ADMI Cloud Computing Presentation
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Integration of CUDA Processing Within the C++ Library for Parallelism and Concurrency (HPX)
1 Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX) Patrick Diehl, Madhavan Seshadri, Thomas Heller, Hartmut Kaiser Abstract—Experience shows that on today’s high performance systems the utilization of different acceleration cards in conjunction with a high utilization of all other parts of the system is difficult. Future architectures, like exascale clusters, are expected to aggravate this issue as the number of cores are expected to increase and memory hierarchies are expected to become deeper. One big aspect for distributed applications is to guarantee high utilization of all available resources, including local or remote acceleration cards on a cluster while fully using all the available CPU resources and the integration of the GPU work into the overall programming model. For the integration of CUDA code we extended HPX, a general purpose C++ run time system for parallel and distributed applications of any scale, and enabled asynchronous data transfers from and to the GPU device and the asynchronous invocation of CUDA kernels on this data. Both operations are well integrated into the general programming model of HPX which allows to seamlessly overlap any GPU operation with work on the main cores. Any user defined CUDA kernel can be launched on any (local or remote) GPU device available to the distributed application. We present asynchronous implementations for the data transfers and kernel launches for CUDA code as part of a HPX asynchronous execution graph. Using this approach we can combine all remotely and locally available acceleration cards on a cluster to utilize its full performance capabilities. -
Working with Storm Topologies Date of Publish: 2018-08-13
Apache Storm 3 Working with Storm Topologies Date of Publish: 2018-08-13 http://docs.hortonworks.com Contents Packaging Storm Topologies................................................................................... 3 Deploying and Managing Apache Storm Topologies............................................4 Configuring the Storm UI.................................................................................................................................... 4 Using the Storm UI.............................................................................................................................................. 5 Monitoring and Debugging an Apache Storm Topology......................................6 Enabling Dynamic Log Levels.............................................................................................................................6 Setting and Clearing Log Levels Using the Storm UI.............................................................................6 Setting and Clearing Log Levels Using the CLI..................................................................................... 7 Enabling Topology Event Logging......................................................................................................................7 Configuring Topology Event Logging.....................................................................................................8 Enabling Event Logging...........................................................................................................................8 -
Apache Flink™: Stream and Batch Processing in a Single Engine
Apache Flink™: Stream and Batch Processing in a Single Engine Paris Carboney Stephan Ewenz Seif Haridiy Asterios Katsifodimos* Volker Markl* Kostas Tzoumasz yKTH & SICS Sweden zdata Artisans *TU Berlin & DFKI parisc,[email protected] fi[email protected] fi[email protected] Abstract Apache Flink1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continu- ous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as pipelined fault-tolerant dataflows. In this paper, we present Flink’s architecture and expand on how a (seemingly diverse) set of use cases can be unified under a single execution model. 1 Introduction Data-stream processing (e.g., as exemplified by complex event processing systems) and static (batch) data pro- cessing (e.g., as exemplified by MPP databases and Hadoop) were traditionally considered as two very different types of applications. They were programmed using different programming models and APIs, and were exe- cuted by different systems (e.g., dedicated streaming systems such as Apache Storm, IBM Infosphere Streams, Microsoft StreamInsight, or Streambase versus relational databases or execution engines for Hadoop, including Apache Spark and Apache Drill). Traditionally, batch data analysis made up for the lion’s share of the use cases, data sizes, and market, while streaming data analysis mostly served specialized applications. It is becoming more and more apparent, however, that a huge number of today’s large-scale data processing use cases handle data that is, in reality, produced continuously over time. -
DSP Frameworks DSP Frameworks We Consider
Università degli Studi di Roma “Tor Vergata” Dipartimento di Ingegneria Civile e Ingegneria Informatica DSP Frameworks Corso di Sistemi e Architetture per Big Data A.A. 2017/18 Valeria Cardellini DSP frameworks we consider • Apache Storm (with lab) • Twitter Heron – From Twitter as Storm and compatible with Storm • Apache Spark Streaming (lab) – Reduce the size of each stream and process streams of data (micro-batch processing) • Apache Flink • Apache Samza • Cloud-based frameworks – Google Cloud Dataflow – Amazon Kinesis Streams Valeria Cardellini - SABD 2017/18 1 Apache Storm • Apache Storm – Open-source, real-time, scalable streaming system – Provides an abstraction layer to execute DSP applications – Initially developed by Twitter • Topology – DAG of spouts (sources of streams) and bolts (operators and data sinks) Valeria Cardellini - SABD 2017/18 2 Stream grouping in Storm • Data parallelism in Storm: how are streams partitioned among multiple tasks (threads of execution)? • Shuffle grouping – Randomly partitions the tuples • Field grouping – Hashes on a subset of the tuple attributes Valeria Cardellini - SABD 2017/18 3 Stream grouping in Storm • All grouping (i.e., broadcast) – Replicates the entire stream to all the consumer tasks • Global grouping – Sends the entire stream to a single task of a bolt • Direct grouping – The producer of the tuple decides which task of the consumer will receive this tuple Valeria Cardellini - SABD 2017/18 4 Storm architecture • Master-worker architecture Valeria Cardellini - SABD 2017/18 5 Storm -
Bench - Benchmarking the State-Of- The-Art Task Execution Frameworks of Many- Task Computing
MATRIX: Bench - Benchmarking the state-of- the-art Task Execution Frameworks of Many- Task Computing Thomas Dubucq, Tony Forlini, Virgile Landeiro Dos Reis, and Isabelle Santos Illinois Institute of Technology, Chicago, IL, USA {tdubucq, tforlini, vlandeir, isantos1}@hawk.iit.edu Stanford University. Finally HPX is a general purpose C++ Abstract — Technology trends indicate that exascale systems will runtime system for parallel and distributed applications of any have billion-way parallelism, and each node will have about three scale developed by Louisiana State University and Staple is a orders of magnitude more intra-node parallelism than today’s framework for developing parallel programs from Texas A&M. peta-scale systems. The majority of current runtime systems focus a great deal of effort on optimizing the inter-node parallelism by MATRIX is a many-task computing job scheduling system maximizing the bandwidth and minimizing the latency of the use [3]. There are many resource managing systems aimed towards of interconnection networks and storage, but suffer from the lack data-intensive applications. Furthermore, distributed task of scalable solutions to expose the intra-node parallelism. Many- scheduling in many-task computing is a problem that has been task computing (MTC) is a distributed fine-grained paradigm that considered by many research teams. In particular, Charm++ [4], aims to address the challenges of managing parallelism and Legion [5], Swift [6], [10], Spark [1][2], HPX [12], STAPL [13] locality of exascale systems. MTC applications are typically structured as direct acyclic graphs of loosely coupled short tasks and MATRIX [11] offer solutions to this problem and have with explicit input/output data dependencies. -
Apache Storm Tutorial
Apache Storm Apache Storm About the Tutorial Storm was originally created by Nathan Marz and team at BackType. BackType is a social analytics company. Later, Storm was acquired and open-sourced by Twitter. In a short time, Apache Storm became a standard for distributed real-time processing system that allows you to process large amount of data, similar to Hadoop. Apache Storm is written in Java and Clojure. It is continuing to be a leader in real-time analytics. This tutorial will explore the principles of Apache Storm, distributed messaging, installation, creating Storm topologies and deploy them to a Storm cluster, workflow of Trident, real-time applications and finally concludes with some useful examples. Audience This tutorial has been prepared for professionals aspiring to make a career in Big Data Analytics using Apache Storm framework. This tutorial will give you enough understanding on creating and deploying a Storm cluster in a distributed environment. Prerequisites Before proceeding with this tutorial, you must have a good understanding of Core Java and any of the Linux flavors. Copyright & Disclaimer © Copyright 2014 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. -
HPX – a Task Based Programming Model in a Global Address Space
HPX – A Task Based Programming Model in a Global Address Space Hartmut Kaiser1 Thomas Heller2 Bryce Adelstein-Lelbach1 [email protected] [email protected] [email protected] Adrian Serio1 Dietmar Fey2 [email protected] [email protected] 1Center for Computation and 2Computer Science 3, Technology, Computer Architectures, Louisiana State University, Friedrich-Alexander-University, Louisiana, U.S.A. Erlangen, Germany ABSTRACT 1. INTRODUCTION The significant increase in complexity of Exascale platforms due to Todays programming models, languages, and related technologies energy-constrained, billion-way parallelism, with major changes to that have sustained High Performance Computing (HPC) appli- processor and memory architecture, requires new energy-efficient cation software development for the past decade are facing ma- and resilient programming techniques that are portable across mul- jor problems when it comes to programmability and performance tiple future generations of machines. We believe that guarantee- portability of future systems. The significant increase in complex- ing adequate scalability, programmability, performance portability, ity of new platforms due to energy constrains, increasing paral- resilience, and energy efficiency requires a fundamentally new ap- lelism and major changes to processor and memory architecture, proach, combined with a transition path for existing scientific ap- requires advanced programming techniques that are portable across plications, to fully explore the rewards of todays and tomorrows multiple future generations of machines [1]. systems. We present HPX – a parallel runtime system which ex- tends the C++11/14 standard to facilitate distributed operations, A fundamentally new approach is required to address these chal- enable fine-grained constraint based parallelism, and support run- lenges. -
Communicate the Future PROCEEDINGS HYATT REGENCY ORLANDO 20–23 MAY | ORLANDO, FL and Summit.Stc.Org #STC18
Communicate the Future PROCEEDINGS HYATT REGENCY ORLANDO 20–23 MAY | ORLANDO, FL www.stc.org and summit.stc.org #STC18 SOCIETY FOR TECHNICAL COMMUNICATION 1 Efficiency exemplified Organizations globally use Adobe FrameMaker (2017 release) Request demo to transform the way they create and deliver content Accelerated turnaround time for 70% reduction in printing and paper customized publications material cost Faster creation and delivery of content 50% faster production of PDF for new products across devices documentation 20% faster development of Accelerated publishing across formats course content Increased efficiency and reduced 20% improvement in process translation costs while producing multilingual manuals efficiency For a personalized demo or questions, write to us at [email protected] © 2018 Adobe Systems Incorporated. All rights reserved. The papers published in these proceedings were reproduced from originals furnished by the authors. The authors, not the Society for Technical Communication (STC), are solely responsible for the opinions expressed, the integrity of the information presented, and the attribution of sources. The papers presented in this publication are the works of their respective authors. Minor copyediting changes were made to ensure consistency. STC grants permission to educators and academic libraries to distribute articles from these proceedings for classroom purposes. There is no charge to these institutions, provided they give credit to the author, the proceedings, and STC. All others must request permission. All product and company names herein are the property of their respective owners. © 2018 Society for Technical Communication 9401 Lee Highway, Suite 300 Fairfax, VA 22031 USA +1.703.522.4114 www.stc.org Design and layout by Avon J. -
Cutter IT Journal
Cutter The Journal of IT Journal Information Technology Management Vol. 26, No. 3 March 2013 “Cloud service providers, the IT industry, professional The Emerging Cloud Ecosystem: and industry associations, governments, and IT pro- Innovative New Services and fessionals all have a role to Business Models play in shaping, fostering, and harnessing the full potential of the emerging cloud ecosystem.” Opening Statement — San Murugesan, by San Murugesan . 3 Guest Editor Merging IaaS with PaaS to Deliver Robust Development Tools by Beth Cohen . 6 Intrusion Detection as a Service (IDaaS) in an Open Source Cloud Infrastructure by John Prakash Veigas and K Chandra Sekaran . 12 Cloud Ecology: Surviving in the Jungle by Claude R. Baudoin . 19 The Promise of a Diverse, Interoperable Cloud Ecosystem — And Recommendations for Realizing It by Kathy L. Grise . 26 NOT FOR DISTRIBUTION For authorized use, contact Cutter Consortium: +1 781 648 8700 [email protected] Cutter IT Journal About Cutter IT Journal Cutter IT Journal® Cutter Business Technology Council: Part of Cutter Consortium’s mission is to Cutter IT Journal subscribers consider the Rob Austin, Ron Blitstein, Tom DeMarco, Lynne Ellyn, Israel Gat, Vince Kellen, foster debate and dialogue on the business Journal a “consultancy in print” and liken Tim Lister, Lou Mazzucchelli, technology issues challenging enterprises each month’s issue to the impassioned Ken Orr, and Robert D. Scott today, helping organizations leverage IT for debates they participate in at the end of Editor Emeritus: Ed Yourdon competitive advantage and business success. a day at a conference. Publisher: Karen Fine Coburn Cutter’s philosophy is that most of the issues Group Publisher: Chris Generali that managers face are complex enough to Every facet of IT — application integration, Managing Editor: Karen Pasley merit examination that goes beyond simple security, portfolio management, and testing, Production Editor: Linda M. -
Efficient Support for Data-Intensive Scientific Workflows on Geo
Efficient support for data-intensive scientific workflows on geo-distributed clouds Luis Eduardo Pineda Morales To cite this version: Luis Eduardo Pineda Morales. Efficient support for data-intensive scientific workflows ongeo- distributed clouds. Computation and Language [cs.CL]. INSA de Rennes, 2017. English. NNT : 2017ISAR0012. tel-01645434v2 HAL Id: tel-01645434 https://tel.archives-ouvertes.fr/tel-01645434v2 Submitted on 13 Dec 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A Lucía, que no dejó que me rindiera. Acknowledgements One page is not enough to thank all the people who, in one way or another, have helped, taught, and motivated me along this journey. My deepest gratitude to them all. To my amazing supervisors Gabriel Antoniu and Alexandru Costan, words fall short to thank you for your guidance, support and patience during these 3+ years. Gabriel: ¡Gracias! For your trust and support went often beyond the academic. Alex: it has been my honor to be your first PhD student, I hope I met the expectations. To my family, Lucía, for standing by my side every time, everywhere; you are the driving force in my life, I love you. -
Apache Taverna
Apache Taverna hp://taverna.incubator.apache.org/ Donal Fellows San Soiland-Reyes @donalfellows @soilandreyes [email protected] [email protected] hp://orcid.org/0000-0002-9091-5938 hp://orcid.org/0000-0001-9842-9718 Alan R Williams @alanrw [email protected] hp://orcid.org/0000-0003-3156-2105 This work is licensed under a Creave Commons Collaboraons Workshop 2015-03-26 A,ribuIon 3.0 Unported License. Taverna Workflow Ecosystem • Workflow Language — SCUFL2 (and t2flow) • Workflow Engine — Taverna • Used in… – Taverna Command Line Tool – Taverna Server – Taverna WorkBench • Allied services – myExperiment, workflow repository – Service Catalographer, service catalog software • Instantiated as BioCatalogue, BiodiversityCatalogue, … NERSC Workflow Day 2 Map of the Taverna Ecosystem Taverna Ruby client Taverna Online Applicaon-Specific Portals Player UI UI Plugins UI Plugins Plugins Taverna UI Plugins Lite Taverna Taverna Workbench UI Plugins Command UI Plugins Line Tool REST API Components Taverna Other TavernaUI Plugins APIs Server Servers Taverna SOAP API Taverna Core AcIvity Engine UI Ps UI Plugins Plugins Workflow Service many Repository Catalogs services… 3 Users, Scientific Areas, Projects Taverna In Use NERSC Workflow Day 4 Taverna Users Worldwide NERSC Workflow Day 5 Taverna Uses — Scientific Areas • Biodiversity — BioVeL project • Digital Preservation — SCAPE project • Astronomy — AstroTaverna product • Solar Wind Physics — HELIO project • In silico Medicine — VPH-Share project NERSC Workflow Day 6 Biodiversity: BioVeL • Virtual e-LaBoratory for -
Exascale Computing Project -- Software
Exascale Computing Project -- Software Paul Messina, ECP Director Stephen Lee, ECP Deputy Director ASCAC Meeting, Arlington, VA Crystal City Marriott April 19, 2017 www.ExascaleProject.org ECP scope and goals Develop applications Partner with vendors to tackle a broad to develop computer spectrum of mission Support architectures that critical problems national security support exascale of unprecedented applications complexity Develop a software Train a next-generation Contribute to the stack that is both workforce of economic exascale-capable and computational competitiveness usable on industrial & scientists, engineers, of the nation academic scale and computer systems, in collaboration scientists with vendors 2 Exascale Computing Project, www.exascaleproject.org ECP has formulated a holistic approach that uses co- design and integration to achieve capable exascale Application Software Hardware Exascale Development Technology Technology Systems Science and Scalable and Hardware Integrated mission productive technology exascale applications software elements supercomputers Correctness Visualization Data Analysis Applicationsstack Co-Design Programming models, Math libraries and development environment, Tools Frameworks and runtimes System Software, resource Workflows Resilience management threading, Data Memory scheduling, monitoring, and management and Burst control I/O and file buffer system Node OS, runtimes Hardware interface ECP’s work encompasses applications, system software, hardware technologies and architectures, and workforce