QosCosGrid Grid Technologies and Complex System Modelling

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "QosCosGrid Grid Technologies and Complex System Modelling"

Transcription

1 QosCosGrid Grid Technologies and Complex System Modelling Pamela Burrage Krzysztof Kurowski Institute for Molecular Bioscience, University of Queensland, Australia

2 Vision, objectives Complex systems (motivations) Very broad application class with widely varying requirements QosCosGrid grid technologies (motivations) requirements, advanced capabilities, prototype Examples: complex systems from molecular bioscience - use cases 3 and 5 Overview and main characteristics Protein interactions, lipid rafts Metabolic pathways Design and development Overview

3 EU 6th Framework Programme STREP Project 2.5 years, ends in 03/ Euro Strong QCG Consortium: 11 partners (2 private companies) from 10 countries Technical Manager DIISR Australian funding, 2 years, ends 06/2009 QCG in numbers

4 Gap and Vision Gap = Vision Reality Still wide range of demanding applications and complex systems run only on supercomputers and/or local clusters QosCosGrid vision To address & (make first step towards closing) this gap by developing a quasi-opportunistic supercomputer based on advanced grid middleware and new programming and execution environments Quasi-opportunistic supercomputer (QoS) Quasi-opportunistic = not really opportunistic Qos uses grid technologies to deliver supercomputer-like performance Qos facilitates execution of demanding parallel and distributed applications in grids through key technologies bridging the visionreality gap of the grid

5 QCG Objectives 1. To develop tools for end users and complex system developers Fault-tolerant cluster-to-cluster message passing libraries based on Open MPI (C/C++/Python) and ProActive (Java) Remote complex system steering and control capabilities User and admin easy-to-use web interfaces based on GridSphere/Vine toolkits 2. To develop advanced grid middleware - Dynamic resource brokering (for complex systems simulations) Reservation and orchestration of resources, communication, synchronization and routing as known from massively parallel processors computers 3. Integrate and evaluate QCG concept with various types of complex systems (9 example use cases) and running simulations on a real prototype testbed

6 Complex Systems gridification Developers/Users 1. Real problem 2. Problem decomposition (including algorithm and communication structure design) 3. Agglomeration 4. Mapping 5. Execution and Control QCG grid middleware

7 Complex Systems categorization EGEE or TeraGrid middleware T0: No communications T1: Explicitly defined, static comm. graph T2: Explicitly defined, dynamic comm. graph T3: Cellular automata T4: Distance-dependent communication T5: Unknown (random) communication QCG grid middleware

8 RECV Does it matter how it goes? It took around 0.3 sec and the average data transfer was 0.1 Mb/s GEANT2 SEND AARnet

9 it is important not for use case developers but for the QCG grid middleware, (fully transparent for CSS developers, using same well known APIs based on MPI or ProActive/RPC) * Use case users using the QCG grid middleware have to provide only a list of requirements for their CSS (number of processes, network topologies, networks speed, hardware architectures, stage-in/out data, etc.) and then the QCG grid middleware will take care of: security (sensitive data, identity/authentication, authorization and accounting with different administrative domains) monitoring of computing, storage and network resources in our international testbed load balancing, advanced reservation and co-allocation of computing resources required for multi domain experiments parallel and distributed application control and steering * It is partially true

10 QosCosGrid features usability (e.g. user interface) web based interfaces for scientific users AND administrators command line tools also provided for advanced users and administrators a set of tutorials, guides, template solutions and best practices available for application developers security and trust: improved authentication and single sign-on mechanisms via web for end users improved authorization, policy control and enforcement mechanisms via web for administrators Performance, deployment Australians have been already added to QCG!

11 Use Case 5 (Barcelona) goals Simulates a genetic regulatory network. Involves sets of highly-coupled DEs. Need to find globally optimal sets of parameters for a given model. ByoDyn* is a computational package aimed at integrating different types of DEs, through its interface with several publicly available packages Python, BLAS, LAPACK, gnuplot, Uses QosCosGrid environment for optimization of parameters through the use of different techniques. More research in this area is conducted in the BioBridge project: * ByoDyn is using Python

12 Use Case 3 (UQ) goals Prototype lattice of size 250 x 378 voxels: 1µm 1.5µm 1 time step ( 4 µs ) allows each molecule to move. Simulation for T = 1000 is only 0.004s real time. 600nm x 600nm, 25% rafts, fences, 2000 proteins, some obstacles: 2 mins compute-time, 0.004s real time. 600nm x 600nm, 50% rafts, proteins, FRAP: approx. 2 weeks on a PC, for 4s real time. To model a membrane 100 times as large, up to several real time seconds.

13 Parallel Implementation Master-slave implementation. Split membrane into vertical strips, one per slave. Track proteins as they move between slaves (unique ID). Provide report data and visualisation capability via the master.

14 Implementation contd. Visualisation capability Slaves send data to master (one large file) Front-end security via certificate signing Animation or snapshots at certain times

15 Implementation Issues Message passing via master more robust than slave-slave. Master outputs all files. Each slave has LH and RH overlap of neighbour s membrane. How much overlap? How often communicate? System integrity maintaining system dynamics.

16 Performance Compare timings for multi-processor computer for local clusters for remote clusters Speed-up vs frequency of communications vs volume of communication (size of membrane overlap)

17 Timings Examples

18 Technical Support Krzysztof Kurowski Original Development of Model Dan Nicolau (UQ, Oxford) John Hancock (UQ, Texas) Kevin Burrage (UQ, Oxford) Project Support Prof. Mark Ragan Other Programming Support Martin Swain (Ulster) Michal Lorenc (Hamburg) Use Case Discussions Jordi Villa i Freixa (Barcelona) George Kampis (Budapest) Acknowledgements

19 QCG Testbed prototype

Cellular Computing on a Linux Cluster

Cellular Computing on a Linux Cluster Cellular Computing on a Linux Cluster Alexei Agueev, Bernd Däne, Wolfgang Fengler TU Ilmenau, Department of Computer Architecture Topics 1. Cellular Computing 2. The Experiment 3. Experimental Results

More information

The Mantid Project. The challenges of delivering flexible HPC for novice end users. Nicholas Draper SOS18

The Mantid Project. The challenges of delivering flexible HPC for novice end users. Nicholas Draper SOS18 The Mantid Project The challenges of delivering flexible HPC for novice end users Nicholas Draper SOS18 What Is Mantid A framework that supports high-performance computing and visualisation of scientific

More information

Write a technical report Present your results Write a workshop/conference paper (optional) Could be a real system, simulation and/or theoretical

Write a technical report Present your results Write a workshop/conference paper (optional) Could be a real system, simulation and/or theoretical Identify a problem Review approaches to the problem Propose a novel approach to the problem Define, design, prototype an implementation to evaluate your approach Could be a real system, simulation and/or

More information

Cluster, Grid, Cloud Concepts

Cluster, Grid, Cloud Concepts Cluster, Grid, Cloud Concepts Kalaiselvan.K Contents Section 1: Cluster Section 2: Grid Section 3: Cloud Cluster An Overview Need for a Cluster Cluster categorizations A computer cluster is a group of

More information

BSC vision on Big Data and extreme scale computing

BSC vision on Big Data and extreme scale computing BSC vision on Big Data and extreme scale computing Jesus Labarta, Eduard Ayguade,, Fabrizio Gagliardi, Rosa M. Badia, Toni Cortes, Jordi Torres, Adrian Cristal, Osman Unsal, David Carrera, Yolanda Becerra,

More information

Grid Scheduling Architectures with Globus GridWay and Sun Grid Engine

Grid Scheduling Architectures with Globus GridWay and Sun Grid Engine Grid Scheduling Architectures with and Sun Grid Engine Sun Grid Engine Workshop 2007 Regensburg, Germany September 11, 2007 Ignacio Martin Llorente Javier Fontán Muiños Distributed Systems Architecture

More information

An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications

An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications Rajkumar Buyya, Jonathan Giddy, and David Abramson School of Computer Science

More information

MEng, BSc Applied Computer Science

MEng, BSc Applied Computer Science School of Computing FACULTY OF ENGINEERING MEng, BSc Applied Computer Science Year 1 COMP1212 Computer Processor Effective programming depends on understanding not only how to give a machine instructions

More information

A Service for Data-Intensive Computations on Virtual Clusters

A Service for Data-Intensive Computations on Virtual Clusters A Service for Data-Intensive Computations on Virtual Clusters Executing Preservation Strategies at Scale Rainer Schmidt, Christian Sadilek, and Ross King rainer.schmidt@arcs.ac.at Planets Project Permanent

More information

Towards a New Model for the Infrastructure Grid

Towards a New Model for the Infrastructure Grid INTERNATIONAL ADVANCED RESEARCH WORKSHOP ON HIGH PERFORMANCE COMPUTING AND GRIDS Cetraro (Italy), June 30 - July 4, 2008 Panel: From Grids to Cloud Services Towards a New Model for the Infrastructure Grid

More information

Mr. Apichon Witayangkurn apichon@iis.u-tokyo.ac.jp Department of Civil Engineering The University of Tokyo

Mr. Apichon Witayangkurn apichon@iis.u-tokyo.ac.jp Department of Civil Engineering The University of Tokyo Sensor Network Messaging Service Hive/Hadoop Mr. Apichon Witayangkurn apichon@iis.u-tokyo.ac.jp Department of Civil Engineering The University of Tokyo Contents 1 Introduction 2 What & Why Sensor Network

More information

Middleware and Distributed Systems. Introduction. Dr. Martin v. Löwis

Middleware and Distributed Systems. Introduction. Dr. Martin v. Löwis Middleware and Distributed Systems Introduction Dr. Martin v. Löwis 14 3. Software Engineering What is Middleware? Bauer et al. Software Engineering, Report on a conference sponsored by the NATO SCIENCE

More information

Concepts and Architecture of the Grid. Summary of Grid 2, Chapter 4

Concepts and Architecture of the Grid. Summary of Grid 2, Chapter 4 Concepts and Architecture of the Grid Summary of Grid 2, Chapter 4 Concepts of Grid Mantra: Coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations Allows

More information

Sector vs. Hadoop. A Brief Comparison Between the Two Systems

Sector vs. Hadoop. A Brief Comparison Between the Two Systems Sector vs. Hadoop A Brief Comparison Between the Two Systems Background Sector is a relatively new system that is broadly comparable to Hadoop, and people want to know what are the differences. Is Sector

More information

The Lattice Project: A Multi-Model Grid Computing System. Center for Bioinformatics and Computational Biology University of Maryland

The Lattice Project: A Multi-Model Grid Computing System. Center for Bioinformatics and Computational Biology University of Maryland The Lattice Project: A Multi-Model Grid Computing System Center for Bioinformatics and Computational Biology University of Maryland Parallel Computing PARALLEL COMPUTING a form of computation in which

More information

Grid based Integration of Real-Time Value-at-Risk (VaR) Services. Abstract

Grid based Integration of Real-Time Value-at-Risk (VaR) Services. Abstract Grid based Integration of Real-Time Value-at-Risk (VaR) s Paul Donachy Daniel Stødle Terrence J harmer Ron H Perrott Belfast e-science Centre www.qub.ac.uk/escience Brian Conlon Gavan Corr First Derivatives

More information

:Introducing Star-P. The Open Platform for Parallel Application Development. Yoel Jacobsen E&M Computing LTD yoel@emet.co.il

:Introducing Star-P. The Open Platform for Parallel Application Development. Yoel Jacobsen E&M Computing LTD yoel@emet.co.il :Introducing Star-P The Open Platform for Parallel Application Development Yoel Jacobsen E&M Computing LTD yoel@emet.co.il The case for VHLLs Functional / applicative / very high-level languages allow

More information

Remote Graphical Visualization of Large Interactive Spatial Data

Remote Graphical Visualization of Large Interactive Spatial Data Remote Graphical Visualization of Large Interactive Spatial Data ComplexHPC Spring School 2011 International ComplexHPC Challenge Cristinel Mihai Mocan Computer Science Department Technical University

More information

Introduction to Cluster Computing

Introduction to Cluster Computing Introduction to Cluster Computing Brian Vinter vinter@diku.dk Overview Introduction Goal/Idea Phases Mandatory Assignments Tools Timeline/Exam General info Introduction Supercomputers are expensive Workstations

More information

Building Platform as a Service for Scientific Applications

Building Platform as a Service for Scientific Applications Building Platform as a Service for Scientific Applications Moustafa AbdelBaky moustafa@cac.rutgers.edu Rutgers Discovery Informa=cs Ins=tute (RDI 2 ) The NSF Cloud and Autonomic Compu=ng Center Department

More information

Scalable Services for Digital Preservation

Scalable Services for Digital Preservation Scalable Services for Digital Preservation A Perspective on Cloud Computing Rainer Schmidt, Christian Sadilek, and Ross King Digital Preservation (DP) Providing long-term access to growing collections

More information

MEng, BSc Computer Science with Artificial Intelligence

MEng, BSc Computer Science with Artificial Intelligence School of Computing FACULTY OF ENGINEERING MEng, BSc Computer Science with Artificial Intelligence Year 1 COMP1212 Computer Processor Effective programming depends on understanding not only how to give

More information

Clouds vs Grids KHALID ELGAZZAR GOODWIN 531 ELGAZZAR@CS.QUEENSU.CA

Clouds vs Grids KHALID ELGAZZAR GOODWIN 531 ELGAZZAR@CS.QUEENSU.CA Clouds vs Grids KHALID ELGAZZAR GOODWIN 531 ELGAZZAR@CS.QUEENSU.CA [REF] I Foster, Y Zhao, I Raicu, S Lu, Cloud computing and grid computing 360-degree compared Grid Computing Environments Workshop, 2008.

More information

Challenges for cloud software engineering

Challenges for cloud software engineering Challenges for cloud software engineering Ian Sommerville St Andrews University Why is cloud software engineering different or is it? What needs to be done to make cloud software engineering easier for

More information

Analytic Modeling in Python

Analytic Modeling in Python Analytic Modeling in Python Why Choose Python for Analytic Modeling A White Paper by Visual Numerics August 2009 www.vni.com Analytic Modeling in Python Why Choose Python for Analytic Modeling by Visual

More information

Scalability and Classifications

Scalability and Classifications Scalability and Classifications 1 Types of Parallel Computers MIMD and SIMD classifications shared and distributed memory multicomputers distributed shared memory computers 2 Network Topologies static

More information

SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS

SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS Foued Jrad, Jie Tao and Achim Streit Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany {foued.jrad, jie.tao, achim.streit}@kit.edu

More information

Scheduling and Resource Management in Computational Mini-Grids

Scheduling and Resource Management in Computational Mini-Grids Scheduling and Resource Management in Computational Mini-Grids July 1, 2002 Project Description The concept of grid computing is becoming a more and more important one in the high performance computing

More information

A Grid for process control

A Grid for process control A Grid for process control Fabrice Sabatier, S u p é l e c, F a b r i c e. S a b a t i e r @m e t z. s u p e l e c. f r Amelia De Vivo, U n i v e r s i t a d i S a l e r n o, a m e d e v @ u n i s a. i

More information

Cloud Computing Simulation Using CloudSim

Cloud Computing Simulation Using CloudSim Cloud Computing Simulation Using CloudSim Ranjan Kumar #1, G.Sahoo *2 # Assistant Professor, Computer Science & Engineering, Ranchi University, India Professor & Head, Information Technology, Birla Institute

More information

A SIMULATOR FOR LOAD BALANCING ANALYSIS IN DISTRIBUTED SYSTEMS

A SIMULATOR FOR LOAD BALANCING ANALYSIS IN DISTRIBUTED SYSTEMS Mihai Horia Zaharia, Florin Leon, Dan Galea (3) A Simulator for Load Balancing Analysis in Distributed Systems in A. Valachi, D. Galea, A. M. Florea, M. Craus (eds.) - Tehnologii informationale, Editura

More information

Introduction to Hadoop and MapReduce

Introduction to Hadoop and MapReduce Introduction to Hadoop and MapReduce THE CONTRACTOR IS ACTING UNDER A FRAMEWORK CONTRACT CONCLUDED WITH THE COMMISSION Large-scale Computation Traditional solutions for computing large quantities of data

More information

Leittechnik für Bahnsysteme mit Eclipse

Leittechnik für Bahnsysteme mit Eclipse DB AG/Christian Bedeschinski www.thalesgroup.com/germany Leittechnik für Bahnsysteme mit Eclipse Software-Entwicklung bei Thales Transportation Systems GmbH Christian Scholz 2 / Content HMI for Railway

More information

Objectives. Distributed Databases and Client/Server Architecture. Distributed Database. Data Fragmentation

Objectives. Distributed Databases and Client/Server Architecture. Distributed Database. Data Fragmentation Objectives Distributed Databases and Client/Server Architecture IT354 @ Peter Lo 2005 1 Understand the advantages and disadvantages of distributed databases Know the design issues involved in distributed

More information

Digital libraries of the future and the role of libraries

Digital libraries of the future and the role of libraries Digital libraries of the future and the role of libraries Donatella Castelli ISTI-CNR, Pisa, Italy Abstract Purpose: To introduce the digital libraries of the future, their enabling technologies and their

More information

Audio networking. François Déchelle (dechelle@ircam.fr) Patrice Tisserand (tisserand@ircam.fr) Simon Schampijer (schampij@ircam.

Audio networking. François Déchelle (dechelle@ircam.fr) Patrice Tisserand (tisserand@ircam.fr) Simon Schampijer (schampij@ircam. Audio networking François Déchelle (dechelle@ircam.fr) Patrice Tisserand (tisserand@ircam.fr) Simon Schampijer (schampij@ircam.fr) IRCAM Distributed virtual concert project and issues network protocols

More information

A Chromium Based Viewer for CUMULVS

A Chromium Based Viewer for CUMULVS A Chromium Based Viewer for CUMULVS Submitted to PDPTA 06 Dan Bennett Corresponding Author Department of Mathematics and Computer Science Edinboro University of PA Edinboro, Pennsylvania 16444 Phone: (814)

More information

Science Gateways and scalable application tools with QosCosGrid (QCG) for large communities in Grids and Clouds

Science Gateways and scalable application tools with QosCosGrid (QCG) for large communities in Grids and Clouds Science Gateways and scalable application tools with QosCosGrid (QCG) for large communities in Grids and Clouds Tomasz Piontek, Krzysztof Kurowski and Dawid Szejnfeld [piontek, krzysztof.kurowski, dejw]@man.poznan.pl

More information

MIKE by DHI 2014 e sviluppi futuri

MIKE by DHI 2014 e sviluppi futuri MIKE by DHI 2014 e sviluppi futuri Johan Hartnack Torino, 9-10 Ottobre 2013 Technology drivers/trends Smart devices Cloud computing Services vs. Products Technology drivers/trends Multiprocessor hardware

More information

On-Demand Supercomputing Multiplies the Possibilities

On-Demand Supercomputing Multiplies the Possibilities Microsoft Windows Compute Cluster Server 2003 Partner Solution Brief Image courtesy of Wolfram Research, Inc. On-Demand Supercomputing Multiplies the Possibilities Microsoft Windows Compute Cluster Server

More information

Denis Caromel, CEO Ac.veEon. Orchestrate and Accelerate Applica.ons. Open Source Cloud Solu.ons Hybrid Cloud: Private with Burst Capacity

Denis Caromel, CEO Ac.veEon. Orchestrate and Accelerate Applica.ons. Open Source Cloud Solu.ons Hybrid Cloud: Private with Burst Capacity Cloud computing et Virtualisation : applications au domaine de la Finance Denis Caromel, CEO Ac.veEon Orchestrate and Accelerate Applica.ons Open Source Cloud Solu.ons Hybrid Cloud: Private with Burst

More information

CHAPTER 1: OPERATING SYSTEM FUNDAMENTALS

CHAPTER 1: OPERATING SYSTEM FUNDAMENTALS CHAPTER 1: OPERATING SYSTEM FUNDAMENTALS What is an operating? A collection of software modules to assist programmers in enhancing efficiency, flexibility, and robustness An Extended Machine from the users

More information

A Flexible Cluster Infrastructure for Systems Research and Software Development

A Flexible Cluster Infrastructure for Systems Research and Software Development Award Number: CNS-551555 Title: CRI: Acquisition of an InfiniBand Cluster with SMP Nodes Institution: Florida State University PIs: Xin Yuan, Robert van Engelen, Kartik Gopalan A Flexible Cluster Infrastructure

More information

Hadoop and Map-Reduce. Swati Gore

Hadoop and Map-Reduce. Swati Gore Hadoop and Map-Reduce Swati Gore Contents Why Hadoop? Hadoop Overview Hadoop Architecture Working Description Fault Tolerance Limitations Why Map-Reduce not MPI Distributed sort Why Hadoop? Existing Data

More information

CLOUD COMPUTING. When It's smarter to rent than to buy

CLOUD COMPUTING. When It's smarter to rent than to buy CLOUD COMPUTING When It's smarter to rent than to buy Is it new concept? Nothing new In 1990 s, WWW itself Grid Technologies- Scientific applications Online banking websites More convenience Not to visit

More information

Multi-Channel Clustered Web Application Servers

Multi-Channel Clustered Web Application Servers THE AMERICAN UNIVERSITY IN CAIRO SCHOOL OF SCIENCES AND ENGINEERING Multi-Channel Clustered Web Application Servers A Masters Thesis Department of Computer Science and Engineering Status Report Seminar

More information

Beyond brokering: Proactive Role of Cloud Federations in Resource Management

Beyond brokering: Proactive Role of Cloud Federations in Resource Management Beyond brokering: Proactive Role of Cloud Federations in Resource Management Massimo Coppola (CNR-ISTI)! contrail is co-funded by the EC 7th Framework Programme under Grant greement nr. 257438 http://contrail-project.eu

More information

presentation Contact information: www.nglogic.com nglogic@nglogic.com + 48 505 091 662 + 48 22 398 743

presentation Contact information: www.nglogic.com nglogic@nglogic.com + 48 505 091 662 + 48 22 398 743 Company presentation Contact information: www.nglogic.com nglogic@nglogic.com + 48 505 091 662 + 48 22 398 743 Introduction NG Logic NG Logic is a young, dynamically expanding company located in Warsaw,

More information

Seeking Opportunities for Hardware Acceleration in Big Data Analytics

Seeking Opportunities for Hardware Acceleration in Big Data Analytics Seeking Opportunities for Hardware Acceleration in Big Data Analytics Paul Chow High-Performance Reconfigurable Computing Group Department of Electrical and Computer Engineering University of Toronto Who

More information

Cloud Platforms, Challenges & Hadoop. Aditee Rele Karpagam Venkataraman Janani Ravi

Cloud Platforms, Challenges & Hadoop. Aditee Rele Karpagam Venkataraman Janani Ravi Cloud Platforms, Challenges & Hadoop Aditee Rele Karpagam Venkataraman Janani Ravi Cloud Platform Models Aditee Rele Microsoft Corporation Dec 8, 2010 IT CAPACITY Provisioning IT Capacity Under-supply

More information

Key Research Challenges in Cloud Computing

Key Research Challenges in Cloud Computing 3rd EU-Japan Symposium on Future Internet and New Generation Networks Tampere, Finland October 20th, 2010 Key Research Challenges in Cloud Computing Ignacio M. Llorente Head of DSA Research Group Universidad

More information

IDL. Get the answers you need from your data. IDL

IDL. Get the answers you need from your data. IDL Get the answers you need from your data. IDL is the preferred computing environment for understanding complex data through interactive visualization and analysis. IDL Powerful visualization. Interactive

More information

Service Oriented Architectures

Service Oriented Architectures 8 Service Oriented Architectures Gustavo Alonso Computer Science Department Swiss Federal Institute of Technology (ETHZ) alonso@inf.ethz.ch http://www.iks.inf.ethz.ch/ The context for SOA A bit of history

More information

Sensing, monitoring and actuating on the UNderwater world through a federated Research InfraStructure Extending the Future Internet SUNRISE

Sensing, monitoring and actuating on the UNderwater world through a federated Research InfraStructure Extending the Future Internet SUNRISE Sensing, monitoring and actuating on the UNderwater world through a federated Research InfraStructure Extending the Future Internet SUNRISE Grant Agreement number 611449 Announcement of the Second Competitive

More information

PARALLEL & CLUSTER COMPUTING CS 6260 PROFESSOR: ELISE DE DONCKER BY: LINA HUSSEIN

PARALLEL & CLUSTER COMPUTING CS 6260 PROFESSOR: ELISE DE DONCKER BY: LINA HUSSEIN 1 PARALLEL & CLUSTER COMPUTING CS 6260 PROFESSOR: ELISE DE DONCKER BY: LINA HUSSEIN Introduction What is cluster computing? Classification of Cluster Computing Technologies: Beowulf cluster Construction

More information

Cloud Federations in Contrail

Cloud Federations in Contrail Cloud Federations in Contrail Emanuele Carlini 1,3, Massimo Coppola 1, Patrizio Dazzi 1, Laura Ricci 1,2, GiacomoRighetti 1,2 " 1 - CNR - ISTI, Pisa, Italy" 2 - University of Pisa, C.S. Dept" 3 - IMT Lucca,

More information

The Fastest Way to Parallel Programming for Multicore, Clusters, Supercomputers and the Cloud.

The Fastest Way to Parallel Programming for Multicore, Clusters, Supercomputers and the Cloud. White Paper 021313-3 Page 1 : A Software Framework for Parallel Programming* The Fastest Way to Parallel Programming for Multicore, Clusters, Supercomputers and the Cloud. ABSTRACT Programming for Multicore,

More information

Experimental Awareness of CO 2 in Federated Cloud Sourcing

Experimental Awareness of CO 2 in Federated Cloud Sourcing Experimental Awareness of CO 2 in Federated Cloud Sourcing Julia Wells, Atos Spain This project is partially funded by European Commission under the 7th Framework Programme - Grant agreement no. 318048

More information

Scientific Computing Programming with Parallel Objects

Scientific Computing Programming with Parallel Objects Scientific Computing Programming with Parallel Objects Esteban Meneses, PhD School of Computing, Costa Rica Institute of Technology Parallel Architectures Galore Personal Computing Embedded Computing Moore

More information

Lightpath Planning and Monitoring

Lightpath Planning and Monitoring Lightpath Planning and Monitoring Ronald van der Pol 1, Andree Toonk 2 1 SARA, Kruislaan 415, Amsterdam, 1098 SJ, The Netherlands Tel: +31205928000, Fax: +31206683167, Email: rvdp@sara.nl 2 SARA, Kruislaan

More information

SCI-BUS gateways for grid and cloud infrastructures

SCI-BUS gateways for grid and cloud infrastructures SCI-BUS gateways for grid and cloud infrastructures Tamas Kiss University of Westminster Peter Kacsuk, Zoltan Farkas MTA SZTAKI VERCE project meeting 1 st February 2013, Edinburgh SCI-BUS is supported

More information

A Case Study - Scaling Legacy Code on Next Generation Platforms

A Case Study - Scaling Legacy Code on Next Generation Platforms Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 00 (2015) 000 000 www.elsevier.com/locate/procedia 24th International Meshing Roundtable (IMR24) A Case Study - Scaling Legacy

More information

Stream Processing on GPUs Using Distributed Multimedia Middleware

Stream Processing on GPUs Using Distributed Multimedia Middleware Stream Processing on GPUs Using Distributed Multimedia Middleware Michael Repplinger 1,2, and Philipp Slusallek 1,2 1 Computer Graphics Lab, Saarland University, Saarbrücken, Germany 2 German Research

More information

Improvements of the grid infrastructure and services within SEE-GRID

Improvements of the grid infrastructure and services within SEE-GRID Improvements of the grid infrastructure and services within SEE-GRID Anastas Misev MARNET/MARGI/UKIM Macedonia Introduction SEE-GRID Project series SEE-GRID establish infrastructure SEE-GRID-2 extend infrastructure,

More information

Analyses on functional capabilities of BizTalk Server, Oracle BPEL Process Manger and WebSphere Process Server for applications in Grid middleware

Analyses on functional capabilities of BizTalk Server, Oracle BPEL Process Manger and WebSphere Process Server for applications in Grid middleware Analyses on functional capabilities of BizTalk Server, Oracle BPEL Process Manger and WebSphere Process Server for applications in Grid middleware R. Goranova University of Sofia St. Kliment Ohridski,

More information

An approach to grid scheduling by using Condor-G Matchmaking mechanism

An approach to grid scheduling by using Condor-G Matchmaking mechanism An approach to grid scheduling by using Condor-G Matchmaking mechanism E. Imamagic, B. Radic, D. Dobrenic University Computing Centre, University of Zagreb, Croatia {emir.imamagic, branimir.radic, dobrisa.dobrenic}@srce.hr

More information

WORKFLOW ENGINE FOR CLOUDS

WORKFLOW ENGINE FOR CLOUDS WORKFLOW ENGINE FOR CLOUDS By SURAJ PANDEY, DILEBAN KARUNAMOORTHY, and RAJKUMAR BUYYA Prepared by: Dr. Faramarz Safi Islamic Azad University, Najafabad Branch, Esfahan, Iran. Workflow Engine for clouds

More information

Command and Control of a Massively Parallel GALS Environment

Command and Control of a Massively Parallel GALS Environment Cameron Patterson Supervisor: Steve Furber SpiNNaker Team, APT Group, University of Manchester, UK. Command and Control of a Massively Parallel GALS Environment 1 SpiNNaker Management ASIC for modelling

More information

Status and Integration of AP2 Monitoring and Online Steering

Status and Integration of AP2 Monitoring and Online Steering Status and Integration of AP2 Monitoring and Online Steering Daniel Lorenz - University of Siegen Stefan Borovac, Markus Mechtel - University of Wuppertal Ralph Müller-Pfefferkorn Technische Universität

More information

The Virtual Grid Application Development Software (VGrADS) Project

The Virtual Grid Application Development Software (VGrADS) Project The Virtual Grid Application Development Software (VGrADS) Project VGrADS: Enabling e-science Workflows on Grids and Clouds with Fault Tolerance http://vgrads.rice.edu/ VGrADS Goal: Distributed Problem

More information

GRID Computing and Networks

GRID Computing and Networks A Member of the ExperTeam Group GRID Computing and Networks Karl Solchenbach Global IPv6 Summit Madrid, May 14, 2003 Pallas GmbH Hermülheimer Straße 10 D-50321 Brühl, Germany info@pallas.de http://www.pallas.com

More information

2.1 What are distributed systems? What are systems? Different kind of systems How to distribute systems? 2.2 Communication concepts

2.1 What are distributed systems? What are systems? Different kind of systems How to distribute systems? 2.2 Communication concepts Chapter 2 Introduction to Distributed systems 1 Chapter 2 2.1 What are distributed systems? What are systems? Different kind of systems How to distribute systems? 2.2 Communication concepts Client-Server

More information

Microsoft Research Worldwide Presence

Microsoft Research Worldwide Presence Microsoft Research Worldwide Presence MSR India MSR New England Redmond Redmond, Washington Sept, 1991 San Francisco, California Jun, 1995 Cambridge, United Kingdom July, 1997 Beijing, China Nov, 1998

More information

Speeding up MATLAB and Simulink Applications

Speeding up MATLAB and Simulink Applications Speeding up MATLAB and Simulink Applications 2009 The MathWorks, Inc. Customer Tour 2009 Today s Schedule Introduction to Parallel Computing with MATLAB and Simulink Break Master Class on Speeding Up MATLAB

More information

Introduction to grid technologies, parallel and cloud computing. Alaa Osama Allam Saida Saad Mohamed Mohamed Ibrahim Gaber

Introduction to grid technologies, parallel and cloud computing. Alaa Osama Allam Saida Saad Mohamed Mohamed Ibrahim Gaber Introduction to grid technologies, parallel and cloud computing Alaa Osama Allam Saida Saad Mohamed Mohamed Ibrahim Gaber OUTLINES Grid Computing Parallel programming technologies (MPI- Open MP-Cuda )

More information

Load Balancing on a Non-dedicated Heterogeneous Network of Workstations

Load Balancing on a Non-dedicated Heterogeneous Network of Workstations Load Balancing on a Non-dedicated Heterogeneous Network of Workstations Dr. Maurice Eggen Nathan Franklin Department of Computer Science Trinity University San Antonio, Texas 78212 Dr. Roger Eggen Department

More information

Australian Research Collaboration Service (ARCS) & Grid Activities in Australia

Australian Research Collaboration Service (ARCS) & Grid Activities in Australia Australian Research Collaboration Service (ARCS) & Grid Activities in Australia Prof Anthony Williams Executive Director Supported by: ARCS Mission The ARCS Mission is to enable and enhance research through

More information

ANALYSIS OF GRID COMPUTING AS IT APPLIES TO HIGH VOLUME DOCUMENT PROCESSING AND OCR

ANALYSIS OF GRID COMPUTING AS IT APPLIES TO HIGH VOLUME DOCUMENT PROCESSING AND OCR ANALYSIS OF GRID COMPUTING AS IT APPLIES TO HIGH VOLUME DOCUMENT PROCESSING AND OCR By: Dmitri Ilkaev, Stephen Pearson Abstract: In this paper we analyze the concept of grid programming as it applies to

More information

Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing

Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing /35 Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing Zuhair Khayyat 1 Karim Awara 1 Amani Alonazi 1 Hani Jamjoom 2 Dan Williams 2 Panos Kalnis 1 1 King Abdullah University of

More information

CHAPTER 6 MAJOR RESULTS AND CONCLUSIONS

CHAPTER 6 MAJOR RESULTS AND CONCLUSIONS 133 CHAPTER 6 MAJOR RESULTS AND CONCLUSIONS The proposed scheduling algorithms along with the heuristic intensive weightage factors, parameters and ß and their impact on the performance of the algorithms

More information

Using WestGrid. Patrick Mann, Manager, Technical Operations Jan.15, 2014

Using WestGrid. Patrick Mann, Manager, Technical Operations Jan.15, 2014 Using WestGrid Patrick Mann, Manager, Technical Operations Jan.15, 2014 Winter 2014 Seminar Series Date Speaker Topic 5 February Gino DiLabio Molecular Modelling Using HPC and Gaussian 26 February Jonathan

More information

GPUs for Scientific Computing

GPUs for Scientific Computing GPUs for Scientific Computing p. 1/16 GPUs for Scientific Computing Mike Giles mike.giles@maths.ox.ac.uk Oxford-Man Institute of Quantitative Finance Oxford University Mathematical Institute Oxford e-research

More information

EFFICIENT SCHEDULING STRATEGY USING COMMUNICATION AWARE SCHEDULING FOR PARALLEL JOBS IN CLUSTERS

EFFICIENT SCHEDULING STRATEGY USING COMMUNICATION AWARE SCHEDULING FOR PARALLEL JOBS IN CLUSTERS EFFICIENT SCHEDULING STRATEGY USING COMMUNICATION AWARE SCHEDULING FOR PARALLEL JOBS IN CLUSTERS A.Neela madheswari 1 and R.S.D.Wahida Banu 2 1 Department of Information Technology, KMEA Engineering College,

More information

The Data Grid: Towards an Architecture for Distributed Management and Analysis of Large Scientific Datasets

The Data Grid: Towards an Architecture for Distributed Management and Analysis of Large Scientific Datasets The Data Grid: Towards an Architecture for Distributed Management and Analysis of Large Scientific Datasets!! Large data collections appear in many scientific domains like climate studies.!! Users and

More information

A Survey Study on Monitoring Service for Grid

A Survey Study on Monitoring Service for Grid A Survey Study on Monitoring Service for Grid Erkang You erkyou@indiana.edu ABSTRACT Grid is a distributed system that integrates heterogeneous systems into a single transparent computer, aiming to provide

More information

Dynamism and Data Management in Distributed, Collaborative Working Environments

Dynamism and Data Management in Distributed, Collaborative Working Environments Dynamism and Data Management in Distributed, Collaborative Working Environments Alexander Kipp 1, Lutz Schubert 1, Matthias Assel 1 and Terrence Fernando 2, 1 High Performance Computing Center Stuttgart,

More information

Operating System Support for Multiprocessor Systems-on-Chip

Operating System Support for Multiprocessor Systems-on-Chip Operating System Support for Multiprocessor Systems-on-Chip Dr. Gabriel marchesan almeida Agenda. Introduction. Adaptive System + Shop Architecture. Preliminary Results. Perspectives & Conclusions Dr.

More information

An Introduction to Virtualization and Cloud Technologies to Support Grid Computing

An Introduction to Virtualization and Cloud Technologies to Support Grid Computing New Paradigms: Clouds, Virtualization and Co. EGEE08, Istanbul, September 25, 2008 An Introduction to Virtualization and Cloud Technologies to Support Grid Computing Distributed Systems Architecture Research

More information

Global Grid Forum: Grid Computing Environments Community Practice (CP) Document

Global Grid Forum: Grid Computing Environments Community Practice (CP) Document Global Grid Forum: Grid Computing Environments Community Practice (CP) Document Project Title: Nimrod/G Problem Solving Environment and Computational Economies CP Document Contact: Rajkumar Buyya, rajkumar@csse.monash.edu.au

More information

Apache Hadoop. Alexandru Costan

Apache Hadoop. Alexandru Costan 1 Apache Hadoop Alexandru Costan Big Data Landscape No one-size-fits-all solution: SQL, NoSQL, MapReduce, No standard, except Hadoop 2 Outline What is Hadoop? Who uses it? Architecture HDFS MapReduce Open

More information

Develop a process for applying updates to systems, including verifying properties of the update. Create File Systems

Develop a process for applying updates to systems, including verifying properties of the update. Create File Systems RH413 Manage Software Updates Develop a process for applying updates to systems, including verifying properties of the update. Create File Systems Allocate an advanced file system layout, and use file

More information

DSEARCH: sensitive database searching using distributed computing

DSEARCH: sensitive database searching using distributed computing DSEARCH: sensitive database searching using distributed computing Keane T.M. 1 and Naughton T.J. 1 1 Department of Computer Science, National University of Ireland, Maynooth, Ireland Email: tom.naughton@may.ie

More information

MathCloud: From Software Toolkit to Cloud Platform for Building Computing Services

MathCloud: From Software Toolkit to Cloud Platform for Building Computing Services MathCloud: From Software Toolkit to Cloud Platform for Building Computing s O.V. Sukhoroslov Centre for Grid Technologies and Distributed Computing ISA RAS Moscow Institute for Physics and Technology MathCloud

More information

Data-Aware Service Choreographies through Transparent Data Exchange

Data-Aware Service Choreographies through Transparent Data Exchange Institute of Architecture of Application Systems Data-Aware Service Choreographies through Transparent Data Exchange Michael Hahn, Dimka Karastoyanova, and Frank Leymann Institute of Architecture of Application

More information

Visualisation in the Google Cloud

Visualisation in the Google Cloud Visualisation in the Google Cloud by Kieran Barker, 1 School of Computing, Faculty of Engineering ABSTRACT Providing software as a service is an emerging trend in the computing world. This paper explores

More information

Overlapping Data Transfer With Application Execution on Clusters

Overlapping Data Transfer With Application Execution on Clusters Overlapping Data Transfer With Application Execution on Clusters Karen L. Reid and Michael Stumm reid@cs.toronto.edu stumm@eecg.toronto.edu Department of Computer Science Department of Electrical and Computer

More information

Resource Management and Scheduling. Mechanisms in Grid Computing

Resource Management and Scheduling. Mechanisms in Grid Computing Resource Management and Scheduling Mechanisms in Grid Computing Edgar Magaña Perdomo Universitat Politècnica de Catalunya Network Management Group Barcelona, Spain emagana@nmg.upc.edu http://nmg.upc.es/~emagana/

More information

NOTUR Technology Transfer Projects (TTP)

NOTUR Technology Transfer Projects (TTP) NOTUR Technology Transfer Projects (TTP) By Trond Kvamsdal NOTUR 10. Juni 2004, Tromsø, Norway CONTENTS The concept behind the TTPs Results obtained from the TTPs Concluding remarks Purpose Enable optimal

More information

Grid Computing Vs. Cloud Computing

Grid Computing Vs. Cloud Computing International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 6 (2013), pp. 577-582 International Research Publications House http://www. irphouse.com /ijict.htm Grid

More information

PARALLEL PROGRAMMING

PARALLEL PROGRAMMING PARALLEL PROGRAMMING TECHNIQUES AND APPLICATIONS USING NETWORKED WORKSTATIONS AND PARALLEL COMPUTERS 2nd Edition BARRY WILKINSON University of North Carolina at Charlotte Western Carolina University MICHAEL

More information