Challenges in HPC I/O

Size: px
Start display at page:

Download "Challenges in HPC I/O"

Transcription

1 Challenges in HPC I/O Universität Basel Julian M. Kunkel German Climate Computing Center / Universität Hamburg 10. October 2014

2 Outline 1 High-Performance Computing 2 Parallel File Systems and Challenges 3 Analyzing and Optimizing I/O 4 Discussion & Outlook 5 Summary & Conclusions Julian M. Kunkel Challenges in HPC I/O 2 / 26

3 High-Performance Computing Motivation Scientific applications have a high demand of Computing time Memory Storage A common PC/server is not able to compute solution (in time) Parallel usage of many CPUs and servers Moore s law increases parallelism and not (any more) frequency Julian M. Kunkel Challenges in HPC I/O 3 / 26

4 Relevance illustrated on the Human Brain project Source: Simulation Codes in the Human Brain Project, Prof. Felix Schürmann, 2014 Julian M. Kunkel Challenges in HPC I/O 4 / 26

5 High-Performance Computing Parallel File Systems and Challenges Analyzing and Optimizing I/O Discussion & Outlook Summary & Conclusions Titan: Second Fastest Supercomputer Titan front view [Source: Wikipedia, Titan_(supercomputer)] 200 racks on 400 m nodes: 1 AMD Opteron 16-core CPU 1 Nvidia Tesla GPU 32 GByte RAM Julian M. Kunkel Peak performance: 27 PFlop/s Main memory: 710 TByte Storage capacity: 40 PByte Costs: 100 million $ Energy consumption: 8.2 MW Challenges in HPC I/O 5 / 26

6 Definition: High-Performance Computing 1 Programming of applications with high resource demand 2 Construction and usage of supercomputers Selected subdisciplines CPU Network Hardware Storage Programming models Parallel I/O Scientific Software- Engineering Software Debugging Tools Cooling Administration Performance analysis User support Data center Operation model & cost-efficiency Julian M. Kunkel Challenges in HPC I/O 6 / 26

7 Research Profile Parallel I/O and performance analysis Scientific Software-Engineering and cost-efficiency CPU Network Hardware Storage Programming models Parallel I/O Scientific Software- Engineering Software Debugging Tools Cooling Administration Performance analysis User support Data center Operation model & cost-efficiency Julian M. Kunkel Challenges in HPC I/O 7 / 26

8 Julian M. Kunkel Challenges in HPC I/O 8 / 26

9 1 High-Performance Computing 2 Parallel File Systems and Challenges Parallel File Systems Challenges Performance Optimization 3 Analyzing and Optimizing I/O 4 Discussion & Outlook 5 Summary & Conclusions Julian M. Kunkel Challenges in HPC I/O 8 / 26

10 Parallel File Systems Distribute data (and metadata) among many servers Goal: Aggregated performance of all servers Parallel: Concurrent access of processes to a shared file Hierarchical namespace / dev home proc usr stored on home hans franz hans solutiona.txt myprogram tmp franz myfile.xyz computed.dat myfile.xyz offset Parallel file system file Example hierarchical namespace Julian M. Kunkel Challenges in HPC I/O 9 / 26

11 Challenges of the HPC-I/O Stack Co-existence of different access paradigms File (POSIX, ADIOS, HDF5), SQL, NoSQL File system: Level of abstraction is very low A file is an array of bytes Scientific domains re-implement similar features Re-implementation of features across the stack Unpredictable interactions and resource waste Loss of semantical (application) knowledge Suboptimal steering and performance loss Insufficient performance portability Each layer needs to be optimized for all systems Handling Petabyte of data? Example I/O stack Julian M. Kunkel Challenges in HPC I/O 10 / 26

12 Semantical Gap of File Access Information hidden from file systems Data types Data semantics Required synchronization Value of data Data lifecycle: production, usage, deletion Characteristics can even vary within a file Storage systems could use this information Improving performance: Automatic tiering, caching, replication Simplifying management: ILM, offering alternative data views Correctness: Ensuring data consistency Julian M. Kunkel Challenges in HPC I/O 11 / 26

13 Tuning Performance There are many options to tune the I/O-stack API: posix_fadvise(), HDF5 properties, open flags, cache size Via command line: lfs setstripe Setup/initialization of a storage system Most options are of technical nature Example: read-ahead window of 256 KiB Many types of hints/tweaks are not portable Performance gain/loss is system (and application) specific Performance loss forces us to use these optimization Are 50% performance loss acceptable if one option is wrong? Julian M. Kunkel Challenges in HPC I/O 12 / 26

14 Julian M. Kunkel Challenges in HPC I/O 13 / 26

15 1 High-Performance Computing 2 Parallel File Systems and Challenges 3 Analyzing and Optimizing I/O 4 Discussion & Outlook 5 Summary & Conclusions Julian M. Kunkel Challenges in HPC I/O 13 / 26

16 BMBF-Project: SIOX Motivation Lack of tools for assessing application s I/O performance Existing optimizations are hard to parameterize Goals of SIOX Monitoring of I/O behavior and performance Automatic assessment of observed activity Extracting of new knowledge Learn (and apply) controllable optimizations (using ML) Cooperation Universität Hamburg, HLRS Stuttgart, ZIH Dresden, Exascale10 Julian M. Kunkel Challenges in HPC I/O 14 / 26

17 High-Level Architectural View Integration of SIOX in a typical HPC I/O stack Julian M. Kunkel Challenges in HPC I/O 15 / 26

18 Example Study: Learning Parameters for MPI-I/O Background Non-Contiguous I/O accesses multiple file regions by one call The data-sieving optimization accesses whole datatype Optimization parameters (on file level): on/off, buffer size Accessed data (ddata) Hole - data not accessed (dhole) Approach Measure a set of similar patterns by one process Vary: block size, gap, optimization (> 700 configurations) Use ML to predict performance choose best optimization Julian M. Kunkel Challenges in HPC I/O 16 / 26

19 Performance for ddata = 256 KiB Julian M. Kunkel Challenges in HPC I/O 17 / 26

20 Decision Trees Performance prediction, first 3 levels, classes slow, avg, fast ([0; 25], ]25; 75], > 75 MiB/s). Dominant label in the leaf nodes class probability is provided in () Optimization to choose Rules can be extracted to create (new) knowledge Julian M. Kunkel Challenges in HPC I/O 18 / 26

21 Example-Optimization: Pre-fetching Results Pre-fetching: Read data from slower medium before needed Use posix_fadvise() to tell OS to read data Benchmark reads data and simulates compute time 100 µs and 10 ms for 20 KiB and 1000 KiB stride Program: Manual pre-fetching vs. extended version SIOX plug-in injects advise without code modification Experiment 20 KiB stride 1000 KiB stride Regular execution 97.1 µs µs Embedded fadvise 38.7 µs 45.1 µs SIOX injects fadvise 52.1 µs 95.4 µs Average time to read 1 KiB of data Julian M. Kunkel Challenges in HPC I/O 19 / 26

22 Julian M. Kunkel Challenges in HPC I/O 20 / 26

23 1 High-Performance Computing 2 Parallel File Systems and Challenges 3 Analyzing and Optimizing I/O 4 Discussion & Outlook 5 Summary & Conclusions Julian M. Kunkel Challenges in HPC I/O 20 / 26

24 Critical Discussion Questions from the users perspective Why do I have to organize the file format? It s like taking care of the memory layout of C-structs Why must I provide system-specific performance hints? It s like telling the compiler to unroll a loop exactly 4 times Why do I have to convert data between storage paradigms? I want to access data of my experiments based on their properties Why is a file system not offering the consistency model I need? My application knows the required level of synchronization Julian M. Kunkel Challenges in HPC I/O 21 / 26

25 User View on Controlling File Systems Performance Source: Elya: Antonow an24 cockpit, Wikipedia commons, CC BY-SA 3.0 Do you believe this complexity is needed in storage systems? Would you rather like to code your actual application? Julian M. Kunkel Challenges in HPC I/O 22 / 26

26 Personal Vision of Future Storage Systems Programmability Application focus Natural storage access Data mining Semantical name space Guided interface NoSQL HDF5 Access paradigm Database Data transformation Dynamic on-disk format Content aware Intelligence Data exploration Smart Semantical access Semi-structured data Data replication Arbitrary views File system Local storage ILM/HSM Hierarchical storage Topology aware Self-awareness System characteristics Performance model User Storage system Julian M. Kunkel Challenges in HPC I/O 23 / 26

27 The Exascale I/O Workgroup (Exascale10) Goal: Development of a middleware with advanced features Data-type aware storage Multiple views to the same data (BigData) Guided interfaces instead of technical hints Data format handled by storage system Multi-tiering support Intelligent monitoring Feedback to optimization and what-if analysis Integrates active storage concept Post-processing is handled by file system First design documents have been published Background E10 is a workgroup of EOFS (Lustre community) International and open initiative Julian M. Kunkel Challenges in HPC I/O 24 / 26

28 Julian M. Kunkel Challenges in HPC I/O 25 / 26

29 1 High-Performance Computing 2 Parallel File Systems and Challenges 3 Analyzing and Optimizing I/O 4 Discussion & Outlook 5 Summary & Conclusions Julian M. Kunkel Challenges in HPC I/O 25 / 26

30 Summary & Conclusions High-Performance Computing is a fascinating area Efficiency of money spent can be improved Requires holistic view of hardware, sw, middleware & data center Demand for semi-automatic optimization tools SIOX combines measurement, analysis and optimization Storage access paradigms need to change Flexible, intelligent and self-aware storage Guided interfaces vs. technical hints Exalscale10 is an enabler for data intensive science Julian M. Kunkel Challenges in HPC I/O 26 / 26

31 Backup Folien Julian M. Kunkel Challenges in HPC I/O 27 / 26

32 Expected Input from Users: Guided Interfaces Guiding vs. automatism vs. technical hints Users provide additional information to guide an intelligent system. The I/O stack exploits this information. Information which could be provided by users Data types Semantics Relations between data Lifecycle (especially usage) Several issues have been addressed in different access paradigms. Also some behavioral hints exist: open() flags, fadvise(),... Julian M. Kunkel Challenges in HPC I/O 28 / 26

33 Parallel Programming Goal Many CPUs cooperatively compute the solution Approach Parallel processing of data and/or tasks Cooperation requires coordination and communication Programming model defines formal specification Application Process 0 Process 1 Process 2 Process 3 Processor Node Network Application with four processes on two nodes Julian M. Kunkel Challenges in HPC I/O 29 / 26

I/O at the German Climate Computing Center (DKRZ)

I/O at the German Climate Computing Center (DKRZ) I/O at the German Climate Computing Center (DKRZ) Julian M. Kunkel, Carsten Beyer kunkel@dkrz.de German Climate Computing Center (DKRZ) 16-07-2015 Outline 1 Introduction 2 Workload 3 System View 4 Obstacles

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

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

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

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

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

朱义普. Resolving High Performance Computing and Big Data Application Bottlenecks with Application-Defined Flash Acceleration. Director, North Asia, HPC

朱义普. Resolving High Performance Computing and Big Data Application Bottlenecks with Application-Defined Flash Acceleration. Director, North Asia, HPC October 28, 2013 Resolving High Performance Computing and Big Data Application Bottlenecks with Application-Defined Flash Acceleration 朱义普 Director, North Asia, HPC DDN Storage Vendor for HPC & Big Data

More information

Crossing the Chasm: Sneaking a parallel file system into Hadoop

Crossing the Chasm: Sneaking a parallel file system into Hadoop Crossing the Chasm: Sneaking a parallel file system into Hadoop Wittawat Tantisiriroj Swapnil Patil, Garth Gibson PARALLEL DATA LABORATORY Carnegie Mellon University In this work Compare and contrast large

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

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

I/O Perspectives for Earthsystem Data

I/O Perspectives for Earthsystem Data I/O Perspectives for Earthsystem Data Julian M. Kunkel kunkel@dkrz.de German Climate Computing Center (DKRZ), Research Group/Scientific Computing (WR) 05-11-2016 My High-Level Perspective on I/O Current

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

Crossing the Chasm: Sneaking a parallel file system into Hadoop

Crossing the Chasm: Sneaking a parallel file system into Hadoop Crossing the Chasm: Sneaking a parallel file system into Hadoop Wittawat Tantisiriroj Swapnil Patil, Garth Gibson PARALLEL DATA LABORATORY Carnegie Mellon University In this work Compare and contrast large

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

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

Managing Hardware Power Saving Modes for High Performance Computing

Managing Hardware Power Saving Modes for High Performance Computing Managing Hardware Power Saving Modes for High Performance Computing Second International Green Computing Conference 2011, Orlando Timo Minartz, Michael Knobloch, Thomas Ludwig, Bernd Mohr timo.minartz@informatik.uni-hamburg.de

More information

Efficiency and Programmability: Enablers for ExaScale. Bill Dally Chief Scientist and SVP, Research NVIDIA Professor (Research), EE&CS, Stanford

Efficiency and Programmability: Enablers for ExaScale. Bill Dally Chief Scientist and SVP, Research NVIDIA Professor (Research), EE&CS, Stanford Efficiency and Programmability: Enablers for ExaScale Bill Dally Chief Scientist and SVP, Research NVIDIA Professor (Research), EE&CS, Stanford Scientific Discovery and Business Analytics Driving an Insatiable

More information

Toward a Windows Native Client (WNC) Meghan McClelland LAD2013

Toward a Windows Native Client (WNC) Meghan McClelland LAD2013 Toward a Windows Native Client (WNC) Meghan McClelland Meghan_McClelland@xyratex.com LAD2013 Overview At LUG 2013 there was expressed strong interest in a WNC client. Xyratex acquired IP from Oracle. The

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

Enosis: Bridging the Semantic Gap between

Enosis: Bridging the Semantic Gap between Enosis: Bridging the Semantic Gap between File-based and Object-based Data Models Anthony Kougkas - akougkas@hawk.iit.edu, Hariharan Devarajan, Xian-He Sun Outline Introduction Background Approach Evaluation

More information

Carlo Cavazzoni, HPC department, CINECA

Carlo Cavazzoni, HPC department, CINECA Introduction to Shared memory architectures Carlo Cavazzoni, HPC department, CINECA Modern Parallel Architectures Two basic architectural scheme: Distributed Memory Shared Memory Now most computers have

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

Green Supercomputing

Green Supercomputing Green Supercomputing On the Energy Consumption of Modern E-Science Prof. Dr. Thomas Ludwig German Climate Computing Centre Hamburg, Germany ludwig@dkrz.de Outline DKRZ 2013 and Climate Science The Exascale

More information

Trends in HPC (hardware complexity and software challenges)

Trends in HPC (hardware complexity and software challenges) Trends in HPC (hardware complexity and software challenges) Mike Giles Oxford e-research Centre Mathematical Institute MIT seminar March 13th, 2013 Mike Giles (Oxford) HPC Trends March 13th, 2013 1 / 18

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

JULEA: A Flexible Storage Framework for HPC

JULEA: A Flexible Storage Framework for HPC JULEA: A Flexible Storage Framework for HPC Workshop on Performance and Scalability of Storage Systems Michael Kuhn Research Group Scientific Computing Department of Informatics Universität Hamburg 2017-06-22

More information

Exploiting Weather & Climate Data at Scale (WP4)

Exploiting Weather & Climate Data at Scale (WP4) Exploiting Weather & Climate Data at Scale (WP4) Julian Kunkel 1 Bryan N. Lawrence 2,3 Jakob Luettgau 1 Neil Massey 4 Alessandro Danca 5 Sandro Fiore 5 Huang Hu 6 1 German Climate Computing Center (DKRZ)

More information

Introduction to Parallel I/O

Introduction to Parallel I/O Introduction to Parallel I/O Bilel Hadri bhadri@utk.edu NICS Scientific Computing Group OLCF/NICS Fall Training October 19 th, 2011 Outline Introduction to I/O Path from Application to File System Common

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

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

Online Monitoring of I/O

Online Monitoring of I/O Introduction On-line Monitoring Framework Evaluation Summary References Research Group German Climate Computing Center 23-03-2017 Introduction On-line Monitoring Framework Evaluation Summary References

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

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

Open Data Standards for Administrative Data Processing

Open Data Standards for Administrative Data Processing University of Pennsylvania ScholarlyCommons 2018 ADRF Network Research Conference Presentations ADRF Network Research Conference Presentations 11-2018 Open Data Standards for Administrative Data Processing

More information

Martin Kruliš, v

Martin Kruliš, v Martin Kruliš 1 Optimizations in General Code And Compilation Memory Considerations Parallelism Profiling And Optimization Examples 2 Premature optimization is the root of all evil. -- D. Knuth Our goal

More information

Exascale: challenges and opportunities in a power constrained world

Exascale: challenges and opportunities in a power constrained world Exascale: challenges and opportunities in a power constrained world Carlo Cavazzoni c.cavazzoni@cineca.it SuperComputing Applications and Innovation Department CINECA CINECA non profit Consortium, made

More information

Real-Time I/O-Monitoring of HPC Applications

Real-Time I/O-Monitoring of HPC Applications Introduction Architecture Evaluation Summary References Research Group German Climate Computing Center 2017-11-15 Introduction Architecture Evaluation Summary References Introduction Why monitoring? Monitoring

More information

The Future of Interconnect Technology

The Future of Interconnect Technology The Future of Interconnect Technology Michael Kagan, CTO HPC Advisory Council Stanford, 2014 Exponential Data Growth Best Interconnect Required 44X 0.8 Zetabyte 2009 35 Zetabyte 2020 2014 Mellanox Technologies

More information

GFS: The Google File System

GFS: The Google File System GFS: The Google File System Brad Karp UCL Computer Science CS GZ03 / M030 24 th October 2014 Motivating Application: Google Crawl the whole web Store it all on one big disk Process users searches on one

More information

A FRAMEWORK ARCHITECTURE FOR SHARED FILE POINTER OPERATIONS IN OPEN MPI

A FRAMEWORK ARCHITECTURE FOR SHARED FILE POINTER OPERATIONS IN OPEN MPI A FRAMEWORK ARCHITECTURE FOR SHARED FILE POINTER OPERATIONS IN OPEN MPI A Thesis Presented to the Faculty of the Department of Computer Science University of Houston In Partial Fulfillment of the Requirements

More information

rfs Remote File System Softwarepraktikum für Fortgeschrittene

rfs Remote File System Softwarepraktikum für Fortgeschrittene rfs Remote File System Softwarepraktikum für Fortgeschrittene Parallele und Verteilte Systeme Institut für Informatik Ruprecht-Karls-Universität Heidelberg Michael Kuhn Betreuer: Julian Kunkel 2009-07-09

More information

Welcome! Virtual tutorial starts at 15:00 BST

Welcome! Virtual tutorial starts at 15:00 BST Welcome! Virtual tutorial starts at 15:00 BST Parallel IO and the ARCHER Filesystem ARCHER Virtual Tutorial, Wed 8 th Oct 2014 David Henty Reusing this material This work is licensed

More information

I/O-500 Status. Julian M. Kunkel 1, Jay Lofstead 2, John Bent 3, George S. Markomanolis

I/O-500 Status. Julian M. Kunkel 1, Jay Lofstead 2, John Bent 3, George S. Markomanolis I/O-500 Status Julian M. Kunkel 1, Jay Lofstead 2, John Bent 3, George S. Markomanolis 4 1. Deutsches Klimarechenzentrum GmbH (DKRZ) 2. Sandia National Laboratory 3. Seagate Government Solutions 4. KAUST

More information

I/O in scientific applications

I/O in scientific applications COSC 4397 Parallel I/O (II) Access patterns Spring 2010 I/O in scientific applications Different classes of I/O operations Required I/O: reading input data and writing final results Checkpointing: data

More information

HPC Architectures. Types of resource currently in use

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

More information

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

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

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

Potentials and Limitations for Energy Efficiency Auto-Tuning

Potentials and Limitations for Energy Efficiency Auto-Tuning Center for Information Services and High Performance Computing (ZIH) Potentials and Limitations for Energy Efficiency Auto-Tuning Parco Symposium Application Autotuning for HPC (Architectures) Robert Schöne

More information

IBM Spectrum Scale IO performance

IBM Spectrum Scale IO performance IBM Spectrum Scale 5.0.0 IO performance Silverton Consulting, Inc. StorInt Briefing 2 Introduction High-performance computing (HPC) and scientific computing are in a constant state of transition. Artificial

More information

AutoTune Workshop. Michael Gerndt Technische Universität München

AutoTune Workshop. Michael Gerndt Technische Universität München AutoTune Workshop Michael Gerndt Technische Universität München AutoTune Project Automatic Online Tuning of HPC Applications High PERFORMANCE Computing HPC application developers Compute centers: Energy

More information

Parallel Programming Principle and Practice. Lecture 9 Introduction to GPGPUs and CUDA Programming Model

Parallel Programming Principle and Practice. Lecture 9 Introduction to GPGPUs and CUDA Programming Model Parallel Programming Principle and Practice Lecture 9 Introduction to GPGPUs and CUDA Programming Model Outline Introduction to GPGPUs and Cuda Programming Model The Cuda Thread Hierarchy / Memory Hierarchy

More information

File Systems. Information Server 1. Content. Motivation. Motivation. File Systems. File Systems. Files

File Systems. Information Server 1. Content. Motivation. Motivation. File Systems. File Systems. Files Content File Systems Hengming Zou, Ph.D. Files Disk scheduling, file structure, indexed files Directories File system implementation File system performance Example file systems 2006-4-29 1 2006-4-29 2

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

Distributed File Systems II

Distributed File Systems II Distributed File Systems II To do q Very-large scale: Google FS, Hadoop FS, BigTable q Next time: Naming things GFS A radically new environment NFS, etc. Independence Small Scale Variety of workloads Cooperation

More information

Outline 1 Motivation 2 Theory of a non-blocking benchmark 3 The benchmark and results 4 Future work

Outline 1 Motivation 2 Theory of a non-blocking benchmark 3 The benchmark and results 4 Future work Using Non-blocking Operations in HPC to Reduce Execution Times David Buettner, Julian Kunkel, Thomas Ludwig Euro PVM/MPI September 8th, 2009 Outline 1 Motivation 2 Theory of a non-blocking benchmark 3

More information

The BioHPC Nucleus Cluster & Future Developments

The BioHPC Nucleus Cluster & Future Developments 1 The BioHPC Nucleus Cluster & Future Developments Overview Today we ll talk about the BioHPC Nucleus HPC cluster with some technical details for those interested! How is it designed? What hardware does

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

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

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

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

Fra superdatamaskiner til grafikkprosessorer og

Fra superdatamaskiner til grafikkprosessorer og Fra superdatamaskiner til grafikkprosessorer og Brødtekst maskinlæring Prof. Anne C. Elster IDI HPC/Lab Parallel Computing: Personal perspective 1980 s: Concurrent and Parallel Pascal 1986: Intel ipsc

More information

Interactive Analysis of Large Distributed Systems with Scalable Topology-based Visualization

Interactive Analysis of Large Distributed Systems with Scalable Topology-based Visualization Interactive Analysis of Large Distributed Systems with Scalable Topology-based Visualization Lucas M. Schnorr, Arnaud Legrand, and Jean-Marc Vincent e-mail : Firstname.Lastname@imag.fr Laboratoire d Informatique

More information

Steve Scott, Tesla CTO SC 11 November 15, 2011

Steve Scott, Tesla CTO SC 11 November 15, 2011 Steve Scott, Tesla CTO SC 11 November 15, 2011 What goal do these products have in common? Performance / W Exaflop Expectations First Exaflop Computer K Computer ~10 MW CM5 ~200 KW Not constant size, cost

More information

Introduction to Parallel Computing. CPS 5401 Fall 2014 Shirley Moore, Instructor October 13, 2014

Introduction to Parallel Computing. CPS 5401 Fall 2014 Shirley Moore, Instructor October 13, 2014 Introduction to Parallel Computing CPS 5401 Fall 2014 Shirley Moore, Instructor October 13, 2014 1 Definition of Parallel Computing Simultaneous use of multiple compute resources to solve a computational

More information

On the Role of Burst Buffers in Leadership- Class Storage Systems

On the Role of Burst Buffers in Leadership- Class Storage Systems On the Role of Burst Buffers in Leadership- Class Storage Systems Ning Liu, Jason Cope, Philip Carns, Christopher Carothers, Robert Ross, Gary Grider, Adam Crume, Carlos Maltzahn Contact: liun2@cs.rpi.edu,

More information

MPI in 2020: Opportunities and Challenges. William Gropp

MPI in 2020: Opportunities and Challenges. William Gropp MPI in 2020: Opportunities and Challenges William Gropp www.cs.illinois.edu/~wgropp MPI and Supercomputing The Message Passing Interface (MPI) has been amazingly successful First released in 1992, it is

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

An Introduction to the SPEC High Performance Group and their Benchmark Suites

An Introduction to the SPEC High Performance Group and their Benchmark Suites An Introduction to the SPEC High Performance Group and their Benchmark Suites Robert Henschel Manager, Scientific Applications and Performance Tuning Secretary, SPEC High Performance Group Research Technologies

More information

Tracing and Visualization of Energy Related Metrics

Tracing and Visualization of Energy Related Metrics Tracing and Visualization of Energy Related Metrics 8th Workshop on High-Performance, Power-Aware Computing 2012, Shanghai Timo Minartz, Julian Kunkel, Thomas Ludwig timo.minartz@informatik.uni-hamburg.de

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

Introduction CPS343. Spring Parallel and High Performance Computing. CPS343 (Parallel and HPC) Introduction Spring / 29

Introduction CPS343. Spring Parallel and High Performance Computing. CPS343 (Parallel and HPC) Introduction Spring / 29 Introduction CPS343 Parallel and High Performance Computing Spring 2018 CPS343 (Parallel and HPC) Introduction Spring 2018 1 / 29 Outline 1 Preface Course Details Course Requirements 2 Background Definitions

More information

Optimizing Energy Consumption and Parallel Performance for Static and Dynamic Betweenness Centrality using GPUs Adam McLaughlin, Jason Riedy, and

Optimizing Energy Consumption and Parallel Performance for Static and Dynamic Betweenness Centrality using GPUs Adam McLaughlin, Jason Riedy, and Optimizing Energy Consumption and Parallel Performance for Static and Dynamic Betweenness Centrality using GPUs Adam McLaughlin, Jason Riedy, and David A. Bader Motivation Real world graphs are challenging

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

Overview of research activities Toward portability of performance

Overview of research activities Toward portability of performance Overview of research activities Toward portability of performance Do dynamically what can t be done statically Understand evolution of architectures Enable new programming models Put intelligence into

More information

The Arm Technology Ecosystem: Current Products and Future Outlook

The Arm Technology Ecosystem: Current Products and Future Outlook The Arm Technology Ecosystem: Current Products and Future Outlook Dan Ernst, PhD Advanced Technology Cray, Inc. Why is an Ecosystem Important? An Ecosystem is a collection of common material Developed

More information

Enabling Active Storage on Parallel I/O Software Stacks. Seung Woo Son Mathematics and Computer Science Division

Enabling Active Storage on Parallel I/O Software Stacks. Seung Woo Son Mathematics and Computer Science Division Enabling Active Storage on Parallel I/O Software Stacks Seung Woo Son sson@mcs.anl.gov Mathematics and Computer Science Division MSST 2010, Incline Village, NV May 7, 2010 Performing analysis on large

More information

Automatic Identification of Application I/O Signatures from Noisy Server-Side Traces. Yang Liu Raghul Gunasekaran Xiaosong Ma Sudharshan S.

Automatic Identification of Application I/O Signatures from Noisy Server-Side Traces. Yang Liu Raghul Gunasekaran Xiaosong Ma Sudharshan S. Automatic Identification of Application I/O Signatures from Noisy Server-Side Traces Yang Liu Raghul Gunasekaran Xiaosong Ma Sudharshan S. Vazhkudai Instance of Large-Scale HPC Systems ORNL s TITAN (World

More information

ENERGY-EFFICIENT VISUALIZATION PIPELINES A CASE STUDY IN CLIMATE SIMULATION

ENERGY-EFFICIENT VISUALIZATION PIPELINES A CASE STUDY IN CLIMATE SIMULATION ENERGY-EFFICIENT VISUALIZATION PIPELINES A CASE STUDY IN CLIMATE SIMULATION Vignesh Adhinarayanan Ph.D. (CS) Student Synergy Lab, Virginia Tech INTRODUCTION Supercomputers are constrained by power Power

More information

The Stampede is Coming: A New Petascale Resource for the Open Science Community

The Stampede is Coming: A New Petascale Resource for the Open Science Community The Stampede is Coming: A New Petascale Resource for the Open Science Community Jay Boisseau Texas Advanced Computing Center boisseau@tacc.utexas.edu Stampede: Solicitation US National Science Foundation

More information

Why you should care about hardware locality and how.

Why you should care about hardware locality and how. Why you should care about hardware locality and how. Brice Goglin TADaaM team Inria Bordeaux Sud-Ouest Agenda Quick example as an introduction Bind your processes What's the actual problem? Convenient

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

Parallel Programming

Parallel Programming Parallel Programming Introduction Diego Fabregat-Traver and Prof. Paolo Bientinesi HPAC, RWTH Aachen fabregat@aices.rwth-aachen.de WS15/16 Acknowledgements Prof. Felix Wolf, TU Darmstadt Prof. Matthias

More information

Overview of Tianhe-2

Overview of Tianhe-2 Overview of Tianhe-2 (MilkyWay-2) Supercomputer Yutong Lu School of Computer Science, National University of Defense Technology; State Key Laboratory of High Performance Computing, China ytlu@nudt.edu.cn

More information

THE GLOBUS PROJECT. White Paper. GridFTP. Universal Data Transfer for the Grid

THE GLOBUS PROJECT. White Paper. GridFTP. Universal Data Transfer for the Grid THE GLOBUS PROJECT White Paper GridFTP Universal Data Transfer for the Grid WHITE PAPER GridFTP Universal Data Transfer for the Grid September 5, 2000 Copyright 2000, The University of Chicago and The

More information

MDHIM: A Parallel Key/Value Store Framework for HPC

MDHIM: A Parallel Key/Value Store Framework for HPC MDHIM: A Parallel Key/Value Store Framework for HPC Hugh Greenberg 7/6/2015 LA-UR-15-25039 HPC Clusters Managed by a job scheduler (e.g., Slurm, Moab) Designed for running user jobs Difficult to run system

More information

Insight: that s for NSA Decision making: that s for Google, Facebook. so they find the best way to push out adds and products

Insight: that s for NSA Decision making: that s for Google, Facebook. so they find the best way to push out adds and products What is big data? Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.

More information

SDS: A Framework for Scientific Data Services

SDS: A Framework for Scientific Data Services SDS: A Framework for Scientific Data Services Bin Dong, Suren Byna*, John Wu Scientific Data Management Group Lawrence Berkeley National Laboratory Finding Newspaper Articles of Interest Finding news articles

More information

Parallel Programming Models. Parallel Programming Models. Threads Model. Implementations 3/24/2014. Shared Memory Model (without threads)

Parallel Programming Models. Parallel Programming Models. Threads Model. Implementations 3/24/2014. Shared Memory Model (without threads) Parallel Programming Models Parallel Programming Models Shared Memory (without threads) Threads Distributed Memory / Message Passing Data Parallel Hybrid Single Program Multiple Data (SPMD) Multiple Program

More information

I/O at the Center for Information Services and High Performance Computing

I/O at the Center for Information Services and High Performance Computing Mich ael Kluge, ZIH I/O at the Center for Information Services and High Performance Computing HPC-I/O in the Data Center Workshop @ ISC 2015 Zellescher Weg 12 Willers-Bau A 208 Tel. +49 351-463 34217 Michael

More information

Parallel Computing. Parallel Computing. Hwansoo Han

Parallel Computing. Parallel Computing. Hwansoo Han Parallel Computing Parallel Computing Hwansoo Han What is Parallel Computing? Software with multiple threads Parallel vs. concurrent Parallel computing executes multiple threads at the same time on multiple

More information

Introduction to tuning on many core platforms. Gilles Gouaillardet RIST

Introduction to tuning on many core platforms. Gilles Gouaillardet RIST Introduction to tuning on many core platforms Gilles Gouaillardet RIST gilles@rist.or.jp Agenda Why do we need many core platforms? Single-thread optimization Parallelization Conclusions Why do we need

More information

File Open, Close, and Flush Performance Issues in HDF5 Scot Breitenfeld John Mainzer Richard Warren 02/19/18

File Open, Close, and Flush Performance Issues in HDF5 Scot Breitenfeld John Mainzer Richard Warren 02/19/18 File Open, Close, and Flush Performance Issues in HDF5 Scot Breitenfeld John Mainzer Richard Warren 02/19/18 1 Introduction Historically, the parallel version of the HDF5 library has suffered from performance

More information

an Object-Based File System for Large-Scale Federated IT Infrastructures

an Object-Based File System for Large-Scale Federated IT Infrastructures an Object-Based File System for Large-Scale Federated IT Infrastructures Jan Stender, Zuse Institute Berlin HPC File Systems: From Cluster To Grid October 3-4, 2007 In this talk... Introduction: Object-based

More information

BigData and Map Reduce VITMAC03

BigData and Map Reduce VITMAC03 BigData and Map Reduce VITMAC03 1 Motivation Process lots of data Google processed about 24 petabytes of data per day in 2009. A single machine cannot serve all the data You need a distributed system to

More information

GPU Computing: Development and Analysis. Part 1. Anton Wijs Muhammad Osama. Marieke Huisman Sebastiaan Joosten

GPU Computing: Development and Analysis. Part 1. Anton Wijs Muhammad Osama. Marieke Huisman Sebastiaan Joosten GPU Computing: Development and Analysis Part 1 Anton Wijs Muhammad Osama Marieke Huisman Sebastiaan Joosten NLeSC GPU Course Rob van Nieuwpoort & Ben van Werkhoven Who are we? Anton Wijs Assistant professor,

More information

Serial. Parallel. CIT 668: System Architecture 2/14/2011. Topics. Serial and Parallel Computation. Parallel Computing

Serial. Parallel. CIT 668: System Architecture 2/14/2011. Topics. Serial and Parallel Computation. Parallel Computing CIT 668: System Architecture Parallel Computing Topics 1. What is Parallel Computing? 2. Why use Parallel Computing? 3. Types of Parallelism 4. Amdahl s Law 5. Flynn s Taxonomy of Parallel Computers 6.

More information

Efficient Object Storage Journaling in a Distributed Parallel File System

Efficient Object Storage Journaling in a Distributed Parallel File System Efficient Object Storage Journaling in a Distributed Parallel File System Presented by Sarp Oral Sarp Oral, Feiyi Wang, David Dillow, Galen Shipman, Ross Miller, and Oleg Drokin FAST 10, Feb 25, 2010 A

More information

Revealing Applications Access Pattern in Collective I/O for Cache Management

Revealing Applications Access Pattern in Collective I/O for Cache Management Revealing Applications Access Pattern in for Yin Lu 1, Yong Chen 1, Rob Latham 2 and Yu Zhuang 1 Presented by Philip Roth 3 1 Department of Computer Science Texas Tech University 2 Mathematics and Computer

More information

Efficiency at Scale. Sanjeev Kumar Director of Engineering, Facebook

Efficiency at Scale. Sanjeev Kumar Director of Engineering, Facebook Efficiency at Scale Sanjeev Kumar Director of Engineering, Facebook International Workshop on Rack-scale Computing, April 2014 Agenda 1 Overview 2 Datacenter Architecture 3 Case Study: Optimizing BLOB

More information