# 1 Bull, 2011 Bull Extreme Computing

Save this PDF as:

Size: px
Start display at page:

Download "1 Bull, 2011 Bull Extreme Computing"

## Transcription

1 1 Bull, 2011 Bull Extreme Computing

2 Table of Contents HPC Overview. Cluster Overview. FLOPS. 2 Bull, 2011 Bull Extreme Computing

3 HPC Overview Ares, Gerardo, HPC Team

4 HPC concepts HPC: High Performance Computing. Motivation: - Advances in technologies have helped to address bigger complex problems. In recent years were developed high performance infrastructure for scientific calculus. In the past 10 years clusters architecture has grown from 2.2% to 84.8% (source Architecture June 2000 June2010 Constelations Clusters MPP SMP Bull, 2011 Bull Extreme Computing

5 HPC concepts Sastify the growing requisites of the computing power. - Complex problems. - Complex models. - Huge data sets. - Responses are time-limited. Parallel processesing. - Many process work together to resolve a common problem. - Domain decomposition or Functional parallelism are used to reduce the computing time of a resolution. 5 Bull, 2011 Bull Extreme Computing

6 HPC concepts Domain decomposition. - Many simulations in science and engineering work with a simplified picture of reality in which a computational domain, e.g., some volume of a fluid, is represented as a grid that defines discrete positions for the physical quantities under consideration. - The goal of the simulation is usually the computation of observables on this grid. - A straightforward way to distribute the work involved across workers, i.e. processors, is to assign a part of the grid to each worker. - This is called domain decomposition. 6 Bull, 2011 Bull Extreme Computing

7 HPC concepts Functional decomposition. - Sometimes, solving a complete problem can be split into more or less disjoint subtasks that may have to be executed in some specific order, each one potentially using results of the previous one as input or being completely unrelated up to some point. - The tasks can be worked on in parallel, using appropriate amounts of resources so that load imbalance is kept under control. 7 Bull, 2011 Bull Extreme Computing

8 Cluster concepts Parallel programming: - Processing power (SMP systems). - Net (data communicatons). - Libraries development and programming API. Shared memory processing: - Multithread programming. Distributed processing: - High performance networks. - Independent machines interconnected by a high performance network. - Sincronization: - Available by message passing mechanism. 8 Bull, 2011 Bull Extreme Computing

9 Cluster Overview Ares, Gerardo, HPC Team

10 Cluster overview A Cluster is composed by: - Compute nodes. - Administration nodes: login, administration. - I/O nodes. - Networks: - Application network. - Administration network. - I/O network. Software components: - Operating system. - Compilers. - Scientific and parallel libraries. - Management software. - Resource Manager. 10 Bull, 2011 Bull Extreme Computing

11 Cluster overview 11 Bull, 2011 Bull Extreme Computing

12 UFMG Cluster UFMG cluster: - Compute nodes: veredas[2-107]: 106 nodes. - 2 x Intel Xeon X GHz (4 cores) GB RAM memory. - Administration nodes: veredas0. - Login node. - Cluster administration. - I/O nodes: veredas[0-1]. - NFS. - Networks: - Application network: Infiniband. - Administration network: 1 GbEthernet. - I/O network: 1GbEthernet. 12 Bull, 2011 Bull Extreme Computing

13 UFMG Cluster Software components: - Operating system: RedHat Enterprise Linux Compilers: - Intel C/C++ & Fortran. - GNU gcc & g77. - Scientific libraries: - BLACS, LAPACK, SCALAPACK, FFTW, NETCDF, HDF5, etc. - Parallel libraries: - Bull MPI 2. - Intel MPI. - Management Software: - Bull XBAS 5v3.1u1. - Resource Manager: - SLURM. 13 Bull, 2011 Bull Extreme Computing

14 P demo P demo 14 Bull, 2011 Bull Extreme Computing

15 FLOPS Ares, Gerardo, HPC Team

16 Flops One of the most accepted metrics to evaluate a cluster power is the number of Flops that it could reach. FLOPS is an acronym: FLoating point OPerations per Second. A cluster has a theorical peak of number of FLOPS in double precision. The Intel 5000 family series has a 128 bit SSE register. Each core can run two float operation in a clock tick. As the SSE register is 128 bit long, each core can run 4 double precision float operations. On an Intel Xeon X5355 Quad-core processor can be done 16 double precision float operations on each clock tick. 16 Bull, 2011 Bull Extreme Computing

17 UFMG Flops To get the number of floating point operations per second on a Intel X5355 processor: #Flops = 4 * CPU_speed * number_of_cores) #Flops = 4 * 2.66 * 4 = 42,56 The UFMG cluster has 106 compute node with 2 Intel X5355 processors, so the theoritical peak of the cluster is: Theoritical peak = 106 * 2 * 42,56 = 9022,7 Gflops = 9,0 Tflops. 17 Bull, 2011 Bull Extreme Computing

18 18 Bull, 2011 Bull Extreme Computing

### Building a Top500-class Supercomputing Cluster at LNS-BUAP

Building a Top500-class Supercomputing Cluster at LNS-BUAP Dr. José Luis Ricardo Chávez Dr. Humberto Salazar Ibargüen Dr. Enrique Varela Carlos Laboratorio Nacional de Supercómputo Benemérita Universidad

### Overview of HPC systems and software available within

Overview of HPC systems and software available within Overview Available HPC Systems Ba Cy-Tera Available Visualization Facilities Software Environments HPC System at Bibliotheca Alexandrina SUN cluster

### Agenda. HPC Software Stack. HPC Post-Processing Visualization. Case Study National Scientific Center. European HPC Benchmark Center Montpellier PSSC

HPC Architecture End to End Alexandre Chauvin Agenda HPC Software Stack Visualization National Scientific Center 2 Agenda HPC Software Stack Alexandre Chauvin Typical HPC Software Stack Externes LAN Typical

### GPU System Architecture. Alan Gray EPCC The University of Edinburgh

GPU System Architecture EPCC The University of Edinburgh Outline Why do we want/need accelerators such as GPUs? GPU-CPU comparison Architectural reasons for GPU performance advantages GPU accelerated systems

### Appro Supercomputer Solutions Best Practices Appro 2012 Deployment Successes. Anthony Kenisky, VP of North America Sales

Appro Supercomputer Solutions Best Practices Appro 2012 Deployment Successes Anthony Kenisky, VP of North America Sales About Appro Over 20 Years of Experience 1991 2000 OEM Server Manufacturer 2001-2007

### Cluster Computing at HRI

Cluster Computing at HRI J.S.Bagla Harish-Chandra Research Institute, Chhatnag Road, Jhunsi, Allahabad 211019. E-mail: jasjeet@mri.ernet.in 1 Introduction and some local history High performance computing

### Introduction to High Performance Cluster Computing. Cluster Training for UCL Part 1

Introduction to High Performance Cluster Computing Cluster Training for UCL Part 1 What is HPC HPC = High Performance Computing Includes Supercomputing HPCC = High Performance Cluster Computing Note: these

### 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

### Virtualization of a Cluster Batch System

Virtualization of a Cluster Batch System Christian Baun, Volker Büge, Benjamin Klein, Jens Mielke, Oliver Oberst and Armin Scheurer Die Kooperation von Cluster Batch System Batch system accepts computational

### High Performance Computing in CST STUDIO SUITE

High Performance Computing in CST STUDIO SUITE Felix Wolfheimer GPU Computing Performance Speedup 18 16 14 12 10 8 6 4 2 0 Promo offer for EUC participants: 25% discount for K40 cards Speedup of Solver

### Trends in High-Performance Computing for Power Grid Applications

Trends in High-Performance Computing for Power Grid Applications Franz Franchetti ECE, Carnegie Mellon University www.spiral.net Co-Founder, SpiralGen www.spiralgen.com This talk presents my personal views

### Using the Windows Cluster

Using the Windows Cluster Christian Terboven terboven@rz.rwth aachen.de Center for Computing and Communication RWTH Aachen University Windows HPC 2008 (II) September 17, RWTH Aachen Agenda o Windows Cluster

### Maximize Performance and Scalability of RADIOSS* Structural Analysis Software on Intel Xeon Processor E7 v2 Family-Based Platforms

Maximize Performance and Scalability of RADIOSS* Structural Analysis Software on Family-Based Platforms Executive Summary Complex simulations of structural and systems performance, such as car crash simulations,

### 1 DCSC/AU: HUGE. DeIC Sekretariat 2013-03-12/RB. Bilag 1. DeIC (DCSC) Scientific Computing Installations

Bilag 1 2013-03-12/RB DeIC (DCSC) Scientific Computing Installations DeIC, previously DCSC, currently has a number of scientific computing installations, distributed at five regional operating centres.

### SR-IOV: Performance Benefits for Virtualized Interconnects!

SR-IOV: Performance Benefits for Virtualized Interconnects! Glenn K. Lockwood! Mahidhar Tatineni! Rick Wagner!! July 15, XSEDE14, Atlanta! Background! High Performance Computing (HPC) reaching beyond traditional

### Fortran Program Development with Visual Studio* 2005 ~ Use Intel Visual Fortran with Visual Studio* ~

Fortran Program Development with Visual Studio* 2005 ~ Use Intel Visual Fortran with Visual Studio* ~ 31/Oct/2006 Software &Solutions group * Agenda Features of Intel Fortran Compiler Integrate with Visual

### Application performance analysis on Pilatus

Application performance analysis on Pilatus Abstract The US group at CSCS performed a set of benchmarks on Pilatus, using the three Programming Environments available (GNU, Intel, PGI): the results can

### Cluster Computing in a College of Criminal Justice

Cluster Computing in a College of Criminal Justice Boris Bondarenko and Douglas E. Salane Mathematics & Computer Science Dept. John Jay College of Criminal Justice The City University of New York 2004

### OpenMP Programming on ScaleMP

OpenMP Programming on ScaleMP Dirk Schmidl schmidl@rz.rwth-aachen.de Rechen- und Kommunikationszentrum (RZ) MPI vs. OpenMP MPI distributed address space explicit message passing typically code redesign

### Performance Evaluation of NAS Parallel Benchmarks on Intel Xeon Phi

Performance Evaluation of NAS Parallel Benchmarks on Intel Xeon Phi ICPP 6 th International Workshop on Parallel Programming Models and Systems Software for High-End Computing October 1, 2013 Lyon, France

### Parallel Programming Survey

Christian Terboven 02.09.2014 / Aachen, Germany Stand: 26.08.2014 Version 2.3 IT Center der RWTH Aachen University Agenda Overview: Processor Microarchitecture Shared-Memory

### Multi-Threading Performance on Commodity Multi-Core Processors

Multi-Threading Performance on Commodity Multi-Core Processors Jie Chen and William Watson III Scientific Computing Group Jefferson Lab 12000 Jefferson Ave. Newport News, VA 23606 Organization Introduction

### Accelerating CST MWS Performance with GPU and MPI Computing. CST workshop series

Accelerating CST MWS Performance with GPU and MPI Computing www.cst.com CST workshop series 2010 1 Hardware Based Acceleration Techniques - Overview - Multithreading GPU Computing Distributed Computing

### Multicore Parallel Computing with OpenMP

Multicore Parallel Computing with OpenMP Tan Chee Chiang (SVU/Academic Computing, Computer Centre) 1. OpenMP Programming The death of OpenMP was anticipated when cluster systems rapidly replaced large

### Introduction to Linux and Cluster Basics for the CCR General Computing Cluster

Introduction to Linux and Cluster Basics for the CCR General Computing Cluster Cynthia Cornelius Center for Computational Research University at Buffalo, SUNY 701 Ellicott St Buffalo, NY 14203 Phone: 716-881-8959

### High Performance Computing: A Review of Parallel Computing with ANSYS solutions. Efficient and Smart Solutions for Large Models

High Performance Computing: A Review of Parallel Computing with ANSYS solutions Efficient and Smart Solutions for Large Models 1 Use ANSYS HPC solutions to perform efficient design variations of large

### MANUFACTURING WEATHER FORECASTING SIMULATIONS ON HPC INFRASTRUCTURES

MANUFACTURING WEATHER FORECASTING SIMULATIONS ON HPC INFRASTRUCTURES LADISLAV HLUCHÝ V. ŠIPKOVÁ, M. DOBRUCKÝ, J. BARTOK, B.M. NGUYEN INSTITUTE OF INFORMATICS, SLOVAK ACADEMY OF SCIENCES ECW 2016 - ENVIRONMENTAL

### Manual for using Super Computing Resources

Manual for using Super Computing Resources Super Computing Research and Education Centre at Research Centre for Modeling and Simulation National University of Science and Technology H-12 Campus, Islamabad

### Parallel Processing using the LOTUS cluster

Parallel Processing using the LOTUS cluster Alison Pamment / Cristina del Cano Novales JASMIN/CEMS Workshop February 2015 Overview Parallelising data analysis LOTUS HPC Cluster Job submission on LOTUS

### LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance

11 th International LS-DYNA Users Conference Session # LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance Gilad Shainer 1, Tong Liu 2, Jeff Layton 3, Onur Celebioglu

### benchmarking Amazon EC2 for high-performance scientific computing

Edward Walker benchmarking Amazon EC2 for high-performance scientific computing Edward Walker is a Research Scientist with the Texas Advanced Computing Center at the University of Texas at Austin. He received

### A PERFORMANCE COMPARISON USING HPC BENCHMARKS: WINDOWS HPC SERVER 2008 AND RED HAT ENTERPRISE LINUX 5

A PERFORMANCE COMPARISON USING HPC BENCHMARKS: WINDOWS HPC SERVER 2008 AND RED HAT ENTERPRISE LINUX 5 R. Henschel, S. Teige, H. Li, J. Doleschal, M. S. Mueller October 2010 Contents HPC at Indiana University

### Best Practice Guide Anselm

Bull Extreme Computing at IT4Innovations / VSB Roman Sliva, IT4Innovations / VSB - Technical University of Ostrava Filip Stanek, IT4Innovations / VSB - Technical University of Ostrava May 2013 1 Table

### LS-DYNA: CAE Simulation Software on Linux Clusters

IBM Deep Computing Group LS-DYNA: CAE Simulation Software on Linux Clusters Guangye Li (guangye@us.ibm.com) IBM Deep Computing Team June, 2003 IBM Deep Computing Group Topics Introduction to LS-DYNA LS-DYNA

### High Performance Computing. R P Thangavelu C-MMACS January 23, 2004

High Performance Computing R P Thangavelu C-MMACS January 23, 2004 Agenda What is HPC? Evolution of the HPC base at C-MMACS Current status Projections for the future What is HPC? Why worry about performance?

### An introduction to Fyrkat

Cluster Computing May 25, 2011 How to get an account https://fyrkat.grid.aau.dk/useraccount How to get help https://fyrkat.grid.aau.dk/wiki What is a Cluster Anyway It is NOT something that does any of

### A Study on the Scalability of Hybrid LS-DYNA on Multicore Architectures

11 th International LS-DYNA Users Conference Computing Technology A Study on the Scalability of Hybrid LS-DYNA on Multicore Architectures Yih-Yih Lin Hewlett-Packard Company Abstract In this paper, the

### Cluster performance, how to get the most out of Abel. Ole W. Saastad, Dr.Scient USIT / UAV / FI April 18 th 2013

Cluster performance, how to get the most out of Abel Ole W. Saastad, Dr.Scient USIT / UAV / FI April 18 th 2013 Introduction Architecture x86-64 and NVIDIA Compilers MPI Interconnect Storage Batch queue

### The Asterope compute cluster

The Asterope compute cluster ÅA has a small cluster named asterope.abo.fi with 8 compute nodes Each node has 2 Intel Xeon X5650 processors (6-core) with a total of 24 GB RAM 2 NVIDIA Tesla M2050 GPGPU

### LS DYNA Performance Benchmarks and Profiling. January 2009

LS DYNA Performance Benchmarks and Profiling January 2009 Note The following research was performed under the HPC Advisory Council activities AMD, Dell, Mellanox HPC Advisory Council Cluster Center The

### Improved LS-DYNA Performance on Sun Servers

8 th International LS-DYNA Users Conference Computing / Code Tech (2) Improved LS-DYNA Performance on Sun Servers Youn-Seo Roh, Ph.D. And Henry H. Fong Sun Microsystems, Inc. Abstract Current Sun platforms

### Amazon Cloud Performance Compared. David Adams

Amazon Cloud Performance Compared David Adams Amazon EC2 performance comparison How does EC2 compare to traditional supercomputer for scientific applications? "Performance Analysis of High Performance

### Kashif Iqbal - PhD Kashif.iqbal@ichec.ie

HPC/HTC vs. Cloud Benchmarking An empirical evalua.on of the performance and cost implica.ons Kashif Iqbal - PhD Kashif.iqbal@ichec.ie ICHEC, NUI Galway, Ireland With acknowledgment to Michele MicheloDo

### A GPU COMPUTING PLATFORM (SAGA) AND A CFD CODE ON GPU FOR AEROSPACE APPLICATIONS

A GPU COMPUTING PLATFORM (SAGA) AND A CFD CODE ON GPU FOR AEROSPACE APPLICATIONS SUDHAKARAN.G APCF, AERO, VSSC, ISRO 914712564742 g_suhakaran@vssc.gov.in THOMAS.C.BABU APCF, AERO, VSSC, ISRO 914712565833

### COMP/CS 605: Intro to Parallel Computing Lecture 01: Parallel Computing Overview (Part 1)

COMP/CS 605: Intro to Parallel Computing Lecture 01: Parallel Computing Overview (Part 1) Mary Thomas Department of Computer Science Computational Science Research Center (CSRC) San Diego State University

### A Crash course to (The) Bighouse

A Crash course to (The) Bighouse Brock Palen brockp@umich.edu SVTI Users meeting Sep 20th Outline 1 Resources Configuration Hardware 2 Architecture ccnuma Altix 4700 Brick 3 Software Packaged Software

### High Performance. CAEA elearning Series. Jonathan G. Dudley, Ph.D. 06/09/2015. 2015 CAE Associates

High Performance Computing (HPC) CAEA elearning Series Jonathan G. Dudley, Ph.D. 06/09/2015 2015 CAE Associates Agenda Introduction HPC Background Why HPC SMP vs. DMP Licensing HPC Terminology Types of

### JUROPA Linux Cluster An Overview. 19 May 2014 Ulrich Detert

Mitglied der Helmholtz-Gemeinschaft JUROPA Linux Cluster An Overview 19 May 2014 Ulrich Detert JuRoPA JuRoPA Jülich Research on Petaflop Architectures Bull, Sun, ParTec, Intel, Mellanox, Novell, FZJ JUROPA

### SA2 REPORT. First report from Activity SA2 (M8): Provide a suitable Software environment for users

Subject: First report from Activity SA2 (M8): Provide a suitable Software environment for users Author(s): Alejandro Soba Distribution: Public Introduction The main target of Service Activities 2 (SA2,

### Cloud Computing through Virtualization and HPC technologies

Cloud Computing through Virtualization and HPC technologies William Lu, Ph.D. 1 Agenda Cloud Computing & HPC A Case of HPC Implementation Application Performance in VM Summary 2 Cloud Computing & HPC HPC

### 64-Bit versus 32-Bit CPUs in Scientific Computing

64-Bit versus 32-Bit CPUs in Scientific Computing Axel Kohlmeyer Lehrstuhl für Theoretische Chemie Ruhr-Universität Bochum March 2004 1/25 Outline 64-Bit and 32-Bit CPU Examples

### Performance Monitoring of Parallel Scientific Applications

Performance Monitoring of Parallel Scientific Applications Abstract. David Skinner National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory This paper introduces an infrastructure

### Are mobile processors ready for HPC?

http://www.montblanc-project.eu Are mobile processors ready for HPC? Nikola Rajovic, Pall Carpenter, Isaac Gelado, Nikola Puzovic, Alex Ramirez Barcelona Supercomputing Center This project and the research

Introduction History Design Blue Gene/Q Job Scheduler Filesystem Power usage Performance Summary Sequoia is a petascale Blue Gene/Q supercomputer Being constructed by IBM for the National Nuclear Security

### PERFORMANCE ENHANCEMENTS IN TreeAge Pro 2014 R1.0

PERFORMANCE ENHANCEMENTS IN TreeAge Pro 2014 R1.0 15 th January 2014 Al Chrosny Director, Software Engineering TreeAge Software, Inc. achrosny@treeage.com Andrew Munzer Director, Training and Customer

### HPC Wales Skills Academy Course Catalogue 2015

HPC Wales Skills Academy Course Catalogue 2015 Overview The HPC Wales Skills Academy provides a variety of courses and workshops aimed at building skills in High Performance Computing (HPC). Our courses

### The CNMS Computer Cluster

The CNMS Computer Cluster This page describes the CNMS Computational Cluster, how to access it, and how to use it. Introduction (2014) The latest block of the CNMS Cluster (2010) Previous blocks of the

### Parallel Computing. Frank McKenna. UC Berkeley. OpenSees Parallel Workshop Berkeley, CA

Parallel Computing Frank McKenna UC Berkeley OpenSees Parallel Workshop Berkeley, CA Overview Introduction to Parallel Computers Parallel Programming Models Race Conditions and Deadlock Problems Performance

### Performance Evaluation of Amazon EC2 for NASA HPC Applications!

National Aeronautics and Space Administration Performance Evaluation of Amazon EC2 for NASA HPC Applications! Piyush Mehrotra!! J. Djomehri, S. Heistand, R. Hood, H. Jin, A. Lazanoff,! S. Saini, R. Biswas!

### Large-Data Software Defined Visualization on CPUs

Large-Data Software Defined Visualization on CPUs Greg P. Johnson, Bruce Cherniak 2015 Rice Oil & Gas HPC Workshop Trend: Increasing Data Size Measuring / modeling increasingly complex phenomena Rendering

### TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 7 th CALL (Tier-0)

TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 7 th CALL (Tier-0) Contributing sites and the corresponding computer systems for this call are: GCS@Jülich, Germany IBM Blue Gene/Q GENCI@CEA, France Bull Bullx

### Advanced Techniques with Newton. Gerald Ragghianti Advanced Newton workshop Sept. 22, 2011

Advanced Techniques with Newton Gerald Ragghianti Advanced Newton workshop Sept. 22, 2011 Workshop Goals Gain independence Executing your work Finding Information Fixing Problems Optimizing Effectiveness

### Parallel Computing with MATLAB

Parallel Computing with MATLAB Scott Benway Senior Account Manager Jiro Doke, Ph.D. Senior Application Engineer 2013 The MathWorks, Inc. 1 Acceleration Strategies Applied in MATLAB Approach Options Best

### Supercomputing applied to Parallel Network Simulation

Supercomputing applied to Parallel Network Simulation David Cortés-Polo Research, Technological Innovation and Supercomputing Centre of Extremadura, CenitS. Trujillo, Spain david.cortes@cenits.es Summary

### Lattice QCD Performance. on Multi core Linux Servers

Lattice QCD Performance on Multi core Linux Servers Yang Suli * Department of Physics, Peking University, Beijing, 100871 Abstract At the moment, lattice quantum chromodynamics (lattice QCD) is the most

### Clusters: Mainstream Technology for CAE

Clusters: Mainstream Technology for CAE Alanna Dwyer HPC Division, HP Linux and Clusters Sparked a Revolution in High Performance Computing! Supercomputing performance now affordable and accessible Linux

### InfiniBand, PCI Express, and Intel Xeon Processors with Extended Memory 64 Technology (Intel EM64T)

White Paper InfiniBand, PCI Express, and Intel Xeon Processors with Extended Memory 64 Technology (Intel EM64T) Towards a Perfectly Balanced Computing Architecture 1.0 The Problem The performance and efficiency

### A Performance and Cost Analysis of the Amazon Elastic Compute Cloud (EC2) Cluster Compute Instance

A Performance and Cost Analysis of the Amazon Elastic Compute Cloud (EC2) Cluster Compute Instance Michael Fenn (mfenn@psu.edu), Jason Holmes (jholmes@psu.edu), Jeffrey Nucciarone (nucci@psu.edu) Research

### Introduction to parallel computers and parallel programming. Introduction to parallel computersand parallel programming p. 1

Introduction to parallel computers and parallel programming Introduction to parallel computersand parallel programming p. 1 Content A quick overview of morden parallel hardware Parallelism within a chip

### Sun Constellation System: The Open Petascale Computing Architecture

CAS2K7 13 September, 2007 Sun Constellation System: The Open Petascale Computing Architecture John Fragalla Senior HPC Technical Specialist Global Systems Practice Sun Microsystems, Inc. 25 Years of Technical

### Performance Across the Generations: Processor and Interconnect Technologies

WHITE Paper Performance Across the Generations: Processor and Interconnect Technologies HPC Performance Results ANSYS CFD 12 Executive Summary Today s engineering, research, and development applications

### Neptune. A Domain Specific Language for Deploying HPC Software on Cloud Platforms. Chris Bunch Navraj Chohan Chandra Krintz Khawaja Shams

Neptune A Domain Specific Language for Deploying HPC Software on Cloud Platforms Chris Bunch Navraj Chohan Chandra Krintz Khawaja Shams ScienceCloud 2011 @ San Jose, CA June 8, 2011 Cloud Computing Three

### Motivation and Goal. Introduction to HPC content and definitions. Learning Outcomes. Organization

Motivation and Goal Introduction to HPC content and definitions Jan Thorbecke, Section of Applied Geophysics Get familiar with hardware building blocks, how they operate, and how to make use of them in

### LS-DYNA Scalability on Cray Supercomputers. Tin-Ting Zhu, Cray Inc. Jason Wang, Livermore Software Technology Corp.

LS-DYNA Scalability on Cray Supercomputers Tin-Ting Zhu, Cray Inc. Jason Wang, Livermore Software Technology Corp. WP-LS-DYNA-12213 www.cray.com Table of Contents Abstract... 3 Introduction... 3 Scalability

### Building an energy dashboard. Energy measurement and visualization in current HPC systems

Building an energy dashboard Energy measurement and visualization in current HPC systems Thomas Geenen 1/58 thomas.geenen@surfsara.nl SURFsara The Dutch national HPC center 2H 2014 > 1PFlop GPGPU accelerators

### HPC and Big Data. EPCC The University of Edinburgh. Adrian Jackson Technical Architect a.jackson@epcc.ed.ac.uk

HPC and Big Data EPCC The University of Edinburgh Adrian Jackson Technical Architect a.jackson@epcc.ed.ac.uk EPCC Facilities Technology Transfer European Projects HPC Research Visitor Programmes Training

### High Performance Computing. Course Notes 2007-2008. HPC Fundamentals

High Performance Computing Course Notes 2007-2008 2008 HPC Fundamentals Introduction What is High Performance Computing (HPC)? Difficult to define - it s a moving target. Later 1980s, a supercomputer performs

### Parallel Processing of Large-Scale XML-Based Application Documents on Multi-core Architectures with PiXiMaL

1 / 35 Parallel Processing of Large-Scale XML-Based Application Documents on Multi-core Architectures with PiXiMaL Michael R. Head Madhusudhan Govindaraju Department of Computer Science Grid Computing

### -------- Overview --------

------------------------------------------------------------------- Intel(R) Trace Analyzer and Collector 9.1 Update 1 for Windows* OS Release Notes -------------------------------------------------------------------

### Introduction to GPU hardware and to CUDA

Introduction to GPU hardware and to CUDA Philip Blakely Laboratory for Scientific Computing, University of Cambridge Philip Blakely (LSC) GPU introduction 1 / 37 Course outline Introduction to GPU hardware

### Hybrid parallelism for Weather Research and Forecasting Model on Intel platforms (performance evaluation)

Hybrid parallelism for Weather Research and Forecasting Model on Intel platforms (performance evaluation) Roman Dubtsov*, Mark Lubin, Alexander Semenov {roman.s.dubtsov,mark.lubin,alexander.l.semenov}@intel.com

### Cluster Implementation and Management; Scheduling

Cluster Implementation and Management; Scheduling CPS343 Parallel and High Performance Computing Spring 2013 CPS343 (Parallel and HPC) Cluster Implementation and Management; Scheduling Spring 2013 1 /

### Thematic Unit of Excellence on Computational Materials Science Solid State and Structural Chemistry Unit, Indian Institute of Science

Thematic Unit of Excellence on Computational Materials Science Solid State and Structural Chemistry Unit, Indian Institute of Science Call for Expression of Interest (EOI) for the Supply, Installation

### Very Large Enterprise Network Deployment, 25,000+ Users

Very Large Enterprise Network Deployment, 25,000+ Users Websense software can be deployed in different configurations, depending on the size and characteristics of the network, and the organization s filtering

### Performance Characteristics of a Cost-Effective Medium-Sized Beowulf Cluster Supercomputer

Res. Lett. Inf. Math. Sci., 2003, Vol.5, pp 1-10 Available online at http://iims.massey.ac.nz/research/letters/ 1 Performance Characteristics of a Cost-Effective Medium-Sized Beowulf Cluster Supercomputer

### CONSISTENT PERFORMANCE ASSESSMENT OF MULTICORE COMPUTER SYSTEMS

CONSISTENT PERFORMANCE ASSESSMENT OF MULTICORE COMPUTER SYSTEMS GH. ADAM 1,2, S. ADAM 1,2, A. AYRIYAN 2, V. KORENKOV 2, V. MITSYN 2, M. DULEA 1, I. VASILE 1 1 Horia Hulubei National Institute for Physics

### MPI / ClusterTools Update and Plans

HPC Technical Training Seminar July 7, 2008 October 26, 2007 2 nd HLRS Parallel Tools Workshop Sun HPC ClusterTools 7+: A Binary Distribution of Open MPI MPI / ClusterTools Update and Plans Len Wisniewski

### Icepak High-Performance Computing at Rockwell Automation: Benefits and Benchmarks

Icepak High-Performance Computing at Rockwell Automation: Benefits and Benchmarks Garron K. Morris Senior Project Thermal Engineer gkmorris@ra.rockwell.com Standard Drives Division Bruce W. Weiss Principal

### LS-DYNA Performance Benchmark and Profiling on Windows. July 2009

LS-DYNA Performance Benchmark and Profiling on Windows July 2009 Note The following research was performed under the HPC Advisory Council activities AMD, Dell, Mellanox HPC Advisory Council Cluster Center

### Performance Baseline of HP Proliant Oracle Platform

Performance Baseline of HP Proliant Oracle Platform Part I: CPU Performance Technical Presentation February 2014 Contents 1 Introduction to CPU Performance Tests 2 CPU and Server Configuration 3 CPU Benchmark

### The PHI solution. Fujitsu Industry Ready Intel XEON-PHI based solution. SC2013 - Denver

1 The PHI solution Fujitsu Industry Ready Intel XEON-PHI based solution SC2013 - Denver Industrial Application Challenges Most of existing scientific and technical applications Are written for legacy execution

### Mathematical Libraries on JUQUEEN. JSC Training Course

Mitglied der Helmholtz-Gemeinschaft Mathematical Libraries on JUQUEEN JSC Training Course May 10, 2012 Outline General Informations Sequential Libraries, planned Parallel Libraries and Application Systems:

### High Productivity Computing With Windows

High Productivity Computing With Windows Windows HPC Server 2008 Justin Alderson 16-April-2009 Agenda The purpose of computing is... The purpose of computing is insight not numbers. Richard Hamming Why

### Pedraforca: ARM + GPU prototype

www.bsc.es Pedraforca: ARM + GPU prototype Filippo Mantovani Workshop on exascale and PRACE prototypes Barcelona, 20 May 2014 Overview Goals: Test the performance, scalability, and energy efficiency of

### Accelerating CFD using OpenFOAM with GPUs

Accelerating CFD using OpenFOAM with GPUs Authors: Saeed Iqbal and Kevin Tubbs The OpenFOAM CFD Toolbox is a free, open source CFD software package produced by OpenCFD Ltd. Its user base represents a wide

### 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

### SGI UV 300, UV 30EX: Big Brains for No-Limit Computing

SGI UV 300, UV 30EX: Big Brains for No-Limit Computing The Most ful In-memory Supercomputers for Data-Intensive Workloads Key Features Scales up to 64 sockets and 64TB of coherent shared memory Extreme

### Department of Computer Sciences University of Salzburg. HPC In The Cloud? Seminar aus Informatik SS 2011/2012. July 16, 2012

Department of Computer Sciences University of Salzburg HPC In The Cloud? Seminar aus Informatik SS 2011/2012 July 16, 2012 Michael Kleber, mkleber@cosy.sbg.ac.at Contents 1 Introduction...................................

### ALPS Supercomputing System A Scalable Supercomputer with Flexible Services

ALPS Supercomputing System A Scalable Supercomputer with Flexible Services 1 Abstract Supercomputing is moving from the realm of abstract to mainstream with more and more applications and research being

### Very special thanks to Wolfgang Gentzsch and Burak Yenier for making the UberCloud HPC Experiment possible.

Digital manufacturing technology and convenient access to High Performance Computing (HPC) in industry R&D are essential to increase the quality of our products and the competitiveness of our companies.