NCAR Globally Accessible Data Environment (GLADE) Updated: 15 Feb 2017

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "NCAR Globally Accessible Data Environment (GLADE) Updated: 15 Feb 2017"

Transcription

1 NCAR Globally Accessible Data Environment (GLADE) Updated: 15 Feb 2017 Overview The Globally Accessible Data Environment (GLADE) provides centralized file storage for HPC computational, data-analysis, visualization and science gateway resources. The environment provides >220GB/s high-performance bandwidth accessible over FDR/EDR IB or 10/40Gb Ethernet networks. The GLADE environment also provides data transfer services to facilitate data movement both within NCAR and to external resources. These data access nodes support common data transfer protocols, Globus transfer service endpoints, Globus data sharing services and high-bandwidth access to the NCAR HPSS system. File Systems NCAR HPC file systems are currently all part of the Globally Accessible Data Environment (GLADE) and can be accesses by all current HPC systems simplifying data sharing between platforms. File systems are configured for different purposes with a total of five spaces available served by three file systems. All file systems are implemented using IBM s Spectrum Scale parallel file system, formerly GPFS. Home (/glade/u/home): Permanent, relatively small storage for data like source code, shell scripts, etc. This file system is not tuned for high performance from parallel jobs, therefore it is not recommended for usage during batch job runs. Project (/glade/p): Large, allocated, permanent, high-performance file system. Project directories are intended for sharing data within a group of researchers and are allocated as part of the annual allocations process. It is recommended that this file system be used for computational runs where the data will be accessed post run in the near future. Work (/glade/p/work): Medium, permanent, high-performance file space. Project directories intended for individual work where data needs to remain resident for an extended period of time. Scratch (/glade/scratch): Large, high-performance file system. Place large data files in this file system for capacity and capability computing. Data is purged as described below, so you must save important files elsewhere, like HPSS, prior to the purge period. Share (/glade/p/datashare): Medium, permanent, high-performance file space. Accessible through Globus Online services and intended to facilitate data transfers to non-ncar users. Available on request. GLADE Overview 15Feb17 1

2 Summary of File Space Policies File Space Type Peak Perf Quota Backups Purge /glade/u/home/user SS Fileset 8 GB/s 25 GB Yes No /glade/p/project SS Fileset 90 GB/s allocated No No /glade/p/work/user SS Fileset 90 GB/s 512 GB No No /glade/scratch/user SS 220 GB/s 10 TB No > 45 days /glade/p/datashare SS 90 GB/s allocated No No File Space Intended Use File System Intended Use File Optimization /glade/u/home Hold source code, executables, configuration files, etc. NOT meant to hold the output from your application runs; the scratch or project file systems should be used for computational output. Optimized for small to medium sized files. /glade/p /glade/p/work /glade/scratch Sharing data within a team or across computational platforms. Store application output files or common scripts, source code and executables. Intended for actively used data that needs to remain resident for an extended period of time. Store application output files intended for individual use. Intended for actively used data that needs to remain resident for an extended period of time. The scratch file system is intended for temporary uses such as storage of checkpoints or application result output. If files need to be retained longer than the purge period, the files should be copied to project space or to HPPS. Optimized for highbandwidth, largeblock-size access to large files. Optimized for highbandwidth, largeblock-size access to large files. Optimized for highbandwidth, largeblock-size access to large files. GLADE Overview 15Feb17 2

3 /data/share Sharing data with non-ncar users. Intended for transient data being delivered offsite. Only available through the Globus Online service. Optimized for network-based transfers. Summary of File Space Capacities File Space Capacity Block Size /glade/u/home 90 TB 1M /glade/p 16.5 PB 4M /glade/p/work 768 TB 4M /glade/scratch 15 PB 8M /glade/p/datashare 500 TB 4M Current File System Usage The following table shows the current file system usage sorted by file system and number of files. The table represents usage across all the available GLADE file systems. Note that in the summary there is a difference in the amount of space actually being used versus the amount of space allocated. This is due to the minimum allocated block size. The data is current as of 14 Feb Total Space Used Space Allocated Total Size PB PB Number of Files 894,960, PB PB Number of Directories 47,643, TB 2.95 TB Number of Links 66,704, GB GB GLADE Overview 15Feb17 3

4 File Distribution Size Bucket # of Files % of Files Total Size % of Capacity 0 Bytes 33,312, % 0 bytes 0.00% < 512 Bytes 92,758, % GB 0.00% < 4 KB 176,062, % GB 0.00% <16 KB 155,940, % 1.26 TB 0.01% <128 KB 195,007, % 9.11 TB 0.06% <512 KB 75,125, % TB 0.13% <1 MB 19,218, % TB 0.09% <4 MB 53,616, % TB 0.78% <100 MB 76,426, % PB 12.47% <1 GB 14,870, % PB 30.67% <10 GB 2,526, % PB 37.74% <100 GB 93, % PB 14.56% <1 TB 2, % TB 3.08% 1 TB % TB 0.40% GLADE Overview 15Feb17 4

5 Systems Served System Description Purpose Connect cheyenne SGI ICE-XA Cluster main computational cluster EDR IB yellowstone IBM idataplex Cluster main computational cluster FDR IB geyser caldera pronghorn data-access RDA science gateway ESG science gateway CDP science gateway IBM xseries Cluster, nvidia GPU IBM idataplex Cluster, nvidia GPU IBM idataplex Cluster, nvidia GPU Linux Cluster Linux Cluster, web services Linux Cluster, web services Linux Cluster, web services data analysis and visualization GPGPU computational cluster GPGPU computational cluster Globus data transfer services, data sharing services Research Data Archive Service Earth Systems Grid Service Community Data Portal Service FDR IB FDR IB FDR IB 40GbE 10GbE 10GbE 10GbE Data Access Nodes Service Globus / GridFTP Globus+ Data Share HPSS Bandwidth 20 GB/s 20 GB/s 30 GB/s GLADE Overview 15Feb17 5

6 Current GLADE Architecture At this time, GLADE resources are split between the FDR IB network of Yellowstone and the EDR network of Cheyenne. The following diagram shows the various components and network connections of GLADE. Figure 1. Current GLADE Architecture GLADE Overview 15Feb17 6

7 Future GLADE Architecture In early 2018, we will be decommissioning the Yellowstone HPC resource. At that time, all GLADE resources will be relocated to the EDR network of Cheyenne. The following diagram shows the various components and network connections of GLADE in early Figure 2. Future GLADE Architecture GLADE Overview 15Feb17 7

GPFS Experiences from the Argonne Leadership Computing Facility (ALCF) William (Bill) E. Allcock ALCF Director of Operations

GPFS Experiences from the Argonne Leadership Computing Facility (ALCF) William (Bill) E. Allcock ALCF Director of Operations GPFS Experiences from the Argonne Leadership Computing Facility (ALCF) William (Bill) E. Allcock ALCF Director of Operations Argonne National Laboratory Argonne National Laboratory is located on 1,500

More information

Data Management Components for a Research Data Archive

Data Management Components for a Research Data Archive Data Management Components for a Research Data Archive Steven Worley and Bob Dattore Scientific Computing Division Computational and Information Systems Laboratory National Center for Atmospheric Research

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

Implementing a Hierarchical Storage Management system in a large-scale Lustre and HPSS environment

Implementing a Hierarchical Storage Management system in a large-scale Lustre and HPSS environment Implementing a Hierarchical Storage Management system in a large-scale Lustre and HPSS environment Brett Bode, Michelle Butler, Sean Stevens, Jim Glasgow National Center for Supercomputing Applications/University

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

RESEARCH DATA DEPOT AT PURDUE UNIVERSITY

RESEARCH DATA DEPOT AT PURDUE UNIVERSITY Preston Smith Director of Research Services RESEARCH DATA DEPOT AT PURDUE UNIVERSITY May 18, 2016 HTCONDOR WEEK 2016 Ran into Miron at a workshop recently.. Talked about data and the challenges of providing

More information

Minnesota Supercomputing Institute Regents of the University of Minnesota. All rights reserved.

Minnesota Supercomputing Institute Regents of the University of Minnesota. All rights reserved. Minnesota Supercomputing Institute Introduction to MSI Systems Andrew Gustafson The Machines at MSI Machine Type: Cluster Source: http://en.wikipedia.org/wiki/cluster_%28computing%29 Machine Type: Cluster

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

XSEDE New User Training. Ritu Arora November 14, 2014

XSEDE New User Training. Ritu Arora   November 14, 2014 XSEDE New User Training Ritu Arora Email: rauta@tacc.utexas.edu November 14, 2014 1 Objectives Provide a brief overview of XSEDE Computational, Visualization and Storage Resources Extended Collaborative

More information

Overview of the Texas Advanced Computing Center. Bill Barth TACC September 12, 2011

Overview of the Texas Advanced Computing Center. Bill Barth TACC September 12, 2011 Overview of the Texas Advanced Computing Center Bill Barth TACC September 12, 2011 TACC Mission & Strategic Approach To enable discoveries that advance science and society through the application of advanced

More information

Data Staging: Moving large amounts of data around, and moving it close to compute resources

Data Staging: Moving large amounts of data around, and moving it close to compute resources Data Staging: Moving large amounts of data around, and moving it close to compute resources PRACE advanced training course on Data Staging and Data Movement Helsinki, September 10 th 2013 Claudio Cacciari

More information

Data Staging: Moving large amounts of data around, and moving it close to compute resources

Data Staging: Moving large amounts of data around, and moving it close to compute resources Data Staging: Moving large amounts of data around, and moving it close to compute resources Digital Preserva-on Advanced Prac--oner Course Glasgow, July 19 th 2013 c.cacciari@cineca.it Definition Starting

More information

Minnesota Supercomputing Institute Regents of the University of Minnesota. All rights reserved.

Minnesota Supercomputing Institute Regents of the University of Minnesota. All rights reserved. Minnesota Supercomputing Institute Introduction to MSI for Physical Scientists Michael Milligan MSI Scientific Computing Consultant Goals Introduction to MSI resources Show you how to access our systems

More information

Data Movement and Storage. 04/07/09 1

Data Movement and Storage. 04/07/09  1 Data Movement and Storage 04/07/09 www.cac.cornell.edu 1 Data Location, Storage, Sharing and Movement Four of the seven main challenges of Data Intensive Computing, according to SC06. (Other three: viewing,

More information

AFM Use Cases Spectrum Scale User Meeting

AFM Use Cases Spectrum Scale User Meeting 1! AFM Use Cases Spectrum Scale User Meeting May, 2017 Vic Cornell, Systems Engineer 2! DDN Who We Are Customers: 1,200+ in 50 Countries Employees: 650+ in 20 Countries Headquarters: Santa Clara, CA Key

More information

CYFRONET SITE REPORT IMPROVING SLURM USABILITY AND MONITORING. M. Pawlik, J. Budzowski, L. Flis, P. Lasoń, M. Magryś

CYFRONET SITE REPORT IMPROVING SLURM USABILITY AND MONITORING. M. Pawlik, J. Budzowski, L. Flis, P. Lasoń, M. Magryś CYFRONET SITE REPORT IMPROVING SLURM USABILITY AND MONITORING M. Pawlik, J. Budzowski, L. Flis, P. Lasoń, M. Magryś Presentation plan 2 Cyfronet introduction System description SLURM modifications Job

More information

GROMACS (GPU) Performance Benchmark and Profiling. February 2016

GROMACS (GPU) Performance Benchmark and Profiling. February 2016 GROMACS (GPU) Performance Benchmark and Profiling February 2016 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Mellanox, NVIDIA Compute

More information

Analyzing the High Performance Parallel I/O on LRZ HPC systems. Sandra Méndez. HPC Group, LRZ. June 23, 2016

Analyzing the High Performance Parallel I/O on LRZ HPC systems. Sandra Méndez. HPC Group, LRZ. June 23, 2016 Analyzing the High Performance Parallel I/O on LRZ HPC systems Sandra Méndez. HPC Group, LRZ. June 23, 2016 Outline SuperMUC supercomputer User Projects Monitoring Tool I/O Software Stack I/O Analysis

More information

Chapter 4:- Introduction to Grid and its Evolution. Prepared By:- NITIN PANDYA Assistant Professor SVBIT.

Chapter 4:- Introduction to Grid and its Evolution. Prepared By:- NITIN PANDYA Assistant Professor SVBIT. Chapter 4:- Introduction to Grid and its Evolution Prepared By:- Assistant Professor SVBIT. Overview Background: What is the Grid? Related technologies Grid applications Communities Grid Tools Case Studies

More information

Advanced Research Compu2ng Informa2on Technology Virginia Tech

Advanced Research Compu2ng Informa2on Technology Virginia Tech Advanced Research Compu2ng Informa2on Technology Virginia Tech www.arc.vt.edu Personnel Associate VP for Research Compu6ng: Terry Herdman (herd88@vt.edu) Director, HPC: Vijay Agarwala (vijaykag@vt.edu)

More information

Extraordinary HPC file system solutions at KIT

Extraordinary HPC file system solutions at KIT Extraordinary HPC file system solutions at KIT Roland Laifer STEINBUCH CENTRE FOR COMPUTING - SCC KIT University of the State Roland of Baden-Württemberg Laifer Lustre and tools for ldiskfs investigation

More information

Introduction to High-Performance Computing (HPC)

Introduction to High-Performance Computing (HPC) Introduction to High-Performance Computing (HPC) Computer components CPU : Central Processing Unit cores : individual processing units within a CPU Storage : Disk drives HDD : Hard Disk Drive SSD : Solid

More information

Lustre HSM at Cambridge. Early user experience using Intel Lemur HSM agent

Lustre HSM at Cambridge. Early user experience using Intel Lemur HSM agent Lustre HSM at Cambridge Early user experience using Intel Lemur HSM agent Matt Rásó-Barnett Wojciech Turek Research Computing Services @ Cambridge University-wide service with broad remit to provide research

More information

Tech Computer Center Documentation

Tech Computer Center Documentation Tech Computer Center Documentation Release 0 TCC Doc February 17, 2014 Contents 1 TCC s User Documentation 1 1.1 TCC SGI Altix ICE Cluster User s Guide................................ 1 i ii CHAPTER 1

More information

Data Movement & Storage Using the Data Capacitor Filesystem

Data Movement & Storage Using the Data Capacitor Filesystem Data Movement & Storage Using the Data Capacitor Filesystem Justin Miller jupmille@indiana.edu http://pti.iu.edu/dc Big Data for Science Workshop July 2010 Challenges for DISC Keynote by Alex Szalay identified

More information

Data Deduplication Makes It Practical to Replicate Your Tape Data for Disaster Recovery

Data Deduplication Makes It Practical to Replicate Your Tape Data for Disaster Recovery Data Deduplication Makes It Practical to Replicate Your Tape Data for Disaster Recovery Scott James VP Global Alliances Luminex Software, Inc. Randy Fleenor Worldwide Data Protection Management IBM Corporation

More information

Altair OptiStruct 13.0 Performance Benchmark and Profiling. May 2015

Altair OptiStruct 13.0 Performance Benchmark and Profiling. May 2015 Altair OptiStruct 13.0 Performance Benchmark and Profiling May 2015 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute

More information

The JANUS Computing Environment

The JANUS Computing Environment Research Computing UNIVERSITY OF COLORADO The JANUS Computing Environment Monte Lunacek monte.lunacek@colorado.edu rc-help@colorado.edu What is JANUS? November, 2011 1,368 Compute nodes 16,416 processors

More information

Outline. ASP 2012 Grid School

Outline. ASP 2012 Grid School Distributed Storage Rob Quick Indiana University Slides courtesy of Derek Weitzel University of Nebraska Lincoln Outline Storage Patterns in Grid Applications Storage

More information

Introduction to the Cluster

Introduction to the Cluster Follow us on Twitter for important news and updates: @ACCREVandy Introduction to the Cluster Advanced Computing Center for Research and Education http://www.accre.vanderbilt.edu The Cluster We will be

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

in Action Fujitsu High Performance Computing Ecosystem Human Centric Innovation Innovation Flexibility Simplicity

in Action Fujitsu High Performance Computing Ecosystem Human Centric Innovation Innovation Flexibility Simplicity Fujitsu High Performance Computing Ecosystem Human Centric Innovation in Action Dr. Pierre Lagier Chief Technology Officer Fujitsu Systems Europe Innovation Flexibility Simplicity INTERNAL USE ONLY 0 Copyright

More information

Preparing GPU-Accelerated Applications for the Summit Supercomputer

Preparing GPU-Accelerated Applications for the Summit Supercomputer Preparing GPU-Accelerated Applications for the Summit Supercomputer Fernanda Foertter HPC User Assistance Group Training Lead foertterfs@ornl.gov This research used resources of the Oak Ridge Leadership

More information

AFM Migration: The Road To Perdition

AFM Migration: The Road To Perdition AFM Migration: The Road To Perdition Spectrum Scale Users Group UK Meeting 9 th -10 th May 2017 Mark Roberts (AWE) Laurence Horrocks-Barlow (OCF) British Crown Owned Copyright [2017]/AWE GPFS Systems Legacy

More information

SNAP Performance Benchmark and Profiling. April 2014

SNAP Performance Benchmark and Profiling. April 2014 SNAP Performance Benchmark and Profiling April 2014 Note The following research was performed under the HPC Advisory Council activities Participating vendors: HP, Mellanox For more information on the supporting

More information

NCL on Yellowstone. Mary Haley October 22, 2014 With consulting support from B.J. Smith. Sponsored in part by the National Science Foundation

NCL on Yellowstone. Mary Haley October 22, 2014 With consulting support from B.J. Smith. Sponsored in part by the National Science Foundation Mary Haley October 22, 2014 With consulting support from B.J. Smith Sponsored in part by the National Science Foundation Main goals Demo two ways to run NCL in yellowstone environment Point you to useful

More information

Lessons learned from Lustre file system operation

Lessons learned from Lustre file system operation Lessons learned from Lustre file system operation Roland Laifer STEINBUCH CENTRE FOR COMPUTING - SCC KIT University of the State of Baden-Württemberg and National Laboratory of the Helmholtz Association

More information

MELLANOX EDR UPDATE & GPUDIRECT MELLANOX SR. SE 정연구

MELLANOX EDR UPDATE & GPUDIRECT MELLANOX SR. SE 정연구 MELLANOX EDR UPDATE & GPUDIRECT MELLANOX SR. SE 정연구 Leading Supplier of End-to-End Interconnect Solutions Analyze Enabling the Use of Data Store ICs Comprehensive End-to-End InfiniBand and Ethernet Portfolio

More information

How To Design a Cluster

How To Design a Cluster How To Design a Cluster PRESENTED BY ROBERT C. JACKSON, MSEE FACULTY AND RESEARCH SUPPORT MANAGER INFORMATION TECHNOLOGY STUDENT ACADEMIC SERVICES GROUP THE UNIVERSITY OF TEXAS-RIO GRANDE VALLEY Abstract

More information

Veeam and Azure Better together. Martin Beran Senior Systems Engineer; Czechia/Slovakia/Hungary

Veeam and Azure Better together. Martin Beran Senior Systems Engineer; Czechia/Slovakia/Hungary Veeam and Azure Better together Martin Beran Senior Systems Engineer; Czechia/Slovakia/Hungary Veeam helps enterprises achieve 24.7.365 Availability Private Cloud / On-Premises Private Cloud / On-Premises

More information

Blue Waters System Overview

Blue Waters System Overview Blue Waters System Overview Blue Waters Computing System Aggregate Memory 1.5 PB Scuba Subsystem Storage Configuration for User Best Access 120+ Gb/sec 100-300 Gbps WAN 10/40/100 Gb Ethernet Switch IB

More information

Lustre2.5 Performance Evaluation: Performance Improvements with Large I/O Patches, Metadata Improvements, and Metadata Scaling with DNE

Lustre2.5 Performance Evaluation: Performance Improvements with Large I/O Patches, Metadata Improvements, and Metadata Scaling with DNE Lustre2.5 Performance Evaluation: Performance Improvements with Large I/O Patches, Metadata Improvements, and Metadata Scaling with DNE Hitoshi Sato *1, Shuichi Ihara *2, Satoshi Matsuoka *1 *1 Tokyo Institute

More information

HPC at UZH: status and plans

HPC at UZH: status and plans HPC at UZH: status and plans Dec. 4, 2013 This presentation s purpose Meet the sysadmin team. Update on what s coming soon in Schroedinger s HW. Review old and new usage policies. Discussion (later on).

More information

Shared Parallel Filesystems in Heterogeneous Linux Multi-Cluster Environments

Shared Parallel Filesystems in Heterogeneous Linux Multi-Cluster Environments LCI HPC Revolution 2005 26 April 2005 Shared Parallel Filesystems in Heterogeneous Linux Multi-Cluster Environments Matthew Woitaszek matthew.woitaszek@colorado.edu Collaborators Organizations National

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

COMPUTE CANADA GLOBUS PORTAL

COMPUTE CANADA GLOBUS PORTAL COMPUTE CANADA GLOBUS PORTAL Fast, user-friendly data transfer and sharing Jason Hlady University of Saskatchewan WestGrid / Compute Canada February 4, 2015 Why Globus? I need to easily, quickly, and reliably

More information

System i and System p. Creating a virtual computing environment

System i and System p. Creating a virtual computing environment System i and System p Creating a virtual computing environment System i and System p Creating a virtual computing environment Note Before using this information and the product it supports, read the information

More information

SDSC s Data Oasis Gen II: ZFS, 40GbE, and Replication

SDSC s Data Oasis Gen II: ZFS, 40GbE, and Replication SDSC s Data Oasis Gen II: ZFS, 40GbE, and Replication Rick Wagner HPC Systems Manager San Diego Supercomputer Center Comet HPC for the long tail of science iphone panorama photograph of 1 of 2 server rows

More information

MEMORY MANAGEMENT/1 CS 409, FALL 2013

MEMORY MANAGEMENT/1 CS 409, FALL 2013 MEMORY MANAGEMENT Requirements: Relocation (to different memory areas) Protection (run time, usually implemented together with relocation) Sharing (and also protection) Logical organization Physical organization

More information

Managing, Monitoring, and Reporting Functions

Managing, Monitoring, and Reporting Functions This chapter discusses various types of managing, monitoring, and reporting functions that can be used with Unified CVP. It covers the following areas: Unified CVP Operations Console Server Management,

More information

VMware vcloud Air User's Guide

VMware vcloud Air User's Guide vcloud Air This document supports the version of each product listed and supports all subsequent versions until the document is replaced by a new edition. To check for more recent editions of this document,

More information

MVAPICH User s Group Meeting August 20, 2015

MVAPICH User s Group Meeting August 20, 2015 MVAPICH User s Group Meeting August 20, 2015 LLNL-PRES-676271 This work was performed under the auspices of the U.S. Department of Energy by under contract DE-AC52-07NA27344. Lawrence Livermore National

More information

Paving the Road to Exascale

Paving the Road to Exascale Paving the Road to Exascale Gilad Shainer August 2015, MVAPICH User Group (MUG) Meeting The Ever Growing Demand for Performance Performance Terascale Petascale Exascale 1 st Roadrunner 2000 2005 2010 2015

More information

Mainframe Virtual Tape: Improve Operational Efficiencies and Mitigate Risk in the Data Center

Mainframe Virtual Tape: Improve Operational Efficiencies and Mitigate Risk in the Data Center Mainframe Virtual Tape: Improve Operational Efficiencies and Mitigate Risk in the Data Center Ralph Armstrong EMC Backup Recovery Systems August 11, 2011 Session # 10135 Agenda Mainframe Tape Use Cases

More information

IBM Spectrum Scale vs EMC Isilon for IBM Spectrum Protect Workloads

IBM Spectrum Scale vs EMC Isilon for IBM Spectrum Protect Workloads 89 Fifth Avenue, 7th Floor New York, NY 10003 www.theedison.com @EdisonGroupInc 212.367.7400 IBM Spectrum Scale vs EMC Isilon for IBM Spectrum Protect Workloads A Competitive Test and Evaluation Report

More information

The Earth System Grid: A Visualisation Solution. Gary Strand

The Earth System Grid: A Visualisation Solution. Gary Strand The Earth System Grid: A Visualisation Solution Gary Strand Introduction Acknowledgments PI s Ian Foster (ANL) Don Middleton (NCAR) Dean Williams (LLNL) ESG Development Team Veronika Nefedova (ANL) Ann

More information

HPC Resources at Lehigh. Steve Anthony March 22, 2012

HPC Resources at Lehigh. Steve Anthony March 22, 2012 HPC Resources at Lehigh Steve Anthony March 22, 2012 HPC at Lehigh: Resources What's Available? Service Level Basic Service Level E-1 Service Level E-2 Leaf and Condor Pool Altair Trits, Cuda0, Inferno,

More information

pbsacct: A Workload Analysis System for PBS-Based HPC Systems

pbsacct: A Workload Analysis System for PBS-Based HPC Systems pbsacct: A Workload Analysis System for PBS-Based HPC Systems Troy Baer Senior HPC System Administrator National Institute for Computational Sciences University of Tennessee Doug Johnson Chief Systems

More information

Triton file systems - an introduction. slide 1 of 28

Triton file systems - an introduction. slide 1 of 28 Triton file systems - an introduction slide 1 of 28 File systems Motivation & basic concepts Storage locations Basic flow of IO Do's and Don'ts Exercises slide 2 of 28 File systems: Motivation Case #1:

More information

Dell Fluid File System Version 6.0 Support Matrix

Dell Fluid File System Version 6.0 Support Matrix Dell Fluid File System Version 6.0 Support Matrix Notes, Cautions, and Warnings NOTE: A NOTE indicates important information that helps you make better use of your product. CAUTION: A CAUTION indicates

More information

ANSYS Fluent 14 Performance Benchmark and Profiling. October 2012

ANSYS Fluent 14 Performance Benchmark and Profiling. October 2012 ANSYS Fluent 14 Performance Benchmark and Profiling October 2012 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information

More information

MOHA: Many-Task Computing Framework on Hadoop

MOHA: Many-Task Computing Framework on Hadoop Apache: Big Data North America 2017 @ Miami MOHA: Many-Task Computing Framework on Hadoop Soonwook Hwang Korea Institute of Science and Technology Information May 18, 2017 Table of Contents Introduction

More information

HPC Innovation Lab Update. Dell EMC HPC Community Meeting 3/28/2017

HPC Innovation Lab Update. Dell EMC HPC Community Meeting 3/28/2017 HPC Innovation Lab Update Dell EMC HPC Community Meeting 3/28/2017 Dell EMC HPC Innovation Lab charter Design, develop and integrate Heading HPC systems Lorem ipsum Flexible reference dolor sit amet, architectures

More information

Overview of HPC at LONI

Overview of HPC at LONI Overview of HPC at LONI Le Yan HPC Consultant User Services @ LONI What Is HPC High performance computing is to use supercomputers to solve problems computationally The most powerful supercomputer today

More information

The Spider Center-Wide File System

The Spider Center-Wide File System The Spider Center-Wide File System Presented by Feiyi Wang (Ph.D.) Technology Integration Group National Center of Computational Sciences Galen Shipman (Group Lead) Dave Dillow, Sarp Oral, James Simmons,

More information

How то Use HPC Resources Efficiently by a Message Oriented Framework.

How то Use HPC Resources Efficiently by a Message Oriented Framework. How то Use HPC Resources Efficiently by a Message Oriented Framework www.hp-see.eu E. Atanassov, T. Gurov, A. Karaivanova Institute of Information and Communication Technologies Bulgarian Academy of Science

More information

High Performance Computing

High Performance Computing High Performance Computing Dror Goldenberg, HPCAC Switzerland Conference March 2015 End-to-End Interconnect Solutions for All Platforms Highest Performance and Scalability for X86, Power, GPU, ARM and

More information

OPEN MPI WITH RDMA SUPPORT AND CUDA. Rolf vandevaart, NVIDIA

OPEN MPI WITH RDMA SUPPORT AND CUDA. Rolf vandevaart, NVIDIA OPEN MPI WITH RDMA SUPPORT AND CUDA Rolf vandevaart, NVIDIA OVERVIEW What is CUDA-aware History of CUDA-aware support in Open MPI GPU Direct RDMA support Tuning parameters Application example Future work

More information

EMC STORAGE FOR MILESTONE XPROTECT CORPORATE

EMC STORAGE FOR MILESTONE XPROTECT CORPORATE Reference Architecture EMC STORAGE FOR MILESTONE XPROTECT CORPORATE Milestone multitier video surveillance storage architectures Design guidelines for Live Database and Archive Database video storage EMC

More information

An Introduction to GPFS

An Introduction to GPFS IBM High Performance Computing July 2006 An Introduction to GPFS gpfsintro072506.doc Page 2 Contents Overview 2 What is GPFS? 3 The file system 3 Application interfaces 4 Performance and scalability 4

More information

Using Cartesius and Lisa. Zheng Meyer-Zhao - Consultant Clustercomputing

Using Cartesius and Lisa. Zheng Meyer-Zhao - Consultant Clustercomputing Zheng Meyer-Zhao - zheng.meyer-zhao@surfsara.nl Consultant Clustercomputing Outline SURFsara About us What we do Cartesius and Lisa Architectures and Specifications File systems Funding Hands-on Logging

More information

Introduction to High Performance Parallel I/O

Introduction to High Performance Parallel I/O Introduction to High Performance Parallel I/O Richard Gerber Deputy Group Lead NERSC User Services August 30, 2013-1- Some slides from Katie Antypas I/O Needs Getting Bigger All the Time I/O needs growing

More information

NCAR Computation and Information Systems Laboratory (CISL) Facilities and Support Overview

NCAR Computation and Information Systems Laboratory (CISL) Facilities and Support Overview NCAR Computation and Information Systems Laboratory (CISL) Facilities and Support Overview NCAR ASP 2008 Summer Colloquium on Numerical Techniques for Global Atmospheric Models June 2, 2008 Mike Page -

More information

Introduction to HPC Resources and Linux

Introduction to HPC Resources and Linux Introduction to HPC Resources and Linux Burak Himmetoglu Enterprise Technology Services & Center for Scientific Computing e-mail: bhimmetoglu@ucsb.edu Paul Weakliem California Nanosystems Institute & Center

More information

System Requirements. Hardware and Virtual Appliance Requirements

System Requirements. Hardware and Virtual Appliance Requirements This chapter provides a link to the Cisco Secure Network Server Data Sheet and lists the virtual appliance requirements. Hardware and Virtual Appliance Requirements, page 1 Virtual Machine Appliance Size

More information

Technological Challenges in the GAIA Archive

Technological Challenges in the GAIA Archive Technological Challenges in the GAIA Archive Juan Gonzalez jgonzale at sciops.esa.int Jesus Salgado jsalgado at sciops.esa.int ESA Science Archives Team IVOA Interop 2013, Heidelberg May 2013 Presentation

More information

Isilon Performance. Name

Isilon Performance. Name 1 Isilon Performance Name 2 Agenda Architecture Overview Next Generation Hardware Performance Caching Performance Streaming Reads Performance Tuning OneFS Architecture Overview Copyright 2014 EMC Corporation.

More information

Index Introduction Setting up an account Searching and accessing Download Advanced features

Index Introduction Setting up an account Searching and accessing Download Advanced features ESGF Earth System Grid Federation Tutorial Index Introduction Setting up an account Searching and accessing Download Advanced features Index Introduction IT Challenges of Climate Change Research ESGF Introduction

More information

Assistance in Lustre administration

Assistance in Lustre administration Assistance in Lustre administration Roland Laifer STEINBUCH CENTRE FOR COMPUTING - SCC KIT University of the State of Baden-Württemberg and National Laboratory of the Helmholtz Association www.kit.edu

More information

Veritas InfoScale Enterprise for Oracle Real Application Clusters (RAC)

Veritas InfoScale Enterprise for Oracle Real Application Clusters (RAC) Veritas InfoScale Enterprise for Oracle Real Application Clusters (RAC) Manageability and availability for Oracle RAC databases Overview Veritas InfoScale Enterprise for Oracle Real Application Clusters

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

Enterprise2014. GPFS with Flash840 on PureFlex and Power8 (AIX & Linux)

Enterprise2014. GPFS with Flash840 on PureFlex and Power8 (AIX & Linux) Chris Churchey Principal ATS Group, LLC churchey@theatsgroup.com (610-574-0207) October 2014 GPFS with Flash840 on PureFlex and Power8 (AIX & Linux) Why Monitor? (Clusters, Servers, Storage, Net, etc.)

More information

Pegasus Workflow Management System. Gideon Juve. USC Informa3on Sciences Ins3tute

Pegasus Workflow Management System. Gideon Juve. USC Informa3on Sciences Ins3tute Pegasus Workflow Management System Gideon Juve USC Informa3on Sciences Ins3tute Scientific Workflows Orchestrate complex, multi-stage scientific computations Often expressed as directed acyclic graphs

More information

Using Quality of Service for Scheduling on Cray XT Systems

Using Quality of Service for Scheduling on Cray XT Systems Using Quality of Service for Scheduling on Cray XT Systems Troy Baer HPC System Administrator National Institute for Computational Sciences, University of Tennessee Outline Introduction Scheduling Cray

More information

Outlook Managing Your Mailbox Quota

Outlook Managing Your Mailbox Quota Outlook 2010 - Managing Your Mailbox Quota Contents > Understand your Mailbox Quota, What makes the mailbox so big, Arranging email messages by size, If the message Size field isn t displayed, Removing

More information

Power Systems AC922 Overview. Chris Mann IBM Distinguished Engineer Chief System Architect, Power HPC Systems December 11, 2017

Power Systems AC922 Overview. Chris Mann IBM Distinguished Engineer Chief System Architect, Power HPC Systems December 11, 2017 Power Systems AC922 Overview Chris Mann IBM Distinguished Engineer Chief System Architect, Power HPC Systems December 11, 2017 IBM POWER HPC Platform Strategy High-performance computer and high-performance

More information

The amount of data increases every day Some numbers ( 2012):

The amount of data increases every day Some numbers ( 2012): 1 The amount of data increases every day Some numbers ( 2012): Data processed by Google every day: 100+ PB Data processed by Facebook every day: 10+ PB To analyze them, systems that scale with respect

More information

2/26/2017. The amount of data increases every day Some numbers ( 2012):

2/26/2017. The amount of data increases every day Some numbers ( 2012): The amount of data increases every day Some numbers ( 2012): Data processed by Google every day: 100+ PB Data processed by Facebook every day: 10+ PB To analyze them, systems that scale with respect to

More information

Data Challenges in Photon Science. Manuela Kuhn GridKa School 2016 Karlsruhe, 29th August 2016

Data Challenges in Photon Science. Manuela Kuhn GridKa School 2016 Karlsruhe, 29th August 2016 Data Challenges in Photon Science Manuela Kuhn GridKa School 2016 Karlsruhe, 29th August 2016 Photon Science > Exploration of tiny samples of nanomaterials > Synchrotrons and free electron lasers generate

More information

10GE network tests with UDP. Janusz Szuba European XFEL

10GE network tests with UDP. Janusz Szuba European XFEL 10GE network tests with UDP Janusz Szuba European XFEL Outline 2 Overview of initial DAQ architecture Slice test hardware specification Initial networking test results DAQ software UDP tests Summary 10GE

More information

IBM V7000 Unified R1.4.2 Asynchronous Replication Performance Reference Guide

IBM V7000 Unified R1.4.2 Asynchronous Replication Performance Reference Guide V7 Unified Asynchronous Replication Performance Reference Guide IBM V7 Unified R1.4.2 Asynchronous Replication Performance Reference Guide Document Version 1. SONAS / V7 Unified Asynchronous Replication

More information

2013 AWS Worldwide Public Sector Summit Washington, D.C.

2013 AWS Worldwide Public Sector Summit Washington, D.C. 2013 AWS Worldwide Public Sector Summit Washington, D.C. EMR for Fun and for Profit Ben Butler Sr. Manager, Big Data butlerb@amazon.com @bensbutler Overview 1. What is big data? 2. What is AWS Elastic

More information

IBM Hortonworks Design Guide 14-Sep-17 v1

IBM Hortonworks Design Guide 14-Sep-17 v1 IBM Hortonworks Design Guide Main Benefits MAXIMIZE THE NETWORK Maximize datacenter connectivity ROI through: Density, Scale, Performance OPEN THE NETWORK Leverage new technologies that increase functionality,

More information

Performance Boost for Seismic Processing with right IT infrastructure. Vsevolod Shabad CEO and founder +7 (985)

Performance Boost for Seismic Processing with right IT infrastructure. Vsevolod Shabad CEO and founder +7 (985) Performance Boost for Seismic Processing with right IT infrastructure Vsevolod Shabad vshabad@netproject.ru CEO and founder +7 (985) 765-76-03 NetProject at a glance 2 System integrator with strong oil&gas

More information

CS370 Operating Systems

CS370 Operating Systems CS370 Operating Systems Colorado State University Yashwant K Malaiya Fall 2016 Lecture 35 Mass Storage Slides based on Text by Silberschatz, Galvin, Gagne Various sources 1 1 Questions For You Local/Global

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 1 Outline GACRC? High Performance

More information

Research Cyberinfrastructure Collaboration Resources

Research Cyberinfrastructure Collaboration Resources Research Cyberinfrastructure Collaboration Resources Computational resources are an important part of research endeavors, and that research varies with respect to its data and processing demands, and also

More information

Automated Verifica/on of I/O Performance. F. Delalondre, M. Baerstchi. EPFL/Blue Brain Project - confiden6al

Automated Verifica/on of I/O Performance. F. Delalondre, M. Baerstchi. EPFL/Blue Brain Project - confiden6al Automated Verifica/on of I/O Performance F. Delalondre, M. Baerstchi Requirements Support Scien6sts Crea6vity Minimize Development 6me Maximize applica6on performance Performance Analysis System Performance

More information

Deploying IBM Rational License Key Server effectively in your organization

Deploying IBM Rational License Key Server effectively in your organization Deploying IBM Rational License Key Server 8.1.1 effectively in your organization Indraneel Paul September 28, 2011 Page 1 of 28 INTRODUCTION...4 IBM RATIONAL LICENSE KEY SERVER 8.1.1...5 TECHNICAL CHANGE

More information

Fast computers, big/fast storage, fast networks Marla Meehl

Fast computers, big/fast storage, fast networks Marla Meehl Fast computers, big/fast storage, fast networks Marla Meehl Manager, Network Engineering and Telecommunications, NCAR/UCAR, Manager of the Front Range GigaPoP Computational & Information Systems Laboratory

More information