The DEEP-ER take on I/O

Size: px
Start display at page:

Download "The DEEP-ER take on I/O"

Transcription

1 Wolfgang Frings Jülich Supercomputing Centre Workshop Exascale I/O: Challenges, Innovations and Solutions SC16, Salt Lake City 18 November 2016 The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/ ) under Grant Agreement n and n

2 DEEP and DEEP-ER EU-Exascale projects 20 partners Total budget: 28,3 M EU-funding: 14,5 M Nov 2011 Mar

3 I/O Hardware Innovation On- NVM Network attached memory 3

4 Driven by Applications Pilot applications: Space weather simulation (KULeuven) High temperature superconductivity (CINECA) Human exposure to electromagnetic fields (Inria) Geoscience (BADW-LRZ) Radio astronomy (ASTRON) Oil exploration (BSC) Lattice QCD (UREG) Goals: Analyse I/O and resiliency requirements of HPC codes Evaluate DEEP-ER I/O and resiliency concept and its programmability 4

5 Scalable I/O Improve I/O scalability on all usage-levels Used also for checkpointing 5

6 BeeGFS and Caching Two instances: Global FS on HDD server Cache FS on NVM at node API for cache domain handling Synchronous version Asynchronous version 6

7 Architecture of the asynchronous cache API User application DEEP-ER cache daemon Application workflow I/0 subsystem DEEP-ER cache API 1. Wait() Messaging facility 2.a Check list Lookup list of requests in processing DEEP-ER cache library Messaging facility 1. Asynchronous API call 2.b Check list FIFO with new requests Lookup list 2. Add request to FIFO 3.a Worker take request from FIFO 3.b Add request to list Worker threads 5. remove request from list Workflow synchronous API call Workflow asynchronous API call Workflow wait call Synchronous API call (executed by the thread of the caller process) 4. Process work Cache file system Global file system 7

8 New MPI-IO Hints e10_cache e10_cache_path e10_cache_flush_flag e10_cache_discard_flag e10_cache_threads DEEP-ER Cache Integration in ROMIO Processes Buffers Tested in DEEP cluster Global Sync Group (MPI_COMM_WORLD) ROMIO Lustre Driver MPI MPI-IO ADIO (Abstract Device IO) GPFS Driver UFS Driver DEEP-ER Cache Parallel File System BeeGFS Driver Collective Buffers Collective I/O DEEP-ER Cache Independent I/O Parallel File System 8

9 Workflows Using DEEP-ER Cache cache enabled cache disabled S(k): amount of written to the file at phase k (here constant) Tc(k): time to write S(k) to the cache Ts(k): time to sync S(k) with the parallel file system C(k): compute time at phase k 9

10 MPIWRAP Support Library MPI-IO hints are defined in a config file and injected by libmpiwrap into the middleware Provides deeper and more flexible control of MPI-IO functionalities to the users Provides transparent integration of E10 functionalities into applications Works with any high level library (e.g. phdf5) MPI_{Init,Finalize} MPI_File_{open,close} ROMIO Lustre Driver MPI MPI-IO ADIO (Abstract Device IO) GPFS Driver MPIWRAP UFS Driver Parallel File System BeeGFS Driver 10

11 : Shared Files for Task- Data Parallel Application HDF5 NETCDF MPI-I/O POSIX I/O Parallel file system t 1 t 2 t n-1 t n #files: O(10000)./checkpoint/file.0001./checkpoint/file.nnnn Parallel file system

12 : Shared Files for Task- Data Parallel Application HDF5 NETCDF MPI-I/O POSIX I/O Serial program Application t 1 t 2 t 3 Tasks t n-2 t n-1 t n Parallel file system #files: O(10) Physical multi-file Logical task- files Parallel file system

13 : Strategy for Local Storage Tasks t 1 t n/2 t n/2+1 t n file container multi-file approach metablock 1 metablock 2 mapping metablock 1 metablock 2 Shared file 1 Shared file n I/Onode I/Onode I/Onode I/Onode Staging I/Onode I/Onode I/Onode I/Onode node group with storage Local Storage node group with storage Transparent access from application to container Global Storage

14 : Local checkpointing MPI/OpenMP tt t tt t tt t tt t tt t ttt Global File System Mapping: task writes to storage, using one physical file of file container Transparent access to from application Transparent access to when files migrated to global storage Re-distribution of task- in layer possible (Buddy-CP)

15 : Buddy-CP Logical mapping MPI/OpenMP tt t ttt ttt t t t t t t t tt Global File System buddy buddy buddy buddy buddy buddy Mapping: 1:1 task writes to storage and storage of buddy node 1:m task writes to storage and storage of m buddy nodes Data exchange to buddy node is done via MPI/OpenMP layer Collective checkpoint calls required Open: sid=sion_paropen_mpi(, bw,buddy=m,mpi_comm_world, ) Write: sion_coll_write_mpi(,size,elements,sid) Close: sion_parclose(sid)

16 Restore checkpoint after failure MPI/OpenMP tt t t t t t t t t t t t t t t tt Global File System buddy buddy buddy buddy buddy buddy Automatic analysis of availability on storage On missing : falls back to buddy if first open fails Open: sid=sion_paropen_mpi(, br,buddy=m,mpi_comm_world, ) Read: sion_coll_read_mpi(,size,elements,sid) Close: sion_parclose(sid)

17 and SCR interoperability SCR_Start_checkpt() SCR_Route_file(fn, fn_scr) fn = check1 fn_scr= /abspath/check1 sid=sion_paropen_mpi(fn_scr, wb,buddy...) info=sion_get_io_info(sid) sion_parclose_mpi(sid) SCR_Complete_checkpt() (node0) /abspath/check1 (node1) /abspath/check (node0) /abspath/check1_buddy_ (node1) /abspath/check1_buddy_ List of filename opened on this task - Bytes written SCR_update_filename(nfiles, info.names,info.sizes, info.roles)

18 Preliminary results MAXW-DGTD: Human exposure to electromagnetic fields (Inria) reduces number of files less filesystem overhead Data write time [s] P1 P1 with P3 P3 with # of tasks # of tasks P1 P1 with P3 P3 with output times on DEEP

19 Preliminary results NVMe Oil exploration (BSC) sdv-work NVMe Backward Forward 28, Impact of NVMe Propagation time [s] Backward Forward sdv-work NVMe Human exposure to electromagnetic fields (Inria) sdv-work NVMe P P P P I/O performance of MAXW-DGTD Writing time [s] P1 P2 P3 P4 sdv-work NVMe 19

20 Summary DEEP-ER explores future directions of I/O On POSIX optimization level On filesystem level BeeGFS On MPI-IO level E10 Aims to be able to test and combine the approaches Exploration and validations of new hardware NVMe NAM More information on 20

The DEEP (and DEEP-ER) projects

The DEEP (and DEEP-ER) projects The DEEP (and DEEP-ER) projects Estela Suarez - Jülich Supercomputing Centre BDEC for Europe Workshop Barcelona, 28.01.2015 The research leading to these results has received funding from the European

More information

I/O and Scheduling aspects in DEEP-EST

I/O and Scheduling aspects in DEEP-EST I/O and Scheduling aspects in DEEP-EST Norbert Eicker Jülich Supercomputing Centre & University of Wuppertal The research leading to these results has received funding from the European Community's Seventh

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

Parallel I/O on JUQUEEN

Parallel I/O on JUQUEEN Parallel I/O on JUQUEEN 4. Februar 2014, JUQUEEN Porting and Tuning Workshop Mitglied der Helmholtz-Gemeinschaft Wolfgang Frings w.frings@fz-juelich.de Jülich Supercomputing Centre Overview Parallel I/O

More information

I/O at JSC. I/O Infrastructure Workloads, Use Case I/O System Usage and Performance SIONlib: Task-Local I/O. Wolfgang Frings

I/O at JSC. I/O Infrastructure Workloads, Use Case I/O System Usage and Performance SIONlib: Task-Local I/O. Wolfgang Frings Mitglied der Helmholtz-Gemeinschaft I/O at JSC I/O Infrastructure Workloads, Use Case I/O System Usage and Performance SIONlib: Task-Local I/O Wolfgang Frings W.Frings@fz-juelich.de Jülich Supercomputing

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

Approaches to I/O Scalability Challenges in the ECMWF Forecasting System

Approaches to I/O Scalability Challenges in the ECMWF Forecasting System Approaches to I/O Scalability Challenges in the ECMWF Forecasting System PASC 16, June 9 2016 Florian Rathgeber, Simon Smart, Tiago Quintino, Baudouin Raoult, Stephan Siemen, Peter Bauer Development Section,

More information

Scalasca support for Intel Xeon Phi. Brian Wylie & Wolfgang Frings Jülich Supercomputing Centre Forschungszentrum Jülich, Germany

Scalasca support for Intel Xeon Phi. Brian Wylie & Wolfgang Frings Jülich Supercomputing Centre Forschungszentrum Jülich, Germany Scalasca support for Intel Xeon Phi Brian Wylie & Wolfgang Frings Jülich Supercomputing Centre Forschungszentrum Jülich, Germany Overview Scalasca performance analysis toolset support for MPI & OpenMP

More information

Dynamical Exascale Entry Platform

Dynamical Exascale Entry Platform DEEP Dynamical Exascale Entry Platform 2 nd IS-ENES Workshop on High performance computing for climate models 30.01.2013, Toulouse, France Estela Suarez The research leading to these results has received

More information

ECMWF s Next Generation IO for the IFS Model

ECMWF s Next Generation IO for the IFS Model ECMWF s Next Generation IO for the Model Part of ECMWF s Scalability Programme Tiago Quintino, B. Raoult, P. Bauer ECMWF tiago.quintino@ecmwf.int ECMWF January 14, 2016 ECMWF s HPC Targets What do we do?

More information

Index LEVERAGING THE POWER OF HETEROGENEITY FOR NEXT GENERATION SUPERCOMPUTERS

Index LEVERAGING THE POWER OF HETEROGENEITY FOR NEXT GENERATION SUPERCOMPUTERS LEVERAGING THE POWER OF HETEROGENEITY FOR NEXT GENERATION SUPERCOMPUTERS 3 The Power of Heterogeneity 4 A DEEP-ER Look 5 DEEP-ER Hardware Architecture 9 DEEP-ER Co-Design 11 DEEP-ER Software Stack 15 DEEP-ER

More information

ECMWF's Next Generation IO for the IFS Model and Product Generation

ECMWF's Next Generation IO for the IFS Model and Product Generation ECMWF's Next Generation IO for the IFS Model and Product Generation Future workflow adaptations Tiago Quintino, B. Raoult, S. Smart, A. Bonanni, F. Rathgeber, P. Bauer ECMWF tiago.quintino@ecmwf.int ECMWF

More information

HPC projects. Grischa Bolls

HPC projects. Grischa Bolls HPC projects Grischa Bolls Outline Why projects? 7th Framework Programme Infrastructure stack IDataCool, CoolMuc Mont-Blanc Poject Deep Project Exa2Green Project 2 Why projects? Pave the way for exascale

More information

Improved Solutions for I/O Provisioning and Application Acceleration

Improved Solutions for I/O Provisioning and Application Acceleration 1 Improved Solutions for I/O Provisioning and Application Acceleration August 11, 2015 Jeff Sisilli Sr. Director Product Marketing jsisilli@ddn.com 2 Why Burst Buffer? The Supercomputing Tug-of-War A supercomputer

More information

IME (Infinite Memory Engine) Extreme Application Acceleration & Highly Efficient I/O Provisioning

IME (Infinite Memory Engine) Extreme Application Acceleration & Highly Efficient I/O Provisioning IME (Infinite Memory Engine) Extreme Application Acceleration & Highly Efficient I/O Provisioning September 22 nd 2015 Tommaso Cecchi 2 What is IME? This breakthrough, software defined storage application

More information

pnfs, POSIX, and MPI-IO: A Tale of Three Semantics

pnfs, POSIX, and MPI-IO: A Tale of Three Semantics Dean Hildebrand Research Staff Member PDSW 2009 pnfs, POSIX, and MPI-IO: A Tale of Three Semantics Dean Hildebrand, Roger Haskin Arifa Nisar IBM Almaden Northwestern University Agenda Motivation pnfs HPC

More information

A Breakthrough in Non-Volatile Memory Technology FUJITSU LIMITED

A Breakthrough in Non-Volatile Memory Technology FUJITSU LIMITED A Breakthrough in Non-Volatile Memory Technology & 0 2018 FUJITSU LIMITED IT needs to accelerate time-to-market Situation: End users and applications need instant access to data to progress faster and

More information

Employing HPC DEEP-EST for HEP Data Analysis. Viktor Khristenko (CERN, DEEP-EST), Maria Girone (CERN)

Employing HPC DEEP-EST for HEP Data Analysis. Viktor Khristenko (CERN, DEEP-EST), Maria Girone (CERN) Employing HPC DEEP-EST for HEP Data Analysis Viktor Khristenko (CERN, DEEP-EST), Maria Girone (CERN) 1 Outline The DEEP-EST Project Goals and Motivation HEP Data Analysis on HPC with Apache Spark on HPC

More information

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

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

More information

Application Performance on IME

Application Performance on IME Application Performance on IME Toine Beckers, DDN Marco Grossi, ICHEC Burst Buffer Designs Introduce fast buffer layer Layer between memory and persistent storage Pre-stage application data Buffer writes

More information

I/O Profiling Towards the Exascale

I/O Profiling Towards the Exascale I/O Profiling Towards the Exascale holger.brunst@tu-dresden.de ZIH, Technische Universität Dresden NEXTGenIO & SAGE: Working towards Exascale I/O Barcelona, NEXTGenIO facts Project Research & Innovation

More information

POP CoE: Understanding applications and how to prepare for exascale

POP CoE: Understanding applications and how to prepare for exascale POP CoE: Understanding applications and how to prepare for exascale Jesus Labarta (BSC) EU H2020 Center of Excellence (CoE) Lecce, May 17 th 2018 5 th ENES HPC workshop POP objective Promote methodologies

More information

Techniques to improve the scalability of Checkpoint-Restart

Techniques to improve the scalability of Checkpoint-Restart Techniques to improve the scalability of Checkpoint-Restart Bogdan Nicolae Exascale Systems Group IBM Research Ireland 1 Outline A few words about the lab and team Challenges of Exascale A case for Checkpoint-Restart

More information

Integrating Analysis and Computation with Trios Services

Integrating Analysis and Computation with Trios Services October 31, 2012 Integrating Analysis and Computation with Trios Services Approved for Public Release: SAND2012-9323P Ron A. Oldfield Scalable System Software Sandia National Laboratories Albuquerque,

More information

EIOW Exa-scale I/O workgroup (exascale10)

EIOW Exa-scale I/O workgroup (exascale10) EIOW Exa-scale I/O workgroup (exascale10) Meghan McClelland Peter Braam Lug 2013 Large scale data management is fundamentally broken but functions somewhat successfully as an awkward patchwork Current

More information

Parallel I/O and Portable Data Formats I/O strategies

Parallel I/O and Portable Data Formats I/O strategies Parallel I/O and Portable Data Formats I/O strategies Sebastian Lührs s.luehrs@fz-juelich.de Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich, March 13 th, 2017 Outline Common I/O strategies

More information

Structuring PLFS for Extensibility

Structuring PLFS for Extensibility Structuring PLFS for Extensibility Chuck Cranor, Milo Polte, Garth Gibson PARALLEL DATA LABORATORY Carnegie Mellon University What is PLFS? Parallel Log Structured File System Interposed filesystem b/w

More information

Store Process Analyze Collaborate Archive Cloud The HPC Storage Leader Invent Discover Compete

Store Process Analyze Collaborate Archive Cloud The HPC Storage Leader Invent Discover Compete Store Process Analyze Collaborate Archive Cloud The HPC Storage Leader Invent Discover Compete 1 DDN Who We Are 2 We Design, Deploy and Optimize Storage Systems Which Solve HPC, Big Data and Cloud Business

More information

Infinite Memory Engine Freedom from Filesystem Foibles

Infinite Memory Engine Freedom from Filesystem Foibles 1 Infinite Memory Engine Freedom from Filesystem Foibles James Coomer 25 th Sept 2017 2 Bad stuff can happen to filesystems Malaligned High Concurrency Random Shared File COMPUTE NODES FILESYSTEM 3 And

More information

I/O: State of the art and Future developments

I/O: State of the art and Future developments I/O: State of the art and Future developments Giorgio Amati SCAI Dept. Rome, 18/19 May 2016 Some questions Just to know each other: Why are you here? Which is the typical I/O size you work with? GB? TB?

More information

RAIDIX Data Storage Solution. Clustered Data Storage Based on the RAIDIX Software and GPFS File System

RAIDIX Data Storage Solution. Clustered Data Storage Based on the RAIDIX Software and GPFS File System RAIDIX Data Storage Solution Clustered Data Storage Based on the RAIDIX Software and GPFS File System 2017 Contents Synopsis... 2 Introduction... 3 Challenges and the Solution... 4 Solution Architecture...

More information

NEXTGenIO Performance Tools for In-Memory I/O

NEXTGenIO Performance Tools for In-Memory I/O NEXTGenIO Performance Tools for In- I/O holger.brunst@tu-dresden.de ZIH, Technische Universität Dresden 22 nd -23 rd March 2017 Credits Intro slides by Adrian Jackson (EPCC) A new hierarchy New non-volatile

More information

Data Movement & Tiering with DMF 7

Data Movement & Tiering with DMF 7 Data Movement & Tiering with DMF 7 Kirill Malkin Director of Engineering April 2019 Why Move or Tier Data? We wish we could keep everything in DRAM, but It s volatile It s expensive Data in Memory 2 Why

More information

Analyzing I/O Performance on a NEXTGenIO Class System

Analyzing I/O Performance on a NEXTGenIO Class System Analyzing I/O Performance on a NEXTGenIO Class System holger.brunst@tu-dresden.de ZIH, Technische Universität Dresden LUG17, Indiana University, June 2 nd 2017 NEXTGenIO Fact Sheet Project Research & Innovation

More information

HDF5 I/O Performance. HDF and HDF-EOS Workshop VI December 5, 2002

HDF5 I/O Performance. HDF and HDF-EOS Workshop VI December 5, 2002 HDF5 I/O Performance HDF and HDF-EOS Workshop VI December 5, 2002 1 Goal of this talk Give an overview of the HDF5 Library tuning knobs for sequential and parallel performance 2 Challenging task HDF5 Library

More information

Advanced Data Placement via Ad-hoc File Systems at Extreme Scales (ADA-FS)

Advanced Data Placement via Ad-hoc File Systems at Extreme Scales (ADA-FS) Advanced Data Placement via Ad-hoc File Systems at Extreme Scales (ADA-FS) Understanding I/O Performance Behavior (UIOP) 2017 Sebastian Oeste, Mehmet Soysal, Marc-André Vef, Michael Kluge, Wolfgang E.

More information

Large-scale Ultrasound Simulations Using the Hybrid OpenMP/MPI Decomposition

Large-scale Ultrasound Simulations Using the Hybrid OpenMP/MPI Decomposition Large-scale Ultrasound Simulations Using the Hybrid OpenMP/MPI Decomposition Jiri Jaros*, Vojtech Nikl*, Bradley E. Treeby *Department of Compute Systems, Brno University of Technology Department of Medical

More information

System that permanently stores data Usually layered on top of a lower-level physical storage medium Divided into logical units called files

System that permanently stores data Usually layered on top of a lower-level physical storage medium Divided into logical units called files System that permanently stores data Usually layered on top of a lower-level physical storage medium Divided into logical units called files Addressable by a filename ( foo.txt ) Usually supports hierarchical

More information

IBM Spectrum NAS, IBM Spectrum Scale and IBM Cloud Object Storage

IBM Spectrum NAS, IBM Spectrum Scale and IBM Cloud Object Storage IBM Spectrum NAS, IBM Spectrum Scale and IBM Cloud Object Storage Silverton Consulting, Inc. StorInt Briefing 2017 SILVERTON CONSULTING, INC. ALL RIGHTS RESERVED Page 2 Introduction Unstructured data has

More information

DELL EMC ISILON F800 AND H600 I/O PERFORMANCE

DELL EMC ISILON F800 AND H600 I/O PERFORMANCE DELL EMC ISILON F800 AND H600 I/O PERFORMANCE ABSTRACT This white paper provides F800 and H600 performance data. It is intended for performance-minded administrators of large compute clusters that access

More information

UK LUG 10 th July Lustre at Exascale. Eric Barton. CTO Whamcloud, Inc Whamcloud, Inc.

UK LUG 10 th July Lustre at Exascale. Eric Barton. CTO Whamcloud, Inc Whamcloud, Inc. UK LUG 10 th July 2012 Lustre at Exascale Eric Barton CTO Whamcloud, Inc. eeb@whamcloud.com Agenda Exascale I/O requirements Exascale I/O model 3 Lustre at Exascale - UK LUG 10th July 2012 Exascale I/O

More information

Co-existence: Can Big Data and Big Computation Co-exist on the Same Systems?

Co-existence: Can Big Data and Big Computation Co-exist on the Same Systems? Co-existence: Can Big Data and Big Computation Co-exist on the Same Systems? Dr. William Kramer National Center for Supercomputing Applications, University of Illinois Where these views come from Large

More information

Stream Processing for Remote Collaborative Data Analysis

Stream Processing for Remote Collaborative Data Analysis Stream Processing for Remote Collaborative Data Analysis Scott Klasky 146, C. S. Chang 2, Jong Choi 1, Michael Churchill 2, Tahsin Kurc 51, Manish Parashar 3, Alex Sim 7, Matthew Wolf 14, John Wu 7 1 ORNL,

More information

Parallel NetCDF. Rob Latham Mathematics and Computer Science Division Argonne National Laboratory

Parallel NetCDF. Rob Latham Mathematics and Computer Science Division Argonne National Laboratory Parallel NetCDF Rob Latham Mathematics and Computer Science Division Argonne National Laboratory robl@mcs.anl.gov I/O for Computational Science Application Application Parallel File System I/O Hardware

More information

Parallel I/O Libraries and Techniques

Parallel I/O Libraries and Techniques Parallel I/O Libraries and Techniques Mark Howison User Services & Support I/O for scientifc data I/O is commonly used by scientific applications to: Store numerical output from simulations Load initial

More information

Next-Generation NVMe-Native Parallel Filesystem for Accelerating HPC Workloads

Next-Generation NVMe-Native Parallel Filesystem for Accelerating HPC Workloads Next-Generation NVMe-Native Parallel Filesystem for Accelerating HPC Workloads Liran Zvibel CEO, Co-founder WekaIO @liranzvibel 1 WekaIO Matrix: Full-featured and Flexible Public or Private S3 Compatible

More information

AllScale Pilots Applications AmDaDos Adaptive Meshing and Data Assimilation for the Deepwater Horizon Oil Spill

AllScale Pilots Applications AmDaDos Adaptive Meshing and Data Assimilation for the Deepwater Horizon Oil Spill This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No. 671603 An Exascale Programming, Multi-objective Optimisation and Resilience

More information

Application Example Running on Top of GPI-Space Integrating D/C

Application Example Running on Top of GPI-Space Integrating D/C Application Example Running on Top of GPI-Space Integrating D/C Tiberiu Rotaru Fraunhofer ITWM This project is funded from the European Union s Horizon 2020 Research and Innovation programme under Grant

More information

Task-based distributed processing for radio-interferometric imaging with CASA

Task-based distributed processing for radio-interferometric imaging with CASA H2020-Astronomy ESFRI and Research Infrastructure Cluster (Grant Agreement number: 653477). Task-based distributed processing for radio-interferometric imaging with CASA BOJAN NIKOLIC 2 nd ASTERICS-OBELICS

More information

JÜLICH SUPERCOMPUTING CENTRE Site Introduction Michael Stephan Forschungszentrum Jülich

JÜLICH SUPERCOMPUTING CENTRE Site Introduction Michael Stephan Forschungszentrum Jülich JÜLICH SUPERCOMPUTING CENTRE Site Introduction 09.04.2018 Michael Stephan JSC @ Forschungszentrum Jülich FORSCHUNGSZENTRUM JÜLICH Research Centre Jülich One of the 15 Helmholtz Research Centers in Germany

More information

The Fusion Distributed File System

The Fusion Distributed File System Slide 1 / 44 The Fusion Distributed File System Dongfang Zhao February 2015 Slide 2 / 44 Outline Introduction FusionFS System Architecture Metadata Management Data Movement Implementation Details Unique

More information

Distributed Filesystem

Distributed Filesystem Distributed Filesystem 1 How do we get data to the workers? NAS Compute Nodes SAN 2 Distributing Code! Don t move data to workers move workers to the data! - Store data on the local disks of nodes in the

More information

THOUGHTS ABOUT THE FUTURE OF I/O

THOUGHTS ABOUT THE FUTURE OF I/O THOUGHTS ABOUT THE FUTURE OF I/O Dagstuhl Seminar Challenges and Opportunities of User-Level File Systems for HPC Franz-Josef Pfreundt, May 2017 Deep Learning I/O Challenges Memory Centric Computing :

More information

Emerging Technologies for HPC Storage

Emerging Technologies for HPC Storage Emerging Technologies for HPC Storage Dr. Wolfgang Mertz CTO EMEA Unstructured Data Solutions June 2018 The very definition of HPC is expanding Blazing Fast Speed Accessibility and flexibility 2 Traditional

More information

An Exploration into Object Storage for Exascale Supercomputers. Raghu Chandrasekar

An Exploration into Object Storage for Exascale Supercomputers. Raghu Chandrasekar An Exploration into Object Storage for Exascale Supercomputers Raghu Chandrasekar Agenda Introduction Trends and Challenges Design and Implementation of SAROJA Preliminary evaluations Summary and Conclusion

More information

MULTITHERMAN: Out-of-band High-Resolution HPC Power and Performance Monitoring Support for Big-Data Analysis

MULTITHERMAN: Out-of-band High-Resolution HPC Power and Performance Monitoring Support for Big-Data Analysis MULTITHERMAN: Out-of-band High-Resolution HPC Power and Performance Monitoring Support for Big-Data Analysis EU H2020 FETHPC project ANTAREX (g.a. 671623) EU FP7 ERC Project MULTITHERMAN (g.a.291125) EETHPC,

More information

Oak Ridge National Laboratory Computing and Computational Sciences

Oak Ridge National Laboratory Computing and Computational Sciences Oak Ridge National Laboratory Computing and Computational Sciences OFA Update by ORNL Presented by: Pavel Shamis (Pasha) OFA Workshop Mar 17, 2015 Acknowledgments Bernholdt David E. Hill Jason J. Leverman

More information

Workshop: Innovation Procurement in Horizon 2020 PCP Contractors wanted

Workshop: Innovation Procurement in Horizon 2020 PCP Contractors wanted Workshop: Innovation Procurement in Horizon 2020 PCP Contractors wanted Supercomputing Centre Institute for Advanced Simulation / FZJ 1 www.prace-ri.eu Challenges: Aging Society Energy Food How we can

More information

Storage for HPC, HPDA and Machine Learning (ML)

Storage for HPC, HPDA and Machine Learning (ML) for HPC, HPDA and Machine Learning (ML) Frank Kraemer, IBM Systems Architect mailto:kraemerf@de.ibm.com IBM Data Management for Autonomous Driving (AD) significantly increase development efficiency by

More information

Introduction to HPC Parallel I/O

Introduction to HPC Parallel I/O Introduction to HPC Parallel I/O Feiyi Wang (Ph.D.) and Sarp Oral (Ph.D.) Technology Integration Group Oak Ridge Leadership Computing ORNL is managed by UT-Battelle for the US Department of Energy Outline

More information

HPC state of play. HPC Ecosystem. HPC system supply. HPC use 24% EU 4,3% EU. Application software & tools. Academia 23% Bio-sciences 22% CAE 21%

HPC state of play. HPC Ecosystem. HPC system supply. HPC use 24% EU 4,3% EU. Application software & tools. Academia 23% Bio-sciences 22% CAE 21% Only half of US GDP spending HPC state of play HPC Ecosystem HPC system supply 4,3% EU EU market: 630 M/yr (EU lost 10% capacity from 2007 to 2009) 24% EU HPC use Academia 23% Bio-sciences 22% CAE 21%

More information

Application level asynchronous check-pointing / restart: first experiences with GPI

Application level asynchronous check-pointing / restart: first experiences with GPI ERLANGEN REGIONAL COMPUTING CENTER Application level asynchronous check-pointing / restart: first experiences with GPI Faisal Shahzad and Gerhard Wellein Dagstuhl Seminar Resilience in Exascale Computing

More information

CSD3 The Cambridge Service for Data Driven Discovery. A New National HPC Service for Data Intensive science

CSD3 The Cambridge Service for Data Driven Discovery. A New National HPC Service for Data Intensive science CSD3 The Cambridge Service for Data Driven Discovery A New National HPC Service for Data Intensive science Dr Paul Calleja Director of Research Computing University of Cambridge Problem statement Today

More information

Parallel I/O and Portable Data Formats

Parallel I/O and Portable Data Formats Parallel I/O and Portable Data Formats Sebastian Lührs s.luehrs@fz-juelich.de Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Reykjavík, August 25 th, 2017 Overview I/O can be the main bottleneck

More information

Storage-Based Convergence Between HPC and Big Data

Storage-Based Convergence Between HPC and Big Data Storage-Based Convergence Between HPC and Big Data Pierre Matri*, Alexandru Costan, Gabriel Antoniu, Jesús Montes*, María S. Pérez* * Universidad Politécnica de Madrid, Madrid, Spain INSA Rennes / IRISA,

More information

Fast Forward I/O & Storage

Fast Forward I/O & Storage Fast Forward I/O & Storage Eric Barton Lead Architect 1 Department of Energy - Fast Forward Challenge FastForward RFP provided US Government funding for exascale research and development Sponsored by 7

More information

FVM - How to program the Multi-Core FVM instead of MPI

FVM - How to program the Multi-Core FVM instead of MPI FVM - How to program the Multi-Core FVM instead of MPI DLR, 15. October 2009 Dr. Mirko Rahn Competence Center High Performance Computing and Visualization Fraunhofer Institut for Industrial Mathematics

More information

Building supercomputers from embedded technologies

Building supercomputers from embedded technologies http://www.montblanc-project.eu Building supercomputers from embedded technologies Alex Ramirez Barcelona Supercomputing Center Technical Coordinator This project and the research leading to these results

More information

Extreme I/O Scaling with HDF5

Extreme I/O Scaling with HDF5 Extreme I/O Scaling with HDF5 Quincey Koziol Director of Core Software Development and HPC The HDF Group koziol@hdfgroup.org July 15, 2012 XSEDE 12 - Extreme Scaling Workshop 1 Outline Brief overview of

More information

Developing Integrated Data Services for Cray Systems with a Gemini Interconnect

Developing Integrated Data Services for Cray Systems with a Gemini Interconnect Developing Integrated Data Services for Cray Systems with a Gemini Interconnect Ron A. Oldfield Scalable System So4ware Sandia Na9onal Laboratories Albuquerque, NM, USA raoldfi@sandia.gov Cray User Group

More information

Energy Efficiency Tuning: READEX. Madhura Kumaraswamy Technische Universität München

Energy Efficiency Tuning: READEX. Madhura Kumaraswamy Technische Universität München Energy Efficiency Tuning: READEX Madhura Kumaraswamy Technische Universität München Project Overview READEX Starting date: 1. September 2015 Duration: 3 years Runtime Exploitation of Application Dynamism

More information

Data Analytics and Storage System (DASS) Mixing POSIX and Hadoop Architectures. 13 November 2016

Data Analytics and Storage System (DASS) Mixing POSIX and Hadoop Architectures. 13 November 2016 National Aeronautics and Space Administration Data Analytics and Storage System (DASS) Mixing POSIX and Hadoop Architectures 13 November 2016 Carrie Spear (carrie.e.spear@nasa.gov) HPC Architect/Contractor

More information

Coordinating Parallel HSM in Object-based Cluster Filesystems

Coordinating Parallel HSM in Object-based Cluster Filesystems Coordinating Parallel HSM in Object-based Cluster Filesystems Dingshan He, Xianbo Zhang, David Du University of Minnesota Gary Grider Los Alamos National Lab Agenda Motivations Parallel archiving/retrieving

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

The Digitising European Industry strategy & H2020 calls related to Cyber-Physical Systems

The Digitising European Industry strategy & H2020 calls related to Cyber-Physical Systems The Digitising European Industry strategy & H2020 calls related to Cyber-Physical Systems #DigitiseEU Dr. Werner Steinhögl European Commission - DG CONNECT Technologies and Systems for Digitising Industry

More information

Welcome to the. Jülich Supercomputing Centre. D. Rohe and N. Attig Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich

Welcome to the. Jülich Supercomputing Centre. D. Rohe and N. Attig Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich Mitglied der Helmholtz-Gemeinschaft Welcome to the Jülich Supercomputing Centre D. Rohe and N. Attig Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich Schedule: Thursday, Nov 26 13:00-13:30

More information

2014 LENOVO INTERNAL. ALL RIGHTS RESERVED.

2014 LENOVO INTERNAL. ALL RIGHTS RESERVED. 2014 LENOVO INTERNAL. ALL RIGHTS RESERVED. Who is Lenovo? A $39 billion, Fortune 500 technology company - Publicly listed/traded on the Hong Kong Stock Exchange - 54,000 employees serving clients in 160+

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

PARALLEL I/O FOR SHARED MEMORY APPLICATIONS USING OPENMP

PARALLEL I/O FOR SHARED MEMORY APPLICATIONS USING OPENMP PARALLEL I/O FOR SHARED MEMORY APPLICATIONS USING OPENMP A Dissertation Presented to the Faculty of the Department of Computer Science University of Houston In Partial Fulfillment of the Requirements for

More information

Scalable In-memory Checkpoint with Automatic Restart on Failures

Scalable In-memory Checkpoint with Automatic Restart on Failures Scalable In-memory Checkpoint with Automatic Restart on Failures Xiang Ni, Esteban Meneses, Laxmikant V. Kalé Parallel Programming Laboratory University of Illinois at Urbana-Champaign November, 2012 8th

More information

Challenges in HPC I/O

Challenges in HPC I/O Challenges in HPC I/O Universität Basel Julian M. Kunkel German Climate Computing Center / Universität Hamburg 10. October 2014 Outline 1 High-Performance Computing 2 Parallel File Systems and Challenges

More information

MPI RUNTIMES AT JSC, NOW AND IN THE FUTURE

MPI RUNTIMES AT JSC, NOW AND IN THE FUTURE , NOW AND IN THE FUTURE Which, why and how do they compare in our systems? 08.07.2018 I MUG 18, COLUMBUS (OH) I DAMIAN ALVAREZ Outline FZJ mission JSC s role JSC s vision for Exascale-era computing JSC

More information

Data Management. Parallel Filesystems. Dr David Henty HPC Training and Support

Data Management. Parallel Filesystems. Dr David Henty HPC Training and Support Data Management Dr David Henty HPC Training and Support d.henty@epcc.ed.ac.uk +44 131 650 5960 Overview Lecture will cover Why is IO difficult Why is parallel IO even worse Lustre GPFS Performance on ARCHER

More information

LUG 2012 From Lustre 2.1 to Lustre HSM IFERC (Rokkasho, Japan)

LUG 2012 From Lustre 2.1 to Lustre HSM IFERC (Rokkasho, Japan) LUG 2012 From Lustre 2.1 to Lustre HSM Lustre @ IFERC (Rokkasho, Japan) Diego.Moreno@bull.net From Lustre-2.1 to Lustre-HSM - Outline About Bull HELIOS @ IFERC (Rokkasho, Japan) Lustre-HSM - Basis of Lustre-HSM

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

PoS(eIeS2013)008. From Large Scale to Cloud Computing. Speaker. Pooyan Dadvand 1. Sònia Sagristà. Eugenio Oñate

PoS(eIeS2013)008. From Large Scale to Cloud Computing. Speaker. Pooyan Dadvand 1. Sònia Sagristà. Eugenio Oñate 1 International Center for Numerical Methods in Engineering (CIMNE) Edificio C1, Campus Norte UPC, Gran Capitán s/n, 08034 Barcelona, Spain E-mail: pooyan@cimne.upc.edu Sònia Sagristà International Center

More information

libhio: Optimizing IO on Cray XC Systems With DataWarp

libhio: Optimizing IO on Cray XC Systems With DataWarp libhio: Optimizing IO on Cray XC Systems With DataWarp May 9, 2017 Nathan Hjelm Cray Users Group May 9, 2017 Los Alamos National Laboratory LA-UR-17-23841 5/8/2017 1 Outline Background HIO Design Functionality

More information

Leveraging Parallelware in MAESTRO and EPEEC

Leveraging Parallelware in MAESTRO and EPEEC Leveraging Parallelware in MAESTRO and EPEEC and Enhancements to Parallelware Manuel Arenaz manuel.arenaz@appentra.com PRACE booth #2033 Thursday, 15 November 2018 Dallas, US http://www.prace-ri.eu/praceatsc18/

More information

Systems Architectures towards Exascale

Systems Architectures towards Exascale Systems Architectures towards Exascale D. Pleiter German-Indian Workshop on HPC Architectures and Applications Pune 29 November 2016 Outline Introduction Exascale computing Technology trends Architectures

More information

SUSE. High Performance Computing. Eduardo Diaz. Alberto Esteban. PreSales SUSE Linux Enterprise

SUSE. High Performance Computing. Eduardo Diaz. Alberto Esteban. PreSales SUSE Linux Enterprise SUSE High Performance Computing Eduardo Diaz PreSales SUSE Linux Enterprise ediaz@suse.com Alberto Esteban Territory Manager North-East SUSE Linux Enterprise aesteban@suse.com HPC Overview SUSE High Performance

More information

CERN openlab & IBM Research Workshop Trip Report

CERN openlab & IBM Research Workshop Trip Report CERN openlab & IBM Research Workshop Trip Report Jakob Blomer, Javier Cervantes, Pere Mato, Radu Popescu 2018-12-03 Workshop Organization 1 full day at IBM Research Zürich ~25 participants from CERN ~10

More information

XtreemFS a case for object-based storage in Grid data management. Jan Stender, Zuse Institute Berlin

XtreemFS a case for object-based storage in Grid data management. Jan Stender, Zuse Institute Berlin XtreemFS a case for object-based storage in Grid data management Jan Stender, Zuse Institute Berlin In this talk... Traditional Grid Data Management Object-based file systems XtreemFS Grid use cases for

More information

Storage Challenges at Los Alamos National Lab

Storage Challenges at Los Alamos National Lab John Bent john.bent@emc.com Storage Challenges at Los Alamos National Lab Meghan McClelland meghan@lanl.gov Gary Grider ggrider@lanl.gov Aaron Torres agtorre@lanl.gov Brett Kettering brettk@lanl.gov Adam

More information

Brent Gorda. General Manager, High Performance Data Division

Brent Gorda. General Manager, High Performance Data Division Brent Gorda General Manager, High Performance Data Division Legal Disclaimer Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the

More information

Utilizing Unused Resources To Improve Checkpoint Performance

Utilizing Unused Resources To Improve Checkpoint Performance Utilizing Unused Resources To Improve Checkpoint Performance Ross Miller Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak Ridge, Tennessee Email: rgmiller@ornl.gov Scott Atchley

More information

MULTITHERMAN: Out-of-band High-Resolution HPC Power and Performance Monitoring Support for Big-Data Analysis

MULTITHERMAN: Out-of-band High-Resolution HPC Power and Performance Monitoring Support for Big-Data Analysis MULTITHERMAN: Out-of-band High-Resolution HPC Power and Performance Monitoring Support for Big-Data Analysis EU H2020 FETHPC project ANTAREX (g.a. 671623) EU FP7 ERC Project MULTITHERMAN (g.a.291125) HPC

More information

SEVENTH FRAMEWORK PROGRAMME

SEVENTH FRAMEWORK PROGRAMME SEVENTH FRAMEWORK PROGRAMME FP7-ICT-2013-10 DEEP-ER DEEP Extended Reach Grant Agreement Number: 610476 D7.1 Report on performance extrapolation based on design decisions Approved Version: 2.0 Author(s):

More information

EMPRESS Extensible Metadata PRovider for Extreme-scale Scientific Simulations

EMPRESS Extensible Metadata PRovider for Extreme-scale Scientific Simulations EMPRESS Extensible Metadata PRovider for Extreme-scale Scientific Simulations Photos placed in horizontal position with even amount of white space between photos and header Margaret Lawson, Jay Lofstead,

More information

The Google File System

The Google File System The Google File System Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung ACM SIGOPS 2003 {Google Research} Vaibhav Bajpai NDS Seminar 2011 Looking Back time Classics Sun NFS (1985) CMU Andrew FS (1988) Fault

More information

Optimization of non-contiguous MPI-I/O operations

Optimization of non-contiguous MPI-I/O operations Optimization of non-contiguous MPI-I/O operations Enno Zickler Arbeitsbereich Wissenschaftliches Rechnen Fachbereich Informatik Fakultät für Mathematik, Informatik und Naturwissenschaften Universität Hamburg

More information