Running applications on the Cray XC30 4/12/2015
|
|
|
- Kelley McLaughlin
- 9 years ago
- Views:
Transcription
1 Running applications on the Cray XC30 4/12/2015 1
2 Running on compute nodes By default, users do not log in and run applications on the compute nodes directly. Instead they launch jobs on compute nodes in one of three available modes: 1. Extreme Scaling Mode (ESM Mode) The default high performance MPP mode 2. Pre/Post Processing Mode A throughput mode designed to allows efficient Multiple Applications Multiple User (MAMU) access for smaller applications. 3. Cluster Compatibility Mode (CCM) Emulation mode designed primarily to support 3 rd ISV applications which cannot be recompiled. 2
3 Extreme Scaling Mode (ESM) ESM is a high performance mode designed to run larger applications at scale. Important features are: Dedicated compute nodes for each user job No interference from other users sharing the node Minimum quanta of compute is 1 node. Nodes running with low-noise, low-overhead CNL OS Inter-node communication is via native Aries API. The best latency and bandwidth comms are available using ESM. Applications need to be linked with a Cray comms libraries (MPI, Shmem etc) or compiled with Cray language support (UPC, Coarrays) The appropriate parallel runtime environment is automatically set up between nodes ESM is expected to be the default mode for the majority of applications that require at least one node to run. 3
4 Requesting resources from a batch system As resources are dedicated to users in ESM mode, most supercomputers share nodes via batch systems. ECMWF Cray XC30s use PBS Pro to schedule resources Users submit batch job scripts to a scheduler from a login node (e.g. PBS) for execution at some point in the future. Each job requests resources and predicts how long it will run. The scheduler (running on an external server) then chooses which jobs to run and when, allocating appropriate resources at the start. The batch system will then execute a copy of the user s job script on an a one of the MOM nodes. The scheduler monitors the job throughout it lifetime. Reclaiming the resources when job scripts finish or are killed for overrunning. Each user job scripts will contain two types of command 1. Serial commands that are executed by the MOM node, e.g. quick setup and post processing commands e.g. (rm, cd, mkdir etc) 2. Parallel launches to run on the allocated compute nodes. 1. Launched using the aprun command. 4
5 Example ECMWF batch script #!/bin/ksh #PBS -N xthi #PBS -l EC_total_tasks=256 #PBS -l EC_threads_per_task=6 #PBS -j oe #PBS -o./output #PBS -l walltime=00:05:00 Request resources from the batch system export OMP_NUM_THREADS=$EC_threads_per_task aprun -n $EC_total_tasks \ -N $EC_tasks_per_node \ -d $EC_threads_per_task./a.out Launch the executable on the compute nodes in parallel 5
6 Lifecycle of an ECMWF batch script eslogin qsub run.sh Example Batch Job Script run.sh #!/bin/bash #PBS -l EC_total_tasks=256,EC_threads_per_task=6 #PBS l walltime=1:00:00 cd $WORKDIR Serial aprun n 256 d 6 simulation.exe rm r $WORKDIR/tmp Serial Requested Resources Parallel Cray XE Compute Nodes PBS Queue Manager PBS MOM Node 6
7 Submitting jobs to the batch system Job scripts are submitted to the batch system with qsub: qsub script.pbs Once a job is submitted is assigned a PBS Job ID, e.g sdb To view the state of all the currently queued and running jobs run: qstat To limit to just jobs owned by a specific user qstat u <username> To remove a job from the queue, or cancel a running job qdel <job id> 7
8 Requesting resources from PBS Jobs provide a list of requirements as #PBS comments in the headers of the submission script, e.g. #PBS l walltime 12:00:00 These can be overriden or suplemented at submission by adding to the qsub command line, e.g. > qsub l walltime 11:59:59 run.pbs Common options are: Option Description -N <name> A name for job, -q <queue> Submit job to a specific queues. -o <output file> A file to write the job s stdout stream in to. -e <error file> A file to write the job s stderr stream in to. -j oe Join stderr stream in to stdout stream as a single file Jobs must also describe how many compute nodes they will need. -l walltime <HH:MM:SS> Maximum wall time job will occupy 8
9 Glossary of terms To understand how many compute nodes a job needs, we need to understand how parallel jobs are described by Cray. PE/Processing Element A discrete software process with an individual address space. One PE is equivalent to: 1 MPI Rank, 1 Coarray Image, 1 UPC Thread, or 1 SHMEM PE Threads A logically separate stream of execution inside a parent PE that shares the same address space CPU The minimum piece of hardware capable of running a PE. It may share some or all of its hardware resources with other CPUs Equivalent to a single Intel Hyperthread Compute Unit The individual unit of hardware for processing, may be seen described as a core. May provide one or more CPUs. 9
10 Implementing the Parallel Programming Model on hardware Parallel Application Threads Processing Element Programming Model Node Node Software Thread is bound to 1 Hardware CPU Linux Process Hardware implementation 10
11 Launching ESM Parallel applications ALPS : Application Level Placement Scheduler aprun is the ALPS application launcher It must be used to run application on the XC compute nodes in ESM mode, (either interactively or as a batch job) If aprun is not used, the application will be run on the MOM node (and will most likely fail). aprun launches sets of PEs on the compute nodes. aprun man page contains several useful examples The 4 most important parameters to set are: Description Option Total Number of PEs used by the application -n Number of PEs per compute node -N Number of threads per PE (More precise, the stride between 2 PEs on a node) Number of to CPUs to use per Compute Unit -j -d 11
12 Running applications on the Cray XC30: Some basic examples Assuming XC30 nodes with 2x12 core Ivybridge processors Each node has: 48 CPUs/Hyperthreads and 24 Compute Units/cores Launching an MPI application on all CPUs of 64 nodes: Using 1 CPU per Compute Unit means a maximum of 24 PEs per node. 64 nodes x 24 ranks/node = 1536 ranks $ aprun n 1536 N 24 j1./model-mpi.exe Launch the same MPI application on 64 nodes but with half as many ranks per node Still using 1 CPU per Compute Unit, but limiting to 12 Compute Units. $ aprun n 768 N 12 j1./model-mpi.exe Doubles the available memory for each PE on the node To use all availble CPUs on 64 nodes. Using 2 CPUs per Compute unit, so 48 PEs per node) $ aprun n 3072 N 48 j2./model-mpi.exe 12
13 Some examples of hybrid invocation To launch a Hybrid MPI/OpenMP application on 64 nodes 256 total ranks, using 1 CPU per Compute Unit (Max 24 Threads) Use 4 PEs per node and 6 Threads per PE Threads set by exporting OMP_NUM_THREADS $ export OMP_NUM_THREADS=6 $ aprun n 256 N 4 d $OMP_NUM_THREADS j1./model-hybrid.exe Launch the same hybrid application with 2 CPUs per CU Still 256 total ranks, using 2 CPU per Compute Unit (Max 48 Threads) Use 4 PEs per node and 12 Threads per PE $ export OMP_NUM_THREADS=12 $ aprun n 256 N 4 d $OMP_NUM_THREADS j2./model-hybrid.exe Much more detail in later session on advance placement and binding. 13
14 ECMWF PBS Job Directives ECMWF have created a bespoke set of job directives for PBS that can be used to define the job. The ECMWF job directives provide a direct map between aprun options and PBS jobs. Description Aprun Option EC Job Directive Total PEs -n <n> #PBS l EC_total_tasks=<n> PEs per node -N <N> #PBS l EC_tasks_per_node=<N> Threads per PE -d <d> #PBS l EC_threads per_task=<d> CPUs per CU -j <j> #PBS l EC_hyperthreads=<j> 14
15 A Note on the mppwidth, mppnppn, mppdepth and select notation Casual examination of older Cray documentation or internet searches may suggest the alternatives, mppwidth, mppnppn and mppdepth for requesting resources. These methods, while still functional, are Cray specific extensions to PBS Pro which have been deprecated by Altair. More recent versions of PBS Pro support select syntax which is in use at other Cray sites. Support for select has been discontinued at ECMWF in favour of a customised schema. 15
16 Watching a launched job on the Cray XE xtnodestat Shows how XE nodes are allocated and corresponding aprun commands apstat Shows aprun processes status apstat overview apstat a[ apid ] info about all the applications or a specific one apstat n info about the status of the nodes Batch qstat command shows batch jobs 16
17 Example qstat output Job id Name User Time Use S Queue sdb g1ma:t_wconsta rdx 0 Q of sdb test.sh syi 0 Q of sdb getini_1 emos 00:00:09 R os sdb getini_1 emos 00:00:00 R os sdb getini_1 emos 00:00:00 R os sdb getini_1 emos 00:00:00 R os sdb getini_1 emos 00:00:09 R os sdb getini_1 emos 00:00:02 R os sdb job_nf co5 0 Q of sdb job_nf co5 0 Q of sdb job_nf co5 0 Q nf 17
18 Example xtnodestat output (cct) Current Allocation Status at Thu Feb 06 15:16: C0-0 n3 --S ; n2 SSS--S n1 SSS--S c0n0 SSS s abcdef Legend: nonexistent node S service node ; free interactive compute node - free batch compute node A allocated (idle) compute or ccm node? suspect compute node W waiting or non-running job X down compute node Y down or admindown service node Z admindown compute node Available compute nodes: 1 interactive, 45 batch 18
19 Pre/Post Processing Mode This is a new mode for the Cray XC30. Designed to allow multi-user jobs to share compute nodes More efficient for apps running on less than one node Possible interference from other users on the node Uses the same fully featured OS as service nodes Multiple use cases, applications can be: entirely serial embarrassingly parallel e.g. fork/exec, spawn + barrier. shared memory using OpenMP or other threading model. MPI (limited to intra-node MPI only*) Scheduling and launching handled by PBS Similar to any normal PBS cluster deployment. 19
20 Understanding Module Targets The wrappers, cc, CC and ftn are cross compilation environments that by default target the compute nodes. This means compilers will build binaries explicitly targeting the CPU architecture in the compute nodes It will also link distributed memory libraries by default. Binaries built using the default settings will probably not work on serial nodes or pre/post processing nodes. Users many need to switch the CPU target and/or opt to remove network dependencies. For example when compiling serial applications for use on eslogin or pre/post processing nodes. Targets are changed by adding/removing appropriate modules 20
21 Cray XC30 Target modules CPU Architecture Targets craype-ivybridge (default) Tell compiler and system libraries to target Intel Ivybridge processors craype-sandybridge Tell compiler and system libraries to build binaries targeting Intel Sandybridge processors Network Targets craype-network-aries (default) Link network libraries into binary (ESM mode). craype-target-local_host Do not link network libraries into the binary (serial apps non-esm mode). For MAMU nodes use mpiexec from module cray-smplauncher instead of aprun if you want support for MPI 21
22 Cluster Compatibility Mode (CCM) CCM creates small TCP/IP connected clusters of compute nodes. It was designed to support legacy ISV applications which could not be recompiled to use ESM mode. There is a small cost in performance due to the overhead of using TCP/IP rather than native Aries calls. Users also are also responsible for initialising the application on all the participating nodes, including any daemon processes. Still exclusive use of each node by a single users, so only recommended when recompilation unavailable. 22
Hodor and Bran - Job Scheduling and PBS Scripts
Hodor and Bran - Job Scheduling and PBS Scripts UND Computational Research Center Now that you have your program compiled and your input file ready for processing, it s time to run your job on the cluster.
Miami University RedHawk Cluster Working with batch jobs on the Cluster
Miami University RedHawk Cluster Working with batch jobs on the Cluster The RedHawk cluster is a general purpose research computing resource available to support the research community at Miami University.
SLURM: Resource Management and Job Scheduling Software. Advanced Computing Center for Research and Education www.accre.vanderbilt.
SLURM: Resource Management and Job Scheduling Software Advanced Computing Center for Research and Education www.accre.vanderbilt.edu Simple Linux Utility for Resource Management But it s also a job scheduler!
Streamline Computing Linux Cluster User Training. ( Nottingham University)
1 Streamline Computing Linux Cluster User Training ( Nottingham University) 3 User Training Agenda System Overview System Access Description of Cluster Environment Code Development Job Schedulers Running
High Performance Computing Facility Specifications, Policies and Usage. Supercomputer Project. Bibliotheca Alexandrina
High Performance Computing Facility Specifications, Policies and Usage Supercomputer Project Bibliotheca Alexandrina Bibliotheca Alexandrina 1/16 Topics Specifications Overview Site Policies Intel Compilers
Introduction to Sun Grid Engine (SGE)
Introduction to Sun Grid Engine (SGE) What is SGE? Sun Grid Engine (SGE) is an open source community effort to facilitate the adoption of distributed computing solutions. Sponsored by Sun Microsystems
SLURM: Resource Management and Job Scheduling Software. Advanced Computing Center for Research and Education www.accre.vanderbilt.
SLURM: Resource Management and Job Scheduling Software Advanced Computing Center for Research and Education www.accre.vanderbilt.edu Simple Linux Utility for Resource Management But it s also a job scheduler!
NYUAD HPC Center Running Jobs
NYUAD HPC Center Running Jobs 1 Overview... Error! Bookmark not defined. 1.1 General List... Error! Bookmark not defined. 1.2 Compilers... Error! Bookmark not defined. 2 Loading Software... Error! Bookmark
Submitting and Running Jobs on the Cray XT5
Submitting and Running Jobs on the Cray XT5 Richard Gerber NERSC User Services [email protected] Joint Cray XT5 Workshop UC-Berkeley Outline Hopper in blue; Jaguar in Orange; Kraken in Green XT5 Overview
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
Parallel Debugging with DDT
Parallel Debugging with DDT Nate Woody 3/10/2009 www.cac.cornell.edu 1 Debugging Debugging is a methodical process of finding and reducing the number of bugs, or defects, in a computer program or a piece
Work Environment. David Tur HPC Expert. HPC Users Training September, 18th 2015
Work Environment David Tur HPC Expert HPC Users Training September, 18th 2015 1. Atlas Cluster: Accessing and using resources 2. Software Overview 3. Job Scheduler 1. Accessing Resources DIPC technicians
Linux für bwgrid. Sabine Richling, Heinz Kredel. Universitätsrechenzentrum Heidelberg Rechenzentrum Universität Mannheim. 27.
Linux für bwgrid Sabine Richling, Heinz Kredel Universitätsrechenzentrum Heidelberg Rechenzentrum Universität Mannheim 27. June 2011 Richling/Kredel (URZ/RUM) Linux für bwgrid FS 2011 1 / 33 Introduction
Ra - Batch Scripts. Timothy H. Kaiser, Ph.D. [email protected]
Ra - Batch Scripts Timothy H. Kaiser, Ph.D. [email protected] Jobs on Ra are Run via a Batch System Ra is a shared resource Purpose: Give fair access to all users Have control over where jobs are run Set
Job scheduler details
Job scheduler details Advanced Computing Center for Research & Education (ACCRE) Job scheduler details 1 / 25 Outline 1 Batch queue system overview 2 Torque and Moab 3 Submitting jobs (ACCRE) Job scheduler
Grid Engine Basics. Table of Contents. Grid Engine Basics Version 1. (Formerly: Sun Grid Engine)
Grid Engine Basics (Formerly: Sun Grid Engine) Table of Contents Table of Contents Document Text Style Associations Prerequisites Terminology What is the Grid Engine (SGE)? Loading the SGE Module on Turing
NEC HPC-Linux-Cluster
NEC HPC-Linux-Cluster Hardware configuration: 4 Front-end servers: each with SandyBridge-EP processors: 16 cores per node 128 GB memory 134 compute nodes: 112 nodes with SandyBridge-EP processors (16 cores
Using the Windows Cluster
Using the Windows Cluster Christian Terboven [email protected] aachen.de Center for Computing and Communication RWTH Aachen University Windows HPC 2008 (II) September 17, RWTH Aachen Agenda o Windows Cluster
The RWTH Compute Cluster Environment
The RWTH Compute Cluster Environment Tim Cramer 11.03.2013 Source: D. Both, Bull GmbH Rechen- und Kommunikationszentrum (RZ) How to login Frontends cluster.rz.rwth-aachen.de cluster-x.rz.rwth-aachen.de
PBS Tutorial. Fangrui Ma Universit of Nebraska-Lincoln. October 26th, 2007
PBS Tutorial Fangrui Ma Universit of Nebraska-Lincoln October 26th, 2007 Abstract In this tutorial we gave a brief introduction to using PBS Pro. We gave examples on how to write control script, and submit
Using Parallel Computing to Run Multiple Jobs
Beowulf Training Using Parallel Computing to Run Multiple Jobs Jeff Linderoth August 5, 2003 August 5, 2003 Beowulf Training Running Multiple Jobs Slide 1 Outline Introduction to Scheduling Software The
Using the Yale HPC Clusters
Using the Yale HPC Clusters Stephen Weston Robert Bjornson Yale Center for Research Computing Yale University Oct 2015 To get help Send an email to: [email protected] Read documentation at: http://research.computing.yale.edu/hpc-support
Grid 101. Grid 101. Josh Hegie. [email protected] http://hpc.unr.edu
Grid 101 Josh Hegie [email protected] http://hpc.unr.edu Accessing the Grid Outline 1 Accessing the Grid 2 Working on the Grid 3 Submitting Jobs with SGE 4 Compiling 5 MPI 6 Questions? Accessing the Grid Logging
Running on Blue Gene/Q at Argonne Leadership Computing Facility (ALCF)
Running on Blue Gene/Q at Argonne Leadership Computing Facility (ALCF) ALCF Resources: Machines & Storage Mira (Production) IBM Blue Gene/Q 49,152 nodes / 786,432 cores 768 TB of memory Peak flop rate:
Grid Engine Users Guide. 2011.11p1 Edition
Grid Engine Users Guide 2011.11p1 Edition Grid Engine Users Guide : 2011.11p1 Edition Published Nov 01 2012 Copyright 2012 University of California and Scalable Systems This document is subject to the
Using NeSI HPC Resources. NeSI Computational Science Team ([email protected])
NeSI Computational Science Team ([email protected]) Outline 1 About Us About NeSI Our Facilities 2 Using the Cluster Suitable Work What to expect Parallel speedup Data Getting to the Login Node 3 Submitting
OpenMP & MPI CISC 879. Tristan Vanderbruggen & John Cavazos Dept of Computer & Information Sciences University of Delaware
OpenMP & MPI CISC 879 Tristan Vanderbruggen & John Cavazos Dept of Computer & Information Sciences University of Delaware 1 Lecture Overview Introduction OpenMP MPI Model Language extension: directives-based
Batch Scripts for RA & Mio
Batch Scripts for RA & Mio Timothy H. Kaiser, Ph.D. [email protected] 1 Jobs are Run via a Batch System Ra and Mio are shared resources Purpose: Give fair access to all users Have control over where jobs
Introduction to Running Hadoop on the High Performance Clusters at the Center for Computational Research
Introduction to Running Hadoop on the High Performance Clusters at the Center for Computational Research Cynthia Cornelius Center for Computational Research University at Buffalo, SUNY 701 Ellicott St
Tutorial: Using WestGrid. Drew Leske Compute Canada/WestGrid Site Lead University of Victoria
Tutorial: Using WestGrid Drew Leske Compute Canada/WestGrid Site Lead University of Victoria Fall 2013 Seminar Series Date Speaker Topic 23 September Lindsay Sill Introduction to WestGrid 9 October Drew
RA MPI Compilers Debuggers Profiling. March 25, 2009
RA MPI Compilers Debuggers Profiling March 25, 2009 Examples and Slides To download examples on RA 1. mkdir class 2. cd class 3. wget http://geco.mines.edu/workshop/class2/examples/examples.tgz 4. tar
Introduction to HPC Workshop. Center for e-research ([email protected])
Center for e-research ([email protected]) Outline 1 About Us About CER and NeSI The CS Team Our Facilities 2 Key Concepts What is a Cluster Parallel Programming Shared Memory Distributed Memory 3 Using
Debugging with TotalView
Tim Cramer 17.03.2015 IT Center der RWTH Aachen University Why to use a Debugger? If your program goes haywire, you may... ( wand (... buy a magic... read the source code again and again and...... enrich
SLURM Workload Manager
SLURM Workload Manager What is SLURM? SLURM (Simple Linux Utility for Resource Management) is the native scheduler software that runs on ASTI's HPC cluster. Free and open-source job scheduler for the Linux
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
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
An Introduction to High Performance Computing in the Department
An Introduction to High Performance Computing in the Department Ashley Ford & Chris Jewell Department of Statistics University of Warwick October 30, 2012 1 Some Background 2 How is Buster used? 3 Software
Getting Started with HPC
Getting Started with HPC An Introduction to the Minerva High Performance Computing Resource 17 Sep 2013 Outline of Topics Introduction HPC Accounts Logging onto the HPC Clusters Common Linux Commands Storage
OLCF Best Practices. Bill Renaud OLCF User Assistance Group
OLCF Best Practices Bill Renaud OLCF User Assistance Group Overview This presentation covers some helpful information for users of OLCF Staying informed Some aspects of system usage that may differ from
How to Run Parallel Jobs Efficiently
How to Run Parallel Jobs Efficiently Shao-Ching Huang High Performance Computing Group UCLA Institute for Digital Research and Education May 9, 2013 1 The big picture: running parallel jobs on Hoffman2
Beyond Windows: Using the Linux Servers and the Grid
Beyond Windows: Using the Linux Servers and the Grid Topics Linux Overview How to Login & Remote Access Passwords Staying Up-To-Date Network Drives Server List The Grid Useful Commands Linux Overview Linux
Debugging and Profiling Lab. Carlos Rosales, Kent Milfeld and Yaakoub Y. El Kharma [email protected]
Debugging and Profiling Lab Carlos Rosales, Kent Milfeld and Yaakoub Y. El Kharma [email protected] Setup Login to Ranger: - ssh -X [email protected] Make sure you can export graphics
Microsoft HPC. V 1.0 José M. Cámara ([email protected])
Microsoft HPC V 1.0 José M. Cámara ([email protected]) Introduction Microsoft High Performance Computing Package addresses computing power from a rather different approach. It is mainly focused on commodity
Parallel Programming for Multi-Core, Distributed Systems, and GPUs Exercises
Parallel Programming for Multi-Core, Distributed Systems, and GPUs Exercises Pierre-Yves Taunay Research Computing and Cyberinfrastructure 224A Computer Building The Pennsylvania State University University
LoadLeveler Overview. January 30-31, 2012. IBM Storage & Technology Group. IBM HPC Developer Education @ TIFR, Mumbai
IBM HPC Developer Education @ TIFR, Mumbai IBM Storage & Technology Group LoadLeveler Overview January 30-31, 2012 Pidad D'Souza ([email protected]) IBM, System & Technology Group 2009 IBM Corporation
Matlab on a Supercomputer
Matlab on a Supercomputer Shelley L. Knuth Research Computing April 9, 2015 Outline Description of Matlab and supercomputing Interactive Matlab jobs Non-interactive Matlab jobs Parallel Computing Slides
Moab and TORQUE Highlights CUG 2015
Moab and TORQUE Highlights CUG 2015 David Beer TORQUE Architect 28 Apr 2015 Gary D. Brown HPC Product Manager 1 Agenda NUMA-aware Heterogeneous Jobs Ascent Project Power Management and Energy Accounting
Quick Tutorial for Portable Batch System (PBS)
Quick Tutorial for Portable Batch System (PBS) The Portable Batch System (PBS) system is designed to manage the distribution of batch jobs and interactive sessions across the available nodes in the cluster.
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
Martinos Center Compute Clusters
Intro What are the compute clusters How to gain access Housekeeping Usage Log In Submitting Jobs Queues Request CPUs/vmem Email Status I/O Interactive Dependencies Daisy Chain Wrapper Script In Progress
- An Essential Building Block for Stable and Reliable Compute Clusters
Ferdinand Geier ParTec Cluster Competence Center GmbH, V. 1.4, March 2005 Cluster Middleware - An Essential Building Block for Stable and Reliable Compute Clusters Contents: Compute Clusters a Real Alternative
How To Build A Supermicro Computer With A 32 Core Power Core (Powerpc) And A 32-Core (Powerpc) (Powerpowerpter) (I386) (Amd) (Microcore) (Supermicro) (
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
Introduction to the SGE/OGS batch-queuing system
Grid Computing Competence Center Introduction to the SGE/OGS batch-queuing system Riccardo Murri Grid Computing Competence Center, Organisch-Chemisches Institut, University of Zurich Oct. 6, 2011 The basic
Overview. Lecture 1: an introduction to CUDA. Hardware view. Hardware view. hardware view software view CUDA programming
Overview Lecture 1: an introduction to CUDA Mike Giles [email protected] hardware view software view Oxford University Mathematical Institute Oxford e-research Centre Lecture 1 p. 1 Lecture 1 p.
Optimization tools. 1) Improving Overall I/O
Optimization tools After your code is compiled, debugged, and capable of running to completion or planned termination, you can begin looking for ways in which to improve execution speed. In general, the
Batch Systems. provide a mechanism for submitting, launching, and tracking jobs on a shared resource
PBS INTERNALS PBS & TORQUE PBS (Portable Batch System)-software system for managing system resources on workstations, SMP systems, MPPs and vector computers. It was based on Network Queuing System (NQS)
1.0. User Manual For HPC Cluster at GIKI. Volume. Ghulam Ishaq Khan Institute of Engineering Sciences & Technology
Volume 1.0 FACULTY OF CUMPUTER SCIENCE & ENGINEERING Ghulam Ishaq Khan Institute of Engineering Sciences & Technology User Manual For HPC Cluster at GIKI Designed and prepared by Faculty of Computer Science
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
Installing and running COMSOL on a Linux cluster
Installing and running COMSOL on a Linux cluster Introduction This quick guide explains how to install and operate COMSOL Multiphysics 5.0 on a Linux cluster. It is a complement to the COMSOL Installation
Aqua Connect Load Balancer User Manual (Mac)
Aqua Connect Load Balancer User Manual (Mac) Table of Contents About Aqua Connect Load Balancer... 3 System Requirements... 4 Hardware... 4 Software... 4 Installing the Load Balancer... 5 Configuration...
Introduction to Running Computations on the High Performance Clusters at the Center for Computational Research
! Introduction to Running Computations on the High Performance Clusters at the Center for Computational Research! Cynthia Cornelius! Center for Computational Research University at Buffalo, SUNY! cdc at
High Performance Computing
High Performance Computing at Stellenbosch University Gerhard Venter Outline 1 Background 2 Clusters 3 SU History 4 SU Cluster 5 Using the Cluster 6 Examples What is High Performance Computing? Wikipedia
Load Imbalance Analysis
With CrayPat Load Imbalance Analysis Imbalance time is a metric based on execution time and is dependent on the type of activity: User functions Imbalance time = Maximum time Average time Synchronization
SGE Roll: Users Guide. Version @VERSION@ Edition
SGE Roll: Users Guide Version @VERSION@ Edition SGE Roll: Users Guide : Version @VERSION@ Edition Published Aug 2006 Copyright 2006 UC Regents, Scalable Systems Table of Contents Preface...i 1. Requirements...1
Optimizing Shared Resource Contention in HPC Clusters
Optimizing Shared Resource Contention in HPC Clusters Sergey Blagodurov Simon Fraser University Alexandra Fedorova Simon Fraser University Abstract Contention for shared resources in HPC clusters occurs
The Maui High Performance Computing Center Department of Defense Supercomputing Resource Center (MHPCC DSRC) Hadoop Implementation on Riptide - -
The Maui High Performance Computing Center Department of Defense Supercomputing Resource Center (MHPCC DSRC) Hadoop Implementation on Riptide - - Hadoop Implementation on Riptide 2 Table of Contents Executive
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
Performance Analysis and Tuning in Windows HPC Server 2008. Xavier Pillons Program Manager Microsoft Corp. [email protected]
erformance Analysis and Tuning in Windows HC Server 2008 Xavier illons rogram Manager Microsoft Corp. [email protected] Introduction How to monitor performance on Windows? What to look for? How to
The Evolution of Cray Management Services
The Evolution of Cray Management Services Tara Fly, Alan Mutschelknaus, Andrew Barry and John Navitsky OS/IO Cray, Inc. Seattle, WA USA e-mail: {tara, alanm, abarry, johnn}@cray.com Abstract Cray Management
8/15/2014. Best Practices @OLCF (and more) General Information. Staying Informed. Staying Informed. Staying Informed-System Status
Best Practices @OLCF (and more) Bill Renaud OLCF User Support General Information This presentation covers some helpful information for users of OLCF Staying informed Aspects of system usage that may differ
Multi-core Programming System Overview
Multi-core Programming System Overview Based on slides from Intel Software College and Multi-Core Programming increasing performance through software multi-threading by Shameem Akhter and Jason Roberts,
HPC at IU Overview. Abhinav Thota Research Technologies Indiana University
HPC at IU Overview Abhinav Thota Research Technologies Indiana University What is HPC/cyberinfrastructure? Why should you care? Data sizes are growing Need to get to the solution faster Compute power is
Parallel Computing using MATLAB Distributed Compute Server ZORRO HPC
Parallel Computing using MATLAB Distributed Compute Server ZORRO HPC Goals of the session Overview of parallel MATLAB Why parallel MATLAB? Multiprocessing in MATLAB Parallel MATLAB using the Parallel Computing
Working with HPC and HTC Apps. Abhinav Thota Research Technologies Indiana University
Working with HPC and HTC Apps Abhinav Thota Research Technologies Indiana University Outline What are HPC apps? Working with typical HPC apps Compilers - Optimizations and libraries Installation Modules
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
Cluster@WU User s Manual
Cluster@WU User s Manual Stefan Theußl Martin Pacala September 29, 2014 1 Introduction and scope At the WU Wirtschaftsuniversität Wien the Research Institute for Computational Methods (Forschungsinstitut
Using the Yale HPC Clusters
Using the Yale HPC Clusters Stephen Weston Robert Bjornson Yale Center for Research Computing Yale University Dec 2015 To get help Send an email to: [email protected] Read documentation at: http://research.computing.yale.edu/hpc-support
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
Interoperability between Sun Grid Engine and the Windows Compute Cluster
Interoperability between Sun Grid Engine and the Windows Compute Cluster Steven Newhouse Program Manager, Windows HPC Team [email protected] 1 Computer Cluster Roadmap Mainstream HPC Mainstream
How do Users and Processes interact with the Operating System? Services for Processes. OS Structure with Services. Services for the OS Itself
How do Users and Processes interact with the Operating System? Users interact indirectly through a collection of system programs that make up the operating system interface. The interface could be: A GUI,
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
Biowulf2 Training Session
Biowulf2 Training Session 9 July 2015 Slides at: h,p://hpc.nih.gov/docs/b2training.pdf HPC@NIH website: h,p://hpc.nih.gov System hardware overview What s new/different The batch system & subminng jobs
The SUN ONE Grid Engine BATCH SYSTEM
The SUN ONE Grid Engine BATCH SYSTEM Juan Luis Chaves Sanabria Centro Nacional de Cálculo Científico (CeCalCULA) Latin American School in HPC on Linux Cluster October 27 November 07 2003 What is SGE? Is
To connect to the cluster, simply use a SSH or SFTP client to connect to:
RIT Computer Engineering Cluster The RIT Computer Engineering cluster contains 12 computers for parallel programming using MPI. One computer, cluster-head.ce.rit.edu, serves as the master controller or
MAQAO Performance Analysis and Optimization Tool
MAQAO Performance Analysis and Optimization Tool Andres S. CHARIF-RUBIAL [email protected] Performance Evaluation Team, University of Versailles S-Q-Y http://www.maqao.org VI-HPS 18 th Grenoble 18/22
Job Scheduling with Moab Cluster Suite
Job Scheduling with Moab Cluster Suite IBM High Performance Computing February 2010 Y. Joanna Wong, Ph.D. [email protected] 2/22/2010 Workload Manager Torque Source: Adaptive Computing 2 Some terminology..
NorduGrid ARC Tutorial
NorduGrid ARC Tutorial / Arto Teräs and Olli Tourunen 2006-03-23 Slide 1(34) NorduGrid ARC Tutorial Arto Teräs and Olli Tourunen CSC, Espoo, Finland March 23
RWTH GPU Cluster. Sandra Wienke [email protected] November 2012. Rechen- und Kommunikationszentrum (RZ) Fotos: Christian Iwainsky
RWTH GPU Cluster Fotos: Christian Iwainsky Sandra Wienke [email protected] November 2012 Rechen- und Kommunikationszentrum (RZ) The RWTH GPU Cluster GPU Cluster: 57 Nvidia Quadro 6000 (Fermi) innovative
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
How to control Resource allocation on pseries multi MCM system
How to control Resource allocation on pseries multi system Pascal Vezolle Deep Computing EMEA ATS-P.S.S.C/ Montpellier FRANCE Agenda AIX Resource Management Tools WorkLoad Manager (WLM) Affinity Services
