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RaaS: Resilience as a Service 18

RaaS: Resilience as a Service

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18th IEEE/ACM international symposium on Cluster, Cloud and Grid Computing (CCGrid)

作者： Villamayor, Jorge Rexachs, Dolores Luque, Emilio Lugones, Diego Univ Autonoma Barcelona CAOS Barcelona Spain Nokia Bell Labs Dublin Ireland

ISBN: (纸本)9781538658154

Cloud computing is continuously increasing its popularity as key features such as scalability, pay-per-use and availability continue to evolve. It is also becoming a competitive platform for running high performance computing (HPC) and parallel applications due to the increasing performance of virtualized, highly-available instances. However, migrating HPC applications to cloud still requires native fault-tolerant solutions to fully leverage cloud features and maximize the resource utilization at the best cost - particularly for long-running parallel applications where faults can cause invalid states or data loss. This requires re-executing applications which increases completion time and cost. We propose Resilience as a Service (RaaS), a fault tolerant framework for HPC applications running in cloud. In this paper RADIC architecture (Redundant Array of distributed Independent Fault Tolerance Controllers) is used to provide clouds with a highly available, distributed and scalable fault-tolerant service. The paper explores how traditional HPC protection and recovery mechanisms must be redesigned to natively leverage cloud properties and its multiple alternatives for implementing rollback recovery protocols using virtual machines, containers, object and block storage or database services. Results show that RaaS restores and completes the application execution using available resources while reducing overhead up to 8% for different fault-tolerant configuration alternatives.

关键词： Cloud computing Fault tolerance Fault tolerant systems Monitoring Task analysis databases

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AutoTuneTMP: Auto-tuning in C++ with runtime template metaprogramming 32

AutoTuneTMP: Auto-tuning in C++ with runtime template metapr...

引用

32nd IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2018

作者： Pfander, David Brunn, Malte Pfluger, Dirk Institute for Parallel and Distributed Systems University of Stuttgart Germany

ISBN: (纸本)9781538655559

Careful tuning of code is crucial to obtain near-optimal runtime performance on the hardware at hand. How-ever, current hardware platforms pose plenty of challenges: multiple levels of cache, many cores and increasingly wider vector units render manual tuning time-consuming and cumbersome. Thus, algorithms have to be able to automatically tune themselves to the underlying hardware platform to maximize performance and to ensure performance portability. In this work, we present AutoTuneTMP, a novel C++-based auto-tuning library. Its unique strength is to combine template metaprogramming and just-in-time (JIT) compilation so that templates can be instantiated at runtime. We use this runtime metaprogramming approach to provide an extensible set of parameterized template-based optimizations and data structures for writing tunable kernels. Together with convenience functionality for parameter tuning, this lightweight approach can be used to implement algorithms with near-optimal and portable performance. We demonstrate the applicability, usefulness and performance of AutoTuneTMP at the example of matrix multiplication. It is well-suited as a demonstrator, being a well-known compute kernel and exhibiting a rather large set of (nine) tunable parameters to be optimized. With simple search algorithms, AutoTuneTMP is able to automatically achieve up to 90% of the peak performance on a Xeon Silver 4116 processor and up to 34% on a Xeon Phi 7210. Starting from an already parallelized and vectorized baseline, we obtained a speedup of up to 3.1x on the Xeon Silver platform and up to 6.7x on the Xeon Phi platform. © 2018 IEEE.

关键词： Silver

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A model-based approach to streamlining distributed training for asynchronous SGD

A model-based approach to streamlining distributed training ...

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26th IEEE international symposium on Modeling, Analysis and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Lin, Sung-Han Paolieri, Marco Chou, Cheng-Fu Golubchik, Leana Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA Natl Taiwan Univ Dept Comp Sci Taipei Taiwan NetApp Inc Sunnyvale CA 94089 USA

ISBN: (纸本)9781538668863

The success of Deep Neural Networks (DNNs) has created significant interest in the development of software tools, hardware architectures, and cloud systems to meet the huge computational demand of their training jobs. A common approach to speeding up an individual job is to distribute training data and computation among multiple nodes, periodically exchanging intermediate results. In this paper, we address two important problems for the application of this strategy to large-scale clusters and multiple, heterogeneous jobs. First, we propose and validate a queueing model to estimate the throughput of a training job as a function of the number of nodes assigned to the job;this model targets asynchronous Stochastic Gradient Descent (SGD), a popular strategy for distributed training, and requires only data from quick, two-node profiling in addition to job characteristics (number of requested training epochs, mini-batch size, size of DNN parameters, assigned bandwidth). Throughput estimations are then used to explore several classes of scheduling heuristics to reduce response time in a scenario where heterogeneous jobs are continuously submitted to a large-scale cluster. These scheduling algorithms dynamically select which jobs to run and how many nodes to assign to each job, based on different trade-offs between service time reduction and efficiency (e.g., speedup per additional node). Heuristics are evaluated through extensive simulations of realistic DNN workloads, also investigating the effects of early termination, a common scenario for DNN training jobs.

关键词： distributed Machine Learning parallel Scheduling Queueing Networks TensorFlow

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Benchmarking Big Data OLAP NoSQL databases 4th

Benchmarking Big Data OLAP NoSQL Databases

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4th international symposium on Ubiquitous Networking (UNet)

作者： El Malki, Mohammed Kopliku, Arlind Sabir, Essaid Teste, Olivier Inst Rech Informat Toulouse Toulouse France Hassan II Univ Casablanca NEST Res Grp ENSEM Casablanca Morocco

ISBN: (纸本)9783030028497;9783030028480

With the advent of Big Data, new challenges have emerged regarding the evaluation of decision support systems (DSS). Existing evaluation benchmarks are not configured to handle a massive data volume and wide data diversity. In this paper, we introduce a new DSS benchmark that supports multiple data storage systems, such as relational and Not Only SQL (NoSQL) systems. Our scheme recognizes numerous data models (snowflake, star and flat topologies) and several data formats (CSV, JSON, TBL, XML, etc.). It entails complex data generation characterized within "volume, variety, and velocity" framework (3 V). Next, our scheme enables distributed and parallel data generation. Furthermore, we exhibit some experimental results with KoalaBench.

关键词： Decision support systems

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Process-in-Process: Techniques for Practical Address-Space Sharing 18

Process-in-Process: Techniques for Practical Address-Space S...

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27th ACM international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Hori, Atsushi Si, Min Gerofi, Balazs Takagi, Masamichi Dayal, Jai Balaji, Pavan Ishikawa, Yutaka RIKEN Wako Saitama Japan Argonne Natl Lab Argonne IL 60439 USA Intel Corp Santa Clara CA 95051 USA

ISBN: (纸本)9781450357852

The two most common parallel execution models for many-core CPUs today are multiprocess (e.g., MPI) and multithread (e.g., OpenMP). The multiprocess model allows each process to own a private address space, although processes can explicitly allocate shared-memory regions. The multithreaded model shares all address space by default, although threads can explicitly move data to thread-private storage. In this paper, we present a third model called process-in-process (PiP), where multiple processes are mapped into a single virtual address space. Thus, each process still owns its process-private storage (like the multiprocess model) but can directly access the private storage of other processes in the same virtual address space (like the multithread model). The idea of address-space sharing between multiple processes itself is not new. What makes PiP unique, however, is that its design is completely in user space, making it a portable and practical approach for large supercomputing systems where porting existing OS-based techniques might be hard. The PiP library is compact and is designed for integrating with other runtime systems such as MPI and OpenMP as a portable low-level support for boosting communication performance in HPC applications. We showcase the uniqueness of the PiP environment through both a variety of parallel runtime optimizations and direct use in a data analysis application. We evaluate PiP on several platforms including two high-ranking supercomputers, and we measure and analyze the performance of PiP by using a variety of micro-and macro-kernels, a proxy application as well as a data analysis application.

关键词： parallel Execution Model Intra-Node Communication MPI In-situ

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A Network-Centric Hardware/Algorithm Co-Design to Accelerate distributed Training of Deep Neural Networks 51

A Network-Centric Hardware/Algorithm Co-Design to Accelerate...

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51st Annual IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Li, Youjie Park, Jongse Alian, Mohammad Yuan, Yifan Qu, Zheng Pan, Peitian Wang, Ren Schwing, Alexander Gerhard Esmaeilzadeh, Hadi Kim, Nam Sung Univ Illinois Urbana IL 61801 USA Georgia Inst Technol Atlanta GA 30332 USA Tsinghua Univ Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Intel Corp Santa Clara CA 95051 USA Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781538662403

Training real-world Deep Neural Networks (DNNs) can take an eon (i.e., weeks or months) without leveraging distributed systems. Even distributed training takes inordinate time, of which a large fraction is spent in communicating weights and gradients over the network. State-of-the-art distributed training algorithms use a hierarchy of worker-aggregator nodes. The aggregators repeatedly receive gradient updates from their allocated group of the workers, and send back the updated weights. This paper sets out to reduce this significant communication cost by embedding data compression accelerators in the Network Interface Cards (NICs). To maximize the benefits of in-network acceleration, the proposed solution, named INCEPTIONN (In-Network Computing to Exchange and Process Training Information Of Neural Networks), uniquely combines hardware and algorithmic innovations by exploiting the following three observations. (1) Gradients are significantly more tolerant to precision loss than weights and as such lend themselves better to aggressive compression without the need for the complex mechanisms to avert any loss. (2) The existing training algorithms only communicate gradients in one leg of the communication, which reduces the opportunities for in-network acceleration of compression. (3) The aggregators can become a bottleneck with compression as they need to compress/decompress multiple streams from their allocated worker group. To this end, we first propose a lightweight and hardware friendly lossy-compression algorithm for floating-point gradients, which exploits their unique value characteristics. This compression not only enables significantly reducing the gradient communication with practically no loss of accuracy, but also comes with low complexity for direct implementation as a hardware block in the NIC. To maximize the opportunities for compression and avoid the bottleneck at aggregators, we also propose an aggregator-free training algorithm that exchanges gradien

关键词： Training Neural networks Hardware Acceleration Complexity theory Compression algorithms distributed databases

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NCA 2018 - 2018 IEEE 17th international symposium on Network Computing and Applications

NCA 2018 - 2018 IEEE 17th International Symposium on Network...

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17th IEEE international symposium on Network Computing and Applications, NCA 2018

ISBN: (纸本)9781538676592

The proceedings contain 62 papers. The topics discussed include: Sycomore: a permissionless distributed ledger that self-adapts to transactions demand;analytical hierarchy process multi-metric objective function for RPL;LoRaWAN analysis under unsaturated traffic, orthogonal and non-orthogonal spreading factor conditions;DBFT: efficient leaderless Byzantine consensus and its application to blockchains;efficient multi-hop broadcasting in dense nanonetworks;securing WirelessHART: monitoring, exploring and detecting new vulnerabilities;response time characterization of microservice-based systems;low-reliable low-latency networks optimized for HPC parallel applications;and recurrent neural network-based prediction of TCP transmission states from passive measurements.

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Introduction to HiCOMB 2018

Introduction to HiCOMB 2018

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

作者： Srinivas Aluru David Bader Paul Medvedev

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

ISBN: (纸本)9781538655566;9781538655559

关键词： Conferences Bioinformatics distributed processing Computational biology distributed databases Complexity theory Genomics

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Formulation of the simulation cluster modelling system development problem 29

Formulation of the simulation cluster modelling system devel...

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29th international DAAAM symposium on Intelligent Manufacturing and Automation, DAAAM 2018

作者： Vendin, Alexandr Karpova, Irina

ISBN: (纸本)9783902734204

The paper deals with the problem creation of a simulation system. The problem is that existing systems, such as Starlogo Nova, Swarm, Mason, TeamBots, Ascape, Stage, V-REP, do not allow in the complex to simulate the physical properties of the object, implement elements of robots, maintain communication between agents, maintain a large number of agents, support the use of clusters and use social-oriented libraries. This ultimately significantly reduces the computational speed. To overcome these difficulties, the structure of a new system of simulation is proposed. At the center of the new system is a calculation module with the possibilities of parallelizing the computational process within a single computer. The module supports distributed computing, if the system is used in a computer network. A variant of the structural scheme of the modeling system is also offered, which includes a calculation module, consisting of a parallel programming support module, a distributed computing support module, an agent module and a simulation environment module. In addition, the proposed structural scheme includes a visualizer, combining the controller/management console and the graphic user interface. The functioning of the system depends on the choice of the basic algorithms that ensure the operation of the simulation system. Offered structure of the system of imitation simulation will allow raising the speed of calculations. Modular system makes it possible to change the configuration of the system with the replacement of some functioning algorithms to others. At the same time, the effective operation of the simulation system, according to the proposed scheme, is not possible without the development of individual software, using high-level programming languages such as C ++ or Java. © 2018, Danube Adria Association for Automation and Manufacturing, DAAAM. All rights reserved.

关键词： distributed computer systems

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HCW 2018 Keynote Talk 1

HCW 2018 Keynote Talk 1

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

作者： Manish Parashar

Provides an abstract of the keynote presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.

ISBN: (纸本)9781538655566;9781538655559

关键词： Computer science Informatics distributed processing Conferences distributed databases Limiting Data processing

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