Übung zur Vorlesung Architektur paralleler Rechnersysteme

Size: px
Start display at page:

Download "Übung zur Vorlesung Architektur paralleler Rechnersysteme"

Transcription

1 Übung zur Vorlesung Architektur paralleler Rechnersysteme SoSe 17 L Architecture of Parallel Computer Systems SoSe 17 J.Simon 1 Overview Computer Systems Test Cluster (Arminius) Multi node jobs max. 60 nodes, each 36 GByte and 12 cores High Performance Cluster (OCuLUS) Multi node jobs for production 552 nodes, each 64 GByte and 16 cores 20 nodes, each 256 GByte and 16 cores 4 nodes, each 1 TByte and 32 cores High Through-Put Cluster (HTC) Single node jobs single threaded shared memory multi threaded Max. 16 GByte and 8 cores Architecture of Parallel Computer Systems SoSe 17 J.Simon 2 1

2 PC 2 Infrastructure PLING3 BisGRID CX1000 HTC Grid 10GE OCuLUS 790 TB 24*10GE Campus Storage (CST) (NFS4,CIFS,HTTP,...) ARMINIUS IB Big Data FhGFS 500TB 2GB/s Architecture of Parallel Computer Systems SoSe 17 J.Simon 3 Basic Cluster Structure Login Frontend(s) Compile, Pre / Post-processing Small Tests Submit Jobs Administration WLM, CMS Network Workload- Manager Campus Storage (CST) Compute Nodes Cluster File System Architecture of Parallel Computer Systems SoSe 17 J.Simon 4 2

3 Arminius Cluster 636 processor cores, 1,8 TByte main memory 53 compute nodes, each 2 x Intel X5650 with 2.67 GHz (2 x 6 cores) 36 GByte main memory InfiniBand-Interconnect 40 Gbit/s Mellanox HCA and switches full bisectional bandwidth Software Linux CentOS 6.6 openmpi Login nodes fe-1.arminius.pc2.uni-paderborn.de fe-2.arminius.pc2.uni-paderborn.de Architecture of Parallel Computer Systems SoSe 17 J.Simon 5 Available File Systems The PC² provides three types of file systems: A shared file system located on an external filer. This file system is available on all clusters. Cluster local file systems. They provide fast access (typically realized by a parallel file system). Node local disks. Based on this file systems, every user can access the following directories: home, group, PC²-scratch, cluster-scratch, nodescratch. More details see: Architecture of Parallel Computer Systems SoSe 17 J.Simon 6 3

4 Executing Programs on Arminius Set MPI-Environment module add openmpi module add gcc (optional) Compile program mpicc o hello_mpi hello_mpi.c Workload Management System (OpenCCS) ccsinfo -a ccsinfo -s ccsinfo -s mine Job Submission (example) ccsalloc -I --group=hpc-lco-simon t 5m -c 1 shell /bin/hostname Explicit Job Termination ccskill <reqid> Architecture of Parallel Computer Systems SoSe 17 J.Simon 7 Executing MPI-Programs MPI-Job Submission 24 MPI-processes each associated with one core ccsalloc -I --group=hpc-lco-simon c 24 ompi --./hello_mpi 2 MPI-processes each placed on a separate node ccsalloc -I --group=hpc-lco-simon t 4m n 2 ompi --./hello_mpi 8 MPI-processes each with 3 threads ccsalloc -I --group=hpc-lco-simon t 5m / --res=rset=8:ncpus=3:mpiprocs=1:ompthreads=3,place=packed / ompi --./hello_mpi ccsalloc -I --group=hpc-lco-simon t 5m / --res=rset=2:ncpus=12:mpiprocs=4:ompthreads=3,place=free / ompi --./hello_mpi ccsalloc -I --group=hpc-lco-simon t 5m / --res=rset=4:ncpus=6:mpiprocs=2:ompthreads=3,place=free / ompi --./hello_mpi Architecture of Parallel Computer Systems SoSe 17 J.Simon 8 4

5 OpenCCS: Philosophy There are no explicit queues in OpenCCS. Authorization and limitation is attached to groups and / or users. Scheduling is planning based (all jobs have a planned start time) user has to specify the expected runtime of the jobs Entities (e.g., user, group, resource, or, limit) may have a validity User-Interface and resource specification is similar to PBSPro A chunk specifies a set of resources that have to be allocated as a unit on one node. Chunks cannot be split across nodes. ncpus=2:mem=10g ncpus=27:vmem=20g:arch=linux + 4:ncpus=1:mem=50g Placement of chunks can be specified free, scatter, pack, shared, exclusive 4 chunks, each with 6 CPUs with 3 MPI processes, each on a separate host 4:cpus=6:mpiprocs=3, place=scatter Architecture of Parallel Computer Systems SoSe 17 J.Simon 9 OWL Cluster OCuLUS processor cores, 200 TFLOPS, 45 TByte main memory 612 compute nodes, each 2 x Intel E (2.6GHz), therefrom 552 nodes with 64 GByte main memory 20 nodes with 256 GByte (16 nodes reserved for vsmp) 32 nodes with 64 GByte and accelerator Nvidia Tesla GK110 8 nodes with 64 GByte and accelerator Intel Xeon Phi 5110P 4 SMP nodes, each 4 x Intel E (2.7GHz) and 1 TByte main memory Parallel Storage System 500 TByte disk capacity Fraunhofer FhGFS 7 Storage nodes and 2 Meta data nodes InfiniBand-Interconnect 40 Gbit/s Mellanox HCA and switches Half bisectional bandwidth Login nodes for Linux and Windows (Terminal-Server) Architecture of Parallel Computer Systems SoSe 17 J.Simon 10 5

6 HTC-Architecture / Software 36 compute nodes, each with 8 cores and 16GB memory. CPU: Intel(R) Xeon(R) CPU 2.13GHz Operating System: Cent-OS 6.5 Linux-kernel: el6.x86_64 #1 SMP Workload-Manager: OpenCCS CST available ($HOME, $PC2WORK, ) Argos 3.0 Boost Cmake CuSP (Templated Sparse Matrix Library) FFMPEG Freeimage 3.16 Freetype (library to render fonts) Gaussian GHMM (General Hidden Markov Model) Gromacs Gurobi Matlab R2014a NAG Libraries Newton-X (compiled with Intel compiler) Octave (compiled with gcc) OpenBLAS (compiled with gcc) Python and R-project Scalasca 2.0 Scilab SuiteSparse Turbomole Architecture of Parallel Computer Systems SoSe 17 J.Simon 11 Documentation Examples scripts: $PC2SW/examples Manuals: $PC2SW/doc Architecture of Parallel Computer Systems SoSe 17 J.Simon 12 6

Exercise Architecture of Parallel Computer Systems

Exercise Architecture of Parallel Computer Systems Exercise Architecture of Parallel Computer Systems SoSe 18 L.079.05810 www.uni-paderborn.de/pc2 Architecture of Parallel Computer Systems SoSe 18 J.Simon 1 Overview Computer Systems Test Cluster (Arminius)

More information

ARMINIUS Brief Instructions

ARMINIUS Brief Instructions ARMINIUS Brief Instructions Version 19.12.2017 University of Paderborn Paderborn Center for Parallel Computing Warburger Str. 100, D-33098 Paderborn http://pc2.uni-paderborn.de/ 2 ARMINIUS BRIEF INSTRUCTIONS

More information

HTC Brief Instructions

HTC Brief Instructions HTC Brief Instructions Version 18.08.2018 University of Paderborn Paderborn Center for Parallel Computing Warburger Str. 100, D-33098 Paderborn http://pc2.uni-paderborn.de/ 2 HTC BRIEF INSTRUCTIONS Table

More information

HPC DOCUMENTATION. 3. Node Names and IP addresses:- Node details with respect to their individual IP addresses are given below:-

HPC DOCUMENTATION. 3. Node Names and IP addresses:- Node details with respect to their individual IP addresses are given below:- HPC DOCUMENTATION 1. Hardware Resource :- Our HPC consists of Blade chassis with 5 blade servers and one GPU rack server. a.total available cores for computing: - 96 cores. b.cores reserved and dedicated

More information

Our new HPC-Cluster An overview

Our new HPC-Cluster An overview Our new HPC-Cluster An overview Christian Hagen Universität Regensburg Regensburg, 15.05.2009 Outline 1 Layout 2 Hardware 3 Software 4 Getting an account 5 Compiling 6 Queueing system 7 Parallelization

More information

The Why and How of HPC-Cloud Hybrids with OpenStack

The Why and How of HPC-Cloud Hybrids with OpenStack The Why and How of HPC-Cloud Hybrids with OpenStack OpenStack Australia Day Melbourne June, 2017 Lev Lafayette, HPC Support and Training Officer, University of Melbourne lev.lafayette@unimelb.edu.au 1.0

More information

HPC Architectures. Types of resource currently in use

HPC Architectures. Types of resource currently in use HPC Architectures Types of resource currently in use Reusing this material This work is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en_us

More information

HPC Middle East. KFUPM HPC Workshop April Mohamed Mekias HPC Solutions Consultant. Agenda

HPC Middle East. KFUPM HPC Workshop April Mohamed Mekias HPC Solutions Consultant. Agenda KFUPM HPC Workshop April 29-30 2015 Mohamed Mekias HPC Solutions Consultant Agenda 1 Agenda-Day 1 HPC Overview What is a cluster? Shared v.s. Distributed Parallel v.s. Massively Parallel Interconnects

More information

Before We Start. Sign in hpcxx account slips Windows Users: Download PuTTY. Google PuTTY First result Save putty.exe to Desktop

Before We Start. Sign in hpcxx account slips Windows Users: Download PuTTY. Google PuTTY First result Save putty.exe to Desktop Before We Start Sign in hpcxx account slips Windows Users: Download PuTTY Google PuTTY First result Save putty.exe to Desktop Research Computing at Virginia Tech Advanced Research Computing Compute Resources

More information

Genius Quick Start Guide

Genius Quick Start Guide Genius Quick Start Guide Overview of the system Genius consists of a total of 116 nodes with 2 Skylake Xeon Gold 6140 processors. Each with 18 cores, at least 192GB of memory and 800 GB of local SSD disk.

More information

HOKUSAI System. Figure 0-1 System diagram

HOKUSAI System. Figure 0-1 System diagram HOKUSAI System October 11, 2017 Information Systems Division, RIKEN 1.1 System Overview The HOKUSAI system consists of the following key components: - Massively Parallel Computer(GWMPC,BWMPC) - Application

More information

INTRODUCTION TO THE CLUSTER

INTRODUCTION TO THE CLUSTER INTRODUCTION TO THE CLUSTER WHAT IS A CLUSTER? A computer cluster consists of a group of interconnected servers (nodes) that work together to form a single logical system. COMPUTE NODES GATEWAYS SCHEDULER

More information

n N c CIni.o ewsrg.au

n N c CIni.o ewsrg.au @NCInews NCI and Raijin National Computational Infrastructure 2 Our Partners General purpose, highly parallel processors High FLOPs/watt and FLOPs/$ Unit of execution Kernel Separate memory subsystem GPGPU

More information

Student HPC Hackathon 8/2018

Student HPC Hackathon 8/2018 Student HPC Hackathon 8/2018 J. Simon, C. Plessl 22. + 23. August 2018 J. Simon - Architecture of Parallel Computer Systems SoSe 2018 < 1 > Student HPC Hackathon 8/2018 Get the most performance out of

More information

Illinois Proposal Considerations Greg Bauer

Illinois Proposal Considerations Greg Bauer - 2016 Greg Bauer Support model Blue Waters provides traditional Partner Consulting as part of its User Services. Standard service requests for assistance with porting, debugging, allocation issues, and

More information

Introduction to GALILEO

Introduction to GALILEO November 27, 2016 Introduction to GALILEO Parallel & production environment Mirko Cestari m.cestari@cineca.it Alessandro Marani a.marani@cineca.it SuperComputing Applications and Innovation Department

More information

MIGRATING TO THE SHARED COMPUTING CLUSTER (SCC) SCV Staff Boston University Scientific Computing and Visualization

MIGRATING TO THE SHARED COMPUTING CLUSTER (SCC) SCV Staff Boston University Scientific Computing and Visualization MIGRATING TO THE SHARED COMPUTING CLUSTER (SCC) SCV Staff Boston University Scientific Computing and Visualization 2 Glenn Bresnahan Director, SCV MGHPCC Buy-in Program Kadin Tseng HPC Programmer/Consultant

More information

Introduction to HPC Using zcluster at GACRC

Introduction to HPC Using zcluster at GACRC Introduction to HPC Using zcluster at GACRC On-class PBIO/BINF8350 Georgia Advanced Computing Resource Center University of Georgia Zhuofei Hou, HPC Trainer zhuofei@uga.edu Outline What is GACRC? What

More information

High Performance Computing (HPC) Using zcluster at GACRC

High Performance Computing (HPC) Using zcluster at GACRC High Performance Computing (HPC) Using zcluster at GACRC On-class STAT8060 Georgia Advanced Computing Resource Center University of Georgia Zhuofei Hou, HPC Trainer zhuofei@uga.edu Outline What is GACRC?

More information

Introduction to High Performance Computing at UEA. Chris Collins Head of Research and Specialist Computing ITCS

Introduction to High Performance Computing at UEA. Chris Collins Head of Research and Specialist Computing ITCS Introduction to High Performance Computing at UEA. Chris Collins Head of Research and Specialist Computing ITCS Introduction to High Performance Computing High Performance Computing at UEA http://rscs.uea.ac.uk/hpc/

More information

Introduction to Abel/Colossus and the queuing system

Introduction to Abel/Colossus and the queuing system Introduction to Abel/Colossus and the queuing system November 14, 2018 Sabry Razick Research Infrastructure Services Group, USIT Topics First 7 slides are about us and links The Research Computing Services

More information

How to run applications on Aziz supercomputer. Mohammad Rafi System Administrator Fujitsu Technology Solutions

How to run applications on Aziz supercomputer. Mohammad Rafi System Administrator Fujitsu Technology Solutions How to run applications on Aziz supercomputer Mohammad Rafi System Administrator Fujitsu Technology Solutions Agenda Overview Compute Nodes Storage Infrastructure Servers Cluster Stack Environment Modules

More information

FUJITSU PHI Turnkey Solution

FUJITSU PHI Turnkey Solution FUJITSU PHI Turnkey Solution Integrated ready to use XEON-PHI based platform Dr. Pierre Lagier ISC2014 - Leipzig PHI Turnkey Solution challenges System performance challenges Parallel IO best architecture

More information

Introduction to CINECA HPC Environment

Introduction to CINECA HPC Environment Introduction to CINECA HPC Environment 23nd Summer School on Parallel Computing 19-30 May 2014 m.cestari@cineca.it, i.baccarelli@cineca.it Goals You will learn: The basic overview of CINECA HPC systems

More information

Advanced Topics in High Performance Scientific Computing [MA5327] Exercise 1

Advanced Topics in High Performance Scientific Computing [MA5327] Exercise 1 Advanced Topics in High Performance Scientific Computing [MA5327] Exercise 1 Manfred Liebmann Technische Universität München Chair of Optimal Control Center for Mathematical Sciences, M17 manfred.liebmann@tum.de

More information

MSC Nastran Explicit Nonlinear (SOL 700) on Advanced SGI Architectures

MSC Nastran Explicit Nonlinear (SOL 700) on Advanced SGI Architectures MSC Nastran Explicit Nonlinear (SOL 700) on Advanced SGI Architectures Presented By: Dr. Olivier Schreiber, Application Engineering, SGI Walter Schrauwen, Senior Engineer, Finite Element Development, MSC

More information

Introduction to HPC Using zcluster at GACRC

Introduction to HPC Using zcluster at GACRC Introduction to HPC Using zcluster at GACRC Georgia Advanced Computing Resource Center University of Georgia Zhuofei Hou, HPC Trainer zhuofei@uga.edu Outline What is GACRC? What is HPC Concept? What is

More information

Dell EMC Ready Bundle for HPC Digital Manufacturing Dassault Systѐmes Simulia Abaqus Performance

Dell EMC Ready Bundle for HPC Digital Manufacturing Dassault Systѐmes Simulia Abaqus Performance Dell EMC Ready Bundle for HPC Digital Manufacturing Dassault Systѐmes Simulia Abaqus Performance This Dell EMC technical white paper discusses performance benchmarking results and analysis for Simulia

More information

Introduction to GALILEO

Introduction to GALILEO Introduction to GALILEO Parallel & production environment Mirko Cestari m.cestari@cineca.it Alessandro Marani a.marani@cineca.it Alessandro Grottesi a.grottesi@cineca.it SuperComputing Applications and

More information

RWTH GPU-Cluster. Sandra Wienke March Rechen- und Kommunikationszentrum (RZ) Fotos: Christian Iwainsky

RWTH GPU-Cluster. Sandra Wienke March Rechen- und Kommunikationszentrum (RZ) Fotos: Christian Iwainsky RWTH GPU-Cluster Fotos: Christian Iwainsky Sandra Wienke wienke@rz.rwth-aachen.de March 2012 Rechen- und Kommunikationszentrum (RZ) The GPU-Cluster GPU-Cluster: 57 Nvidia Quadro 6000 (29 nodes) innovative

More information

PlaFRIM. Technical presentation of the platform

PlaFRIM. Technical presentation of the platform PlaFRIM Technical presentation of the platform 1-11/12/2018 Contents 2-11/12/2018 01. 02. 03. 04. 05. 06. 07. Overview Nodes description Networks Storage Evolutions How to acces PlaFRIM? Need Help? 01

More information

UL HPC Monitoring in practice: why, what, how, where to look

UL HPC Monitoring in practice: why, what, how, where to look C. Parisot UL HPC Monitoring in practice: why, what, how, where to look 1 / 22 What is HPC? Best Practices Getting Fast & Efficient UL HPC Monitoring in practice: why, what, how, where to look Clément

More information

Introduction to High Performance Computing at ZIH

Introduction to High Performance Computing at ZIH Center for Information Services and High Performance Computing (ZIH) Introduction to High Performance Computing at ZIH Architecture of the PC Farm (Deimos) Zellescher Weg 12 Trefftz-Bau/HRSK 151 Phone

More information

Outline. March 5, 2012 CIRMMT - McGill University 2

Outline. March 5, 2012 CIRMMT - McGill University 2 Outline CLUMEQ, Calcul Quebec and Compute Canada Research Support Objectives and Focal Points CLUMEQ Site at McGill ETS Key Specifications and Status CLUMEQ HPC Support Staff at McGill Getting Started

More information

TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 11th CALL (T ier-0)

TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 11th CALL (T ier-0) TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 11th CALL (T ier-0) Contributing sites and the corresponding computer systems for this call are: BSC, Spain IBM System X idataplex CINECA, Italy The site selection

More information

LBRN - HPC systems : CCT, LSU

LBRN - HPC systems : CCT, LSU LBRN - HPC systems : CCT, LSU HPC systems @ CCT & LSU LSU HPC Philip SuperMike-II SuperMIC LONI HPC Eric Qeenbee2 CCT HPC Delta LSU HPC Philip 3 Compute 32 Compute Two 2.93 GHz Quad Core Nehalem Xeon 64-bit

More information

Introduction to HPC Using zcluster at GACRC

Introduction to HPC Using zcluster at GACRC Introduction to HPC Using zcluster at GACRC On-class STAT8330 Georgia Advanced Computing Resource Center University of Georgia Suchitra Pakala pakala@uga.edu Slides courtesy: Zhoufei Hou 1 Outline What

More information

TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 13 th CALL (T ier-0)

TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 13 th CALL (T ier-0) TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 13 th CALL (T ier-0) Contributing sites and the corresponding computer systems for this call are: BSC, Spain IBM System x idataplex CINECA, Italy Lenovo System

More information

I/O Monitoring at JSC, SIONlib & Resiliency

I/O Monitoring at JSC, SIONlib & Resiliency Mitglied der Helmholtz-Gemeinschaft I/O Monitoring at JSC, SIONlib & Resiliency Update: I/O Infrastructure @ JSC Update: Monitoring with LLview (I/O, Memory, Load) I/O Workloads on Jureca SIONlib: Task-Local

More information

Getting started with the CEES Grid

Getting started with the CEES Grid Getting started with the CEES Grid October, 2013 CEES HPC Manager: Dennis Michael, dennis@stanford.edu, 723-2014, Mitchell Building room 415. Please see our web site at http://cees.stanford.edu. Account

More information

Parallel Computing at DESY Zeuthen. Introduction to Parallel Computing at DESY Zeuthen and the new cluster machines

Parallel Computing at DESY Zeuthen. Introduction to Parallel Computing at DESY Zeuthen and the new cluster machines Parallel Computing at DESY Zeuthen. Introduction to Parallel Computing at DESY Zeuthen and the new cluster machines Götz Waschk Technical Seminar, Zeuthen April 27, 2010 > Introduction > Hardware Infiniband

More information

Frequently Asked Questions

Frequently Asked Questions Frequently Asked Questions Fabien Archambault Aix-Marseille Université 2012 F. Archambault (AMU) Rheticus: F.A.Q. 2012 1 / 13 1 Rheticus configuration 2 Front-end connection 3 Modules 4 OAR submission

More information

The RWTH Compute Cluster Environment

The RWTH Compute Cluster Environment The RWTH Compute Cluster Environment Tim Cramer 29.07.2013 Source: D. Both, Bull GmbH Rechen- und Kommunikationszentrum (RZ) The RWTH Compute Cluster (1/2) The Cluster provides ~300 TFlop/s No. 32 in TOP500

More information

KISTI TACHYON2 SYSTEM Quick User Guide

KISTI TACHYON2 SYSTEM Quick User Guide KISTI TACHYON2 SYSTEM Quick User Guide Ver. 2.4 2017. Feb. SupercomputingCenter 1. TACHYON 2 System Overview Section Specs Model SUN Blade 6275 CPU Intel Xeon X5570 2.93GHz(Nehalem) Nodes 3,200 total Cores

More information

A Comprehensive Study on the Performance of Implicit LS-DYNA

A Comprehensive Study on the Performance of Implicit LS-DYNA 12 th International LS-DYNA Users Conference Computing Technologies(4) A Comprehensive Study on the Performance of Implicit LS-DYNA Yih-Yih Lin Hewlett-Packard Company Abstract This work addresses four

More information

Advances of parallel computing. Kirill Bogachev May 2016

Advances of parallel computing. Kirill Bogachev May 2016 Advances of parallel computing Kirill Bogachev May 2016 Demands in Simulations Field development relies more and more on static and dynamic modeling of the reservoirs that has come a long way from being

More information

Introduction to High Performance Computing at UEA. Chris Collins Head of Research and Specialist Computing ITCS

Introduction to High Performance Computing at UEA. Chris Collins Head of Research and Specialist Computing ITCS Introduction to High Performance Computing at UEA. Chris Collins Head of Research and Specialist Computing ITCS Introduction to High Performance Computing High Performance Computing at UEA http://rscs.uea.ac.uk/hpc/

More information

OBTAINING AN ACCOUNT:

OBTAINING AN ACCOUNT: HPC Usage Policies The IIA High Performance Computing (HPC) System is managed by the Computer Management Committee. The User Policies here were developed by the Committee. The user policies below aim to

More information

Leibniz Supercomputer Centre. Movie on YouTube

Leibniz Supercomputer Centre. Movie on YouTube SuperMUC @ Leibniz Supercomputer Centre Movie on YouTube Peak Performance Peak performance: 3 Peta Flops 3*10 15 Flops Mega 10 6 million Giga 10 9 billion Tera 10 12 trillion Peta 10 15 quadrillion Exa

More information

Graham vs legacy systems

Graham vs legacy systems New User Seminar Graham vs legacy systems This webinar only covers topics pertaining to graham. For the introduction to our legacy systems (Orca etc.), please check the following recorded webinar: SHARCNet

More information

The rcuda technology: an inexpensive way to improve the performance of GPU-based clusters Federico Silla

The rcuda technology: an inexpensive way to improve the performance of GPU-based clusters Federico Silla The rcuda technology: an inexpensive way to improve the performance of -based clusters Federico Silla Technical University of Valencia Spain The scope of this talk Delft, April 2015 2/47 More flexible

More information

HMEM and Lemaitre2: First bricks of the CÉCI s infrastructure

HMEM and Lemaitre2: First bricks of the CÉCI s infrastructure HMEM and Lemaitre2: First bricks of the CÉCI s infrastructure - CÉCI: What we want - Cluster HMEM - Cluster Lemaitre2 - Comparison - What next? - Support and training - Conclusions CÉCI: What we want CÉCI:

More information

Lisa User Day Lisa architecture. John Donners

Lisa User Day Lisa architecture. John Donners Lisa User Day 2011 Lisa architecture John Donners John.Donners@sara.nl What's in this presentation? Overview of all nodes in lisa How to specify jobs for particular purposes: -a quick turnaround -highest

More information

The rcuda middleware and applications

The rcuda middleware and applications The rcuda middleware and applications Will my application work with rcuda? rcuda currently provides binary compatibility with CUDA 5.0, virtualizing the entire Runtime API except for the graphics functions,

More information

Slurm and Abel job scripts. Katerina Michalickova The Research Computing Services Group SUF/USIT October 23, 2012

Slurm and Abel job scripts. Katerina Michalickova The Research Computing Services Group SUF/USIT October 23, 2012 Slurm and Abel job scripts Katerina Michalickova The Research Computing Services Group SUF/USIT October 23, 2012 Abel in numbers Nodes - 600+ Cores - 10000+ (1 node->2 processors->16 cores) Total memory

More information

Using the IBM Opteron 1350 at OSC. October 19-20, 2010

Using the IBM Opteron 1350 at OSC. October 19-20, 2010 Using the IBM Opteron 1350 at OSC October 19-20, 2010 Table of Contents Hardware Overview The Linux Operating System User Environment and Storage 2 Hardware Overview Hardware introduction Login node configuration

More information

SuperMike-II Launch Workshop. System Overview and Allocations

SuperMike-II Launch Workshop. System Overview and Allocations : System Overview and Allocations Dr Jim Lupo CCT Computational Enablement jalupo@cct.lsu.edu SuperMike-II: Serious Heterogeneous Computing Power System Hardware SuperMike provides 442 nodes, 221TB of

More information

Choosing Resources Wisely Plamen Krastev Office: 38 Oxford, Room 117 FAS Research Computing

Choosing Resources Wisely Plamen Krastev Office: 38 Oxford, Room 117 FAS Research Computing Choosing Resources Wisely Plamen Krastev Office: 38 Oxford, Room 117 Email:plamenkrastev@fas.harvard.edu Objectives Inform you of available computational resources Help you choose appropriate computational

More information

Cluster Network Products

Cluster Network Products Cluster Network Products Cluster interconnects include, among others: Gigabit Ethernet Myrinet Quadrics InfiniBand 1 Interconnects in Top500 list 11/2009 2 Interconnects in Top500 list 11/2008 3 Cluster

More information

The GPU-Cluster. Sandra Wienke Rechen- und Kommunikationszentrum (RZ) Fotos: Christian Iwainsky

The GPU-Cluster. Sandra Wienke Rechen- und Kommunikationszentrum (RZ) Fotos: Christian Iwainsky The GPU-Cluster Sandra Wienke wienke@rz.rwth-aachen.de Fotos: Christian Iwainsky Rechen- und Kommunikationszentrum (RZ) The GPU-Cluster GPU-Cluster: 57 Nvidia Quadro 6000 (29 nodes) innovative computer

More information

Introduction to High Performance Computing and an Statistical Genetics Application on the Janus Supercomputer. Purpose

Introduction to High Performance Computing and an Statistical Genetics Application on the Janus Supercomputer. Purpose Introduction to High Performance Computing and an Statistical Genetics Application on the Janus Supercomputer Daniel Yorgov Department of Mathematical & Statistical Sciences, University of Colorado Denver

More information

Accelerating Implicit LS-DYNA with GPU

Accelerating Implicit LS-DYNA with GPU Accelerating Implicit LS-DYNA with GPU Yih-Yih Lin Hewlett-Packard Company Abstract A major hindrance to the widespread use of Implicit LS-DYNA is its high compute cost. This paper will show modern GPU,

More information

System Design of Kepler Based HPC Solutions. Saeed Iqbal, Shawn Gao and Kevin Tubbs HPC Global Solutions Engineering.

System Design of Kepler Based HPC Solutions. Saeed Iqbal, Shawn Gao and Kevin Tubbs HPC Global Solutions Engineering. System Design of Kepler Based HPC Solutions Saeed Iqbal, Shawn Gao and Kevin Tubbs HPC Global Solutions Engineering. Introduction The System Level View K20 GPU is a powerful parallel processor! K20 has

More information

Introduction to PICO Parallel & Production Enviroment

Introduction to PICO Parallel & Production Enviroment Introduction to PICO Parallel & Production Enviroment Mirko Cestari m.cestari@cineca.it Alessandro Marani a.marani@cineca.it Domenico Guida d.guida@cineca.it Nicola Spallanzani n.spallanzani@cineca.it

More information

Resources Current and Future Systems. Timothy H. Kaiser, Ph.D.

Resources Current and Future Systems. Timothy H. Kaiser, Ph.D. Resources Current and Future Systems Timothy H. Kaiser, Ph.D. tkaiser@mines.edu 1 Most likely talk to be out of date History of Top 500 Issues with building bigger machines Current and near future academic

More information

Overview of Parallel Computing. Timothy H. Kaiser, PH.D.

Overview of Parallel Computing. Timothy H. Kaiser, PH.D. Overview of Parallel Computing Timothy H. Kaiser, PH.D. tkaiser@mines.edu Introduction What is parallel computing? Why go parallel? The best example of parallel computing Some Terminology Slides and examples

More information

NAMD GPU Performance Benchmark. March 2011

NAMD GPU Performance Benchmark. March 2011 NAMD GPU Performance Benchmark March 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Intel, Mellanox Compute resource - HPC Advisory

More information

Parallel Applications on Distributed Memory Systems. Le Yan HPC User LSU

Parallel Applications on Distributed Memory Systems. Le Yan HPC User LSU Parallel Applications on Distributed Memory Systems Le Yan HPC User Services @ LSU Outline Distributed memory systems Message Passing Interface (MPI) Parallel applications 6/3/2015 LONI Parallel Programming

More information

Bright Cluster Manager

Bright Cluster Manager Bright Cluster Manager Using Slurm for Data Aware Scheduling in the Cloud Martijn de Vries CTO About Bright Computing Bright Computing 1. Develops and supports Bright Cluster Manager for HPC systems, server

More information

Introduction to Joker Cyber Infrastructure Architecture Team CIA.NMSU.EDU

Introduction to Joker Cyber Infrastructure Architecture Team CIA.NMSU.EDU Introduction to Joker Cyber Infrastructure Architecture Team CIA.NMSU.EDU What is Joker? NMSU s supercomputer. 238 core computer cluster. Intel E-5 Xeon CPUs and Nvidia K-40 GPUs. InfiniBand innerconnect.

More information

PRACE Project Access Technical Guidelines - 19 th Call for Proposals

PRACE Project Access Technical Guidelines - 19 th Call for Proposals PRACE Project Access Technical Guidelines - 19 th Call for Proposals Peer-Review Office Version 5 06/03/2019 The contributing sites and the corresponding computer systems for this call are: System Architecture

More information

Cerebro Quick Start Guide

Cerebro Quick Start Guide Cerebro Quick Start Guide Overview of the system Cerebro consists of a total of 64 Ivy Bridge processors E5-4650 v2 with 10 cores each, 14 TB of memory and 24 TB of local disk. Table 1 shows the hardware

More information

MERCED CLUSTER BASICS Multi-Environment Research Computer for Exploration and Discovery A Centerpiece for Computational Science at UC Merced

MERCED CLUSTER BASICS Multi-Environment Research Computer for Exploration and Discovery A Centerpiece for Computational Science at UC Merced MERCED CLUSTER BASICS Multi-Environment Research Computer for Exploration and Discovery A Centerpiece for Computational Science at UC Merced Sarvani Chadalapaka HPC Administrator University of California

More information

Block Lanczos-Montgomery Method over Large Prime Fields with GPU Accelerated Dense Operations

Block Lanczos-Montgomery Method over Large Prime Fields with GPU Accelerated Dense Operations Block Lanczos-Montgomery Method over Large Prime Fields with GPU Accelerated Dense Operations D. Zheltkov, N. Zamarashkin INM RAS September 24, 2018 Scalability of Lanczos method Notations Matrix order

More information

Sherlock for IBIIS. William Law Stanford Research Computing

Sherlock for IBIIS. William Law Stanford Research Computing Sherlock for IBIIS William Law Stanford Research Computing Overview How we can help System overview Tech specs Signing on Batch submission Software environment Interactive jobs Next steps We are here to

More information

SCALABLE HYBRID PROTOTYPE

SCALABLE HYBRID PROTOTYPE SCALABLE HYBRID PROTOTYPE Scalable Hybrid Prototype Part of the PRACE Technology Evaluation Objectives Enabling key applications on new architectures Familiarizing users and providing a research platform

More information

Oncilla - a Managed GAS Runtime for Accelerating Data Warehousing Queries

Oncilla - a Managed GAS Runtime for Accelerating Data Warehousing Queries Oncilla - a Managed GAS Runtime for Accelerating Data Warehousing Queries Jeffrey Young, Alex Merritt, Se Hoon Shon Advisor: Sudhakar Yalamanchili 4/16/13 Sponsors: Intel, NVIDIA, NSF 2 The Problem Big

More information

Feedback on BeeGFS. A Parallel File System for High Performance Computing

Feedback on BeeGFS. A Parallel File System for High Performance Computing Feedback on BeeGFS A Parallel File System for High Performance Computing Philippe Dos Santos et Georges Raseev FR 2764 Fédération de Recherche LUmière MATière December 13 2016 LOGO CNRS LOGO IO December

More information

Introduction to High Performance Computing (HPC) Resources at GACRC

Introduction to High Performance Computing (HPC) Resources at GACRC Introduction to High Performance Computing (HPC) Resources at GACRC Georgia Advanced Computing Resource Center University of Georgia Zhuofei Hou, HPC Trainer zhuofei@uga.edu Outline What is GACRC? Concept

More information

Docker task in HPC Pack

Docker task in HPC Pack Docker task in HPC Pack We introduced docker task in HPC Pack 2016 Update1. To use this feature, set the environment variable CCP_DOCKER_IMAGE of a task so that it could be run in a docker container on

More information

Computing with the Moore Cluster

Computing with the Moore Cluster Computing with the Moore Cluster Edward Walter An overview of data management and job processing in the Moore compute cluster. Overview Getting access to the cluster Data management Submitting jobs (MPI

More information

The Cray CX1 puts massive power and flexibility right where you need it in your workgroup

The Cray CX1 puts massive power and flexibility right where you need it in your workgroup The Cray CX1 puts massive power and flexibility right where you need it in your workgroup Up to 96 cores of Intel 5600 compute power 3D visualization Up to 32TB of storage GPU acceleration Small footprint

More information

My operating system is old but I don't care : I'm using NIX! B.Bzeznik BUX meeting, Vilnius 22/03/2016

My operating system is old but I don't care : I'm using NIX! B.Bzeznik BUX meeting, Vilnius 22/03/2016 My operating system is old but I don't care : I'm using NIX! B.Bzeznik BUX meeting, Vilnius 22/03/2016 CIMENT is the computing center of the University of Grenoble CIMENT computing platforms 132Tflops

More information

Introduction to HPC Using zcluster at GACRC On-Class GENE 4220

Introduction to HPC Using zcluster at GACRC On-Class GENE 4220 Introduction to HPC Using zcluster at GACRC On-Class GENE 4220 Georgia Advanced Computing Resource Center University of Georgia Suchitra Pakala pakala@uga.edu Slides courtesy: Zhoufei Hou 1 OVERVIEW GACRC

More information

Resources Current and Future Systems. Timothy H. Kaiser, Ph.D.

Resources Current and Future Systems. Timothy H. Kaiser, Ph.D. Resources Current and Future Systems Timothy H. Kaiser, Ph.D. tkaiser@mines.edu 1 Most likely talk to be out of date History of Top 500 Issues with building bigger machines Current and near future academic

More information

Name Department/Research Area Have you used the Linux command line?

Name Department/Research Area Have you used the Linux command line? Please log in with HawkID (IOWA domain) Macs are available at stations as marked To switch between the Windows and the Mac systems, press scroll lock twice 9/27/2018 1 Ben Rogers ITS-Research Services

More information

Practical Introduction to

Practical Introduction to 1 2 Outline of the workshop Practical Introduction to What is ScaleMP? When do we need it? How do we run codes on the ScaleMP node on the ScaleMP Guillimin cluster? How to run programs efficiently on ScaleMP?

More information

Parallel Visualization At TACC. Greg Abram

Parallel Visualization At TACC. Greg Abram Parallel Visualization At TACC Greg Abram Visualization Problems * With thanks to Sean Ahern for the metaphor Huge problems: Data cannot be moved off system where it is computed Large Visualization problems:

More information

Evaluating the Impact of RDMA on Storage I/O over InfiniBand

Evaluating the Impact of RDMA on Storage I/O over InfiniBand Evaluating the Impact of RDMA on Storage I/O over InfiniBand J Liu, DK Panda and M Banikazemi Computer and Information Science IBM T J Watson Research Center The Ohio State University Presentation Outline

More information

Pedraforca: a First ARM + GPU Cluster for HPC

Pedraforca: a First ARM + GPU Cluster for HPC www.bsc.es Pedraforca: a First ARM + GPU Cluster for HPC Nikola Puzovic, Alex Ramirez We ve hit the power wall ALL computers are limited by power consumption Energy-efficient approaches Multi-core Fujitsu

More information

RHRK-Seminar. High Performance Computing with the Cluster Elwetritsch - II. Course instructor : Dr. Josef Schüle, RHRK

RHRK-Seminar. High Performance Computing with the Cluster Elwetritsch - II. Course instructor : Dr. Josef Schüle, RHRK RHRK-Seminar High Performance Computing with the Cluster Elwetritsch - II Course instructor : Dr. Josef Schüle, RHRK Overview Course I Login to cluster SSH RDP / NX Desktop Environments GNOME (default)

More information

DESCRIPTION GHz, 1.536TB shared memory RAM, and 20.48TB RAW internal storage teraflops About ScaleMP

DESCRIPTION GHz, 1.536TB shared memory RAM, and 20.48TB RAW internal storage teraflops About ScaleMP DESCRIPTION The Auburn University College of Engineering Computational Fluid Dynamics Cluster is built using Dell M1000E Blade Chassis Server Platform. The Cluster will consist of (4) M1000E Blade Chassis

More information

Day 9: Introduction to CHTC

Day 9: Introduction to CHTC Day 9: Introduction to CHTC Suggested reading: Condor 7.7 Manual: http://www.cs.wisc.edu/condor/manual/v7.7/ Chapter 1: Overview Chapter 2: Users Manual (at most, 2.1 2.7) 1 Turn In Homework 2 Homework

More information

STAR-CCM+ Performance Benchmark and Profiling. July 2014

STAR-CCM+ Performance Benchmark and Profiling. July 2014 STAR-CCM+ Performance Benchmark and Profiling July 2014 Note The following research was performed under the HPC Advisory Council activities Participating vendors: CD-adapco, Intel, Dell, Mellanox Compute

More information

DATARMOR: Comment s'y préparer? Tina Odaka

DATARMOR: Comment s'y préparer? Tina Odaka DATARMOR: Comment s'y préparer? Tina Odaka 30.09.2016 PLAN DATARMOR: Detailed explanation on hard ware What can you do today to be ready for DATARMOR DATARMOR : convention de nommage ClusterHPC REF SCRATCH

More information

Introduc)on to Hyades

Introduc)on to Hyades Introduc)on to Hyades Shawfeng Dong Department of Astronomy & Astrophysics, UCSSC Hyades 1 Hardware Architecture 2 Accessing Hyades 3 Compu)ng Environment 4 Compiling Codes 5 Running Jobs 6 Visualiza)on

More information

Intel Manycore Testing Lab (MTL) - Linux Getting Started Guide

Intel Manycore Testing Lab (MTL) - Linux Getting Started Guide Intel Manycore Testing Lab (MTL) - Linux Getting Started Guide Introduction What are the intended uses of the MTL? The MTL is prioritized for supporting the Intel Academic Community for the testing, validation

More information

Vienna Scientific Cluster: Problems and Solutions

Vienna Scientific Cluster: Problems and Solutions Vienna Scientific Cluster: Problems and Solutions Dieter Kvasnicka Neusiedl/See February 28 th, 2012 Part I Past VSC History Infrastructure Electric Power May 2011: 1 transformer 5kV Now: 4-5 transformer

More information

Datura The new HPC-Plant at Albert Einstein Institute

Datura The new HPC-Plant at Albert Einstein Institute Datura The new HPC-Plant at Albert Einstein Institute Nico Budewitz Max Planck Institue for Gravitational Physics, Germany Cluster Day, 2011 Outline 1 History HPC-Plants at AEI -2009 Peyote, Lagavulin,

More information

ACEnet for CS6702 Ross Dickson, Computational Research Consultant 29 Sep 2009

ACEnet for CS6702 Ross Dickson, Computational Research Consultant 29 Sep 2009 ACEnet for CS6702 Ross Dickson, Computational Research Consultant 29 Sep 2009 What is ACEnet? Shared resource......for research computing... physics, chemistry, oceanography, biology, math, engineering,

More information