biology, data protection, and computer security settings, which can be abstracted in terms of two parties, a \emph{responder}, Alice, who must correctly answer queries of a given type regarding a degree-$d$

    更新日期:2020-06-30
  • Dominate or Delete: Decentralized Competing Bandits with Uniform Valuation
    arXiv.cs.DC Pub Date : 2020-06-26
    Abishek Sankararaman; Soumya Basu; Karthik Abinav Sankararaman

    We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms) with unknown and heterogeneous valuations. Such markets abstract online matching platforms (for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et

    更新日期:2020-06-30
  • The TRaCaR Ratio: Selecting the Right Storage Technology for Active Dataset-Serving Databases
    arXiv.cs.DC Pub Date : 2020-06-26
    Francisco Romero; Benjamin Braun; David Cheriton

    Main memory database systems aim to provide users with low latency and high throughput access to data. Most data resides in secondary storage, which is limited by the access speed of the technology. For hot content, data resides in DRAM, which has become increasingly expensive as datasets grow in size and access demand. With the emergence of low-latency storage solutions such as Flash and Intel's 3D

    更新日期:2020-06-29
  • Self-Scaling Clusters and Reproducible Containers to Enable Scientific Computing
    arXiv.cs.DC Pub Date : 2020-06-26
    Peter Z. Vaillancourt; J. Eric Coulter; Richard Knepper; Brandon Barker

    Container technologies such as Docker have become a crucial component of many software industry practices especially those pertaining to reproducibility and portability. The containerization philosophy has influenced the scientific computing community, which has begun to adopt - and even develop - container technologies (such as Singularity). Leveraging containers for scientific software often poses

    更新日期:2020-06-29
  • Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
    arXiv.cs.DC Pub Date : 2020-06-25
    Ojas Parekh; Cynthia A. Phillips; Conrad D. James; James B. Aimone

    Boolean circuits of McCulloch-Pitts threshold gates are a classic model of neural computation studied heavily in the late 20th century as a model of general computation. Recent advances in large-scale neural computing hardware has made their practical implementation a near-term possibility. We describe a theoretical approach for multiplying two $N$ by $N$ matrices that integrates threshold gate logic

    更新日期:2020-06-29
  • Fast General Distributed Transactions with Opacity using Global Time
    arXiv.cs.DC Pub Date : 2020-06-25
    Alex Shamis; Matthew Renzelmann; Stanko Novakovic; Georgios Chatzopoulos; Anders T. Gjerdrum; Dan Alistarh; Aleksandar Dragojevic; Dushyanth Narayanan; Miguel Castro

    Transactions can simplify distributed applications by hiding data distribution, concurrency, and failures from the application developer. Ideally the developer would see the abstraction of a single large machine that runs transactions sequentially and never fails. This requires the transactional subsystem to provide opacity (strict serializability for both committed and aborted transactions), as well

    更新日期:2020-06-26
  • Blockchain-Based Applications in Higher Education: A Systematic Mapping Study
    arXiv.cs.DC Pub Date : 2020-06-25
    B. Awaji; E. Solaiman; A. Albshri

    The utilisation of blockchain has moved beyond digital currency to other fields such as health, the Internet of Things, and education. In this paper, we present a systematic mapping study to collect and analyse relevant research on blockchain technology related to the higher education field. The paper concentrates on two main themes. First, it examines state of the art in blockchain-based applications

    更新日期:2020-06-26
  • Dual-Free Stochastic Decentralized Optimization with Variance Reduction
    arXiv.cs.DC Pub Date : 2020-06-25
    Hadrien Hendrikx; Francis Bach; Laurent Massoulié

    We consider the problem of training machine learning models on distributed data in a decentralized way. For finite-sum problems, fast single-machine algorithms for large datasets rely on stochastic updates combined with variance reduction. Yet, existing decentralized stochastic algorithms either do not obtain the full speedup allowed by stochastic updates, or require oracles that are more expensive

    更新日期:2020-06-26
  • Effective Elastic Scaling of Deep Learning Workloads
    arXiv.cs.DC Pub Date : 2020-06-24
    Vaibhav Saxena; K. R. Jayaram; Saurav Basu; Yogish Sabharwal; Ashish Verma

    The increased use of deep learning (DL) in academia, government and industry has, in turn, led to the popularity of on-premise and cloud-hosted deep learning platforms, whose goals are to enable organizations utilize expensive resources effectively, and to share said resources among multiple teams in a fair and effective manner. In this paper, we examine the elastic scaling of Deep Learning (DL) jobs

    更新日期:2020-06-25
  • Caffe Barista: Brewing Caffe with FPGAs in the Training Loop
    arXiv.cs.DC Pub Date : 2020-06-18
    Diederik Adriaan Vink; Aditya Rajagopal; Stylianos I. Venieris; Christos-Savvas Bouganis

    As the complexity of deep learning (DL) models increases, their compute requirements increase accordingly. Deploying a Convolutional Neural Network (CNN) involves two phases: training and inference. With the inference task typically taking place on resource-constrained devices, a lot of research has explored the field of low-power inference on custom hardware accelerators. On the other hand, training

    更新日期:2020-06-25
  • Integrating LHCb workflows on HPC resources: status and strategies
    arXiv.cs.DC Pub Date : 2020-06-24
    Federico Stagni; Andrea Valassi; Vladimir Romanovskiy

    High Performance Computing (HPC) supercomputers are expected to play an increasingly important role in HEP computing in the coming years. While HPC resources are not necessarily the optimal fit for HEP workflows, computing time at HPC centers on an opportunistic basis has already been available to the LHC experiments for some time, and it is also possible that part of the pledged computing resources

    更新日期:2020-06-25
  • Local-Search Based Heuristics for Advertisement Scheduling
    arXiv.cs.DC Pub Date : 2020-06-24
    M. R. C. da Silva; R. C. S. Schouery

    In the MAXSPACE problem, given a set of ads A, one wants to place a subset A' of A into K slots B_1, ..., B_K of size L. Each ad A_i in A has a size s_i and a frequency w_i. A schedule is feasible if the total size of ads in any slot is at most L, and each ad A_i in A' appears in exactly w_i slots. The goal is to find a feasible schedule that maximizes the sum of the space occupied by all slots. We

    更新日期:2020-06-25
  • A Benchmarking Framework for Interactive 3D Applications in the Cloud
    arXiv.cs.DC Pub Date : 2020-06-23
    Tianyi Liu; Sen He; Sunzhou Huang; Danny Tsang; Lingjia Tang; Jason Mars; Wei Wang

    With the growing popularity of cloud gaming and cloud virtual reality (VR), interactive 3D applications have become a major type of workloads for the cloud. However, despite their growing importance, there is limited public research on how to design cloud systems to efficiently support these applications, due to the lack of an open and reliable research infrastructure, including benchmarks and performance

    更新日期:2020-06-25
  • A Cloud Computing Capability Model for Large-Scale Semantic Annotation
    arXiv.cs.DC Pub Date : 2020-06-24
    Oluwasegun Adedugbe; Elhadj Benkhelifa

    Semantic technologies are designed to facilitate context-awareness for web content, enabling machines to understand and process them. However, this has been faced with several challenges, such as disparate nature of existing solutions and lack of scalability in proportion to web scale. With a holistic perspective to web content semantic annotation, this paper focuses on leveraging cloud computing for

    更新日期:2020-06-25
  • Randomized Block-Diagonal Preconditioning for Parallel Learning
    arXiv.cs.DC Pub Date : 2020-06-24
    Celestine Mendler-Dünner; Aurelien Lucchi

    We study preconditioned gradient-based optimization methods where the preconditioning matrix has block-diagonal form. Such a structural constraint comes with the advantage that the update computation can be parallelized across multiple independent tasks. Our main contribution is to demonstrate that the convergence of these methods can significantly be improved by a randomization technique which corresponds

    更新日期:2020-06-25
  • Ramanujan Bipartite Graph Products for Efficient Block Sparse Neural Networks
    arXiv.cs.DC Pub Date : 2020-06-24
    Dharma Teja Vooturi; Girish Varma; Kishore Kothapalli

    Sparse neural networks are shown to give accurate predictions competitive to denser versions, while also minimizing the number of arithmetic operations performed. However current hardware like GPU's can only exploit structured sparsity patterns for better efficiency. Hence the run time of a sparse neural network may not correspond to the arithmetic operations required. In this work, we propose RBGP(

    更新日期:2020-06-25
  • Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes
    arXiv.cs.DC Pub Date : 2020-06-24
    Shuai Zheng; Haibin Lin; Sheng Zha; Mu Li

    BERT has recently attracted a lot of attention in natural language understanding (NLU) and achieved state-of-the-art results in various NLU tasks. However, its success requires large deep neural networks and huge amount of data, which result in long training time and impede development progress. Using stochastic gradient methods with large mini-batch has been advocated as an efficient tool to reduce

    更新日期:2020-06-25
  • Befriending The Byzantines Through Reputation Scores
    arXiv.cs.DC Pub Date : 2020-06-24
    Jayanth Regatti; Abhishek Gupta

    We propose two novel stochastic gradient descent algorithms, ByGARS and ByGARS++, for distributed machine learning in the presence of Byzantine adversaries. In these algorithms, reputation score of workers are computed using an auxiliary dataset with a larger stepsize. This reputation score is then used for aggregating the gradients for stochastic gradient descent with a smaller stepsize. We show that

    更新日期:2020-06-25
  • Optimised allgatherv, reduce_scatter and allreduce communication in message-passing systems
    arXiv.cs.DC Pub Date : 2020-06-23
    Andreas Jocksch; Noe Ohana; Emmanuel Lanti; Vasileios Karakasis; Laurent Villard

    Collective communications, namely the patterns allgatherv, reduce_scatter, and allreduce in message-passing systems are optimised based on measurements at the installation time of the library. The algorithms used are set up in an initialisation phase of the communication, similar to the method used in so-called persistent collective communication introduced in the literature. For allgatherv and reduce_scatter

    更新日期:2020-06-24
  • On the Interoperability of Decentralized Exposure Notification Systems
    arXiv.cs.DC Pub Date : 2020-06-23
    Marko Vukolic

    This report summarizes the requirements and proposes a high-level solution for interoperability across recently proposed COVID-19 exposure notification efforts. Our focus is on interoperability across exposure notification (EN) applications which are based on the decentralized Bluetooth Low Energy (BLE) protocol driven by Google/Apple Exposure Notifications API (including DP3T and similar protocols)

    更新日期:2020-06-24
  • Intermediate Value Linearizability: A Quantitative Correctness Criterion
    arXiv.cs.DC Pub Date : 2020-06-23
    Arik Rinberg; Idit Keidar

    Big data processing systems often employ batched updates and data sketches to estimate certain properties of large data. For example, a CountMin sketch approximates the frequencies at which elements occur in a data stream, and a batched counter counts events in batches. This paper focuses on the correctness of concurrent implementations of such objects. Specifically, we consider quantitative objects

    更新日期:2020-06-24
  • Distributed Subgraph Enumeration via Backtracking-based Framework
    arXiv.cs.DC Pub Date : 2020-06-23
    Zhaokang Wang; Weiwei Hu; Chunfeng Yuan; Rong Gu; Yihua Huang

    Given a small pattern graph and a large data graph, the task of subgraph enumeration is to find all subgraphs of the data graph that are isomorphic to the pattern graph. When the data graph is dynamic, the task of continuous subgraph enumeration is to detect the changes in the matching results caused by the edge updates at each time step. The two tasks are fundamental in many graph analysis applications

    更新日期:2020-06-24
  • PipeSim: Trace-driven Simulation of Large-Scale AI Operations Platforms
    arXiv.cs.DC Pub Date : 2020-06-22
    Thomas Rausch; Waldemar Hummer; Vinod Muthusamy

    Operationalizing AI has become a major endeavor in both research and industry. Automated, operationalized pipelines that manage the AI application lifecycle will form a significant part of tomorrow's infrastructure workloads. To optimize operations of production-grade AI workflow platforms we can leverage existing scheduling approaches, yet it is challenging to fine-tune operational strategies that

    更新日期:2020-06-24
  • Multiverse: Dynamic VM Provisioning for Virtualized High Performance Computing Clusters
    arXiv.cs.DC Pub Date : 2020-06-22
    Jashwant Raj Gunasekaran; Michael Cui; Prashanth Thinakaran; Josh Simons; Mahmut Taylan Kandemir; Chita R. Das

    Traditionally, HPC workloads have been deployed in bare-metal clusters; but the advances in virtualization have led the pathway for these workloads to be deployed in virtualized clusters. However, HPC cluster administrators/providers still face challenges in terms of resource elasticity and virtual machine (VM) provisioning at large-scale, due to the lack of coordination between a traditional HPC scheduler

    更新日期:2020-06-24
  • An Agent-based Cloud Service Negotiation in Hybrid Cloud Computing
    arXiv.cs.DC Pub Date : 2020-06-16
    Saurabh Deochake; Debajyoti Mukhopadhyay

    With the advent of evolution of cloud computing, large organizations have been scaling the on-premise IT infrastructure to the cloud. Although this being a popular practice, it lacks comprehensive efforts to study the aspects of automated negotiation of resources among cloud customers and providers. This paper proposes a full-fledged framework for the multi-party, multi-issue negotiation system for

    更新日期:2020-06-24
  • Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data
    arXiv.cs.DC Pub Date : 2020-06-22
    Deepesh Data; Suhas Diggavi

    We study stochastic gradient descent (SGD) with local iterations in the presence of malicious/Byzantine clients, motivated by the federated learning. The clients, instead of communicating with the central server in every iteration, maintain their local models, which they update by taking several SGD iterations based on their own datasets and then communicate the net update with the server, thereby

    更新日期:2020-06-24
  • Location-Aware Resource Allocation Algorithm in Satellite Ground Station Networks
    arXiv.cs.DC Pub Date : 2020-06-23
    Xiangqiang Gao; Rongke liu; Aryan Kaushik

    As per the increase in satellite number and variety, satellite ground station should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on demand for user services over the underlying network. In this paper, we investigate the virtualized network function (VNF) placement and routing traffic

    更新日期:2020-06-24
  • Similarity Search with Tensor Core Units
    arXiv.cs.DC Pub Date : 2020-06-22
    Thomas D. Ahle; Francesco Silvestri

    Tensor Core Units (TCUs) are hardware accelerators developed for deep neural networks, which efficiently support the multiplication of two dense $\sqrt{m}\times \sqrt{m}$ matrices, where $m$ is a given hardware parameter. In this paper, we show that TCUs can speed up similarity search problems as well. We propose algorithms for the Johnson-Lindenstrauss dimensionality reduction and for similarity join

    更新日期:2020-06-24
  • LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
    arXiv.cs.DC Pub Date : 2020-06-22
    Wentao Zhu; Can Zhao; Wenqi Li; Holger Roth; Ziyue Xu; Daguang Xu

    Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data parallelism and model parallelism are two well-known approaches for parallel training. However, data parallelism does not help reduce memory footprint per device. In this work, we introduce Large deep 3D ConvNets with Automated

    更新日期:2020-06-24
  • Money Transfer Made Simple
    arXiv.cs.DC Pub Date : 2020-06-18
    Alex AuvolatWIDE, Univ-Rennes, IRISA, DI-ENS, CNRS; Davide FreyWIDE, Univ-Rennes, IRISA, CNRS; Michel RaynalWIDE, Univ-Rennes, IRISA, POLYU, CNRS; François TaïaniWIDE, Univ-Rennes, IRISA, CNRS

    It has recently been shown (PODC 2019) that, contrarily to a common belief, money transfer in the presence of faulty (Byzantine) processes does not require strong agreement such as consensus. This article goes one step further: namely, it shows that money transfers do not need to explicitly capture the causality relation that links individual transfers. A simple FIFO order between each pair of processes

    更新日期:2020-06-23
  • Dataflow Aware Mapping of Convolutional Neural Networks Onto Many-Core Platforms With Network-on-Chip Interconnect
    arXiv.cs.DC Pub Date : 2020-06-18
    Andreas Bytyn; René Ahlsdorf; Rainer Leupers; Gerd Ascheid

    Machine intelligence, especially using convolutional neural networks (CNNs), has become a large area of research over the past years. Increasingly sophisticated hardware accelerators are proposed that exploit e.g. the sparsity in computations and make use of reduced precision arithmetic to scale down the energy consumption. However, future platforms require more than just energy efficiency: Scalability

    更新日期:2020-06-23
  • Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks
    arXiv.cs.DC Pub Date : 2020-06-22
    Shaocheng Huang; Yu Ye; Ming Xiao; H. Vincent Poor; Mikael Skoglund

    Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) are required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose

    更新日期:2020-06-23
  • Fast overlap detection between hard-core colloidal cuboids and spheres. The OCSI algorithm
    arXiv.cs.DC Pub Date : 2020-06-22
    Luca Tonti; Alessandro Patti

    Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are

    更新日期:2020-06-23
  • Scalable Range Locks for Scalable Address Spaces and Beyond
    arXiv.cs.DC Pub Date : 2020-06-22
    Alex Kogan; Dave Dice; Shady Issa

    Range locks are a synchronization construct designed to provide concurrent access to multiple threads (or processes) to disjoint parts of a shared resource. Originally conceived in the file system context, range locks are gaining increasing interest in the Linux kernel community seeking to alleviate bottlenecks in the virtual memory management subsystem. The existing implementation of range locks in

    更新日期:2020-06-23
  • Planarity is (almost) locally checkable in constant-time
    arXiv.cs.DC Pub Date : 2020-06-21
    Gábor Elek

    Locally checkable proofs for graph properties were introduced by G\"o\"os and Suomela \cite{GS}. Roughly speaking, a graph property $\cP$ is locally checkable in constant-time, if the vertices of a graph having the property can be convinced, in a short period of time not depending on the size of the graph, that they are indeed vertices of a graph having the given property. For a given $\eps>0$, we

    更新日期:2020-06-23
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