IME (Infinite Memory Engine) Extreme Application Acceleration & Highly Efficient I/O Provisioning
|
|
- Janice Pope
- 6 years ago
- Views:
Transcription
1 IME (Infinite Memory Engine) Extreme Application Acceleration & Highly Efficient I/O Provisioning September 22 nd 2015 Tommaso Cecchi
2 2 What is IME? This breakthrough, software defined storage application introduces a whole new applicationaware data acceleration tier that provides gamechanging latency reduction and greater bandwidth and IOPS performance for today s and tomorrow s performance hungry scientific, analytic and big data applications.
3 3 What is IME? IME delivers the performance of flash with the manageability & capacity of shared storage IME is a new new tier of transparent, extendable, non-volatile memory (NVM), that provides gamechanging latency reduction and greater bandwidth and IOPS performance for the next generation of performance hungry scientific, analytic and big data applications.
4 4 What is IME? IME creates a new applicationaware fast data tier that resides right between compute and the parallel file system to accelerate I/O, reduce latency and provide greater operational and economic efficiency
5 5 How Does IME Help? Changes the I/O Provisioning Paradigm & Reduces the Total Cost of Storage IME enables organizations to separate the provisioning of peak & sustained performance requirements with greater operational efficiency and cost savings than utilizing exclusively disk-based parallel file systems STORAGE BANDWIDTH UTILIZATION OF A MAJOR HPC PRODUCTION STORAGE SYSTEM 99% of the time < 33% of max 70% of the time< 5% of max IME Reduces Storage Hardware up to 70% Fewer systems to buy, power manage, maintain
6 6 How Does IME Help? Limitless Performance Scaling Removes Architectural & Economic & Barriers IME makes exascale I/O a reality, and finally enables the enterprise to run HPC jobs with much greater performance and efficiency IME Eliminates: Parallel file system locking, limitations & bottlenecks 70% of storage hardware, consumed floorspace Latency driving a 30% loss of compute resources 90% of checkpoint/restart downtime
7 7 Why Cache Matters in HPC Even Large HPC Sites Drive a Lot of Small I/O Cache is critical in aligning all-too-frequent unaligned writes and capturing small writes to preserve spinning disk performance All DDN Storage products offers cache mirroring & battery-backed RAM cache - proven across 3 generations to accelerate all varieties of data Many systems today do not even offer a protected, redundant write cache. Caching is one of the most difficult layers of a storage stack to engineer, it s also the most critical 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
8 8 Where IME Provides Value IME Accelerates Parallel Filesystems Absorbs all sizes of I/O at full performance, unlike Lustre* and GPFS 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
9 9 Where IME Provides Value 1. MITIGATES POOR PFS PERFORMANCE caused by PFS locking, small I/O, and mal-aligned, fragmented I/O patterns. IME makes bad apps run well and also prevents a poor-behaving app from impacting the entire supercomputer. This is especially valuable to diverse workload environments and ISV applications. IOR benchmarks indicate a 3x 20x speedup on I/Os <32KB. 25 MB/s S3D Turbulent Flow Model 50 GB/s 4 GB/s 2) PROVIDES HIGHER PERFORMANCE I/O (bandwidth and latency) to the application. At ISC14, we demonstrated three orders of magnitude speed-up due to this high performance tier 3) IME DRIVES SIGNIFICANTLY MORE EFFICIENT I/O TO THE PFS by re-aligning and coalescing data within the non-volatile storage. At ISC14, we demonstrated two orders of magnitude speed-up due to this efficiency 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
10 10 IME Lowers the Total Cost of Storage IME+PFS delivers better price/performance over PFS alone Cluster Memory = 400TB Qty: 12 IME Appliances NVM Capacity = 2.75X Cluster Memory (Each w/ Qty: TB NVMe SSDs) Components SFA Only IME + SFA Advantages Cluster I/O BW 540 GB/s 756 GB/s 216 GB/s More BW Delivered Storage Fabric BW 540 GB/s 270 GB/s 50% Less BW Needed to PFS Qty: OSS % Less OSS to Buy Qty: SFA Appliances % Less SFA Appliances Needed Qty HDDs/SFA QTY: HDDs IME Value Proposition 400 (80 HDD * 5 Enclos) 5,600 (14 SFA *400 HDD) 800 (80 HDD * 10 Enclos) 5,600 (7 SFA *800 HDD) More bandwidth to the cluster (Faster job turn-around, more jobs in same period, fewer nodes needed to complete same amount of work) 200% More HDD Density per SFA Appliance Delivering the Same Capacity Fewer OSS and SFAs Reduced power, space and operational cost Similar persistent capacity Lower overall capital cost 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
11 11 HPC Ecosystem Client IO Interfaces Application IO Implementation High-level IO Libraries (optional) MPI-IO Native IO POSIX IO Data path for HL IO library built on POSIX Forwarded + Exported IO (optional) File System IO Interface (VFS, User Space Library) 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
12 12 High-Level IO Libraries Provides an application and end-user oriented IO interface Files / directories abstracted from users in favor of data sets / objects / containers / variables Object operations (put, get) instead of byte streams (read, write) Portable, self-describing data sets Example High-Level IO Libraries HDF5 ( netcdf ( PnetCDF ( ADIOS ( Implementations leverage lower-level IO interfaces POSIX MPI-IO 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
13 13 MPI-IO Provides a high-performance parallel IO interface and semantics Applies successful MPI capabilities to file IO Bulk data capabilities (MPI_File_write_at_all) Metadata capabilities (e.g. scalable file open() ) Most popular implementation is Argonne National Laboratory s ROMIO Distributed in MPICH Available in MPICH derivatives (MVAPICH, IBM MPI, Intel MPI, Cray MPI, and others) Key Features: Independent IO: Uncoordinated parallel IO from many concurrent readers and writers Collective IO: Coordinated IO from many readers and writers. Two popular implementations o Data Sieving Selective filtering of data (reduces IOPs) o Two-phase IO Intermediate processes collect and serve data to other processes (reduces number of readers-writers touching PFS) MPI Derived Data Type support: Allow MPI runtime to load non-contiguous data in files directly into application data structures in RAM o Used heavily by higher-level IO libraries (e.g. PnetCDF and HDF5) Specialization for storage system targets (ROMIO ADIO drivers) o IME provides an ADIO driver that translates MPI-IO requests into IME requests o ROMIO provides drivers for Lustre, GPFS, PanFS, Further Reading Chapter 13 of the MPI3 Standard DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
14 14 POSIX IO Provides a portable byte-stream IO interface read(), write(), open(), close(), POSIX IO Pros Portable Inertia POSIX IO Cons Some design assumptions no longer true for modern computers (concurrency and parallelism) Lots of state at runtime (file descriptors) Further Reading POSIX standard (POSIX ) 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
15 15 DDN IME Ecosystem Client IO Interfaces Application IO Implementation High-level IO Libraries (optional) MPI-IO MPI-IO (IME) (POSIX) POSIX (IME FUSE) IME Native Client Library Data path for HL IO library built on POSIX 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
16 16 DDN IME Ecosystem Client IO Interfaces Three primary interfaces for IME IME FUSE o Provides POSIX IO o Captures IO requests through the Linux VFS o Target Use Case: General purpose applications that use POSIX IME ROMIO o Provides MPI-IO support o Captures IO requests through the MPI runtime in user space o Target Use Case: Parallel applications IME Native Library o Low-level programming interface o FUSE and ROMIO layers implemented on this interface o Target Use Case: Highly-optimized customer applications that may not map cleanly onto POSIX or MPI-IO 2014 DataDirect Networks, Inc. * Other names and brands may be claimed as the property of others. Any statements or representations around future events are subject to change. ddn.com
17 IME Internal Architecture Overview
18 18 Aggregate IME Adaptive vs. Non-Adaptive WRITE Performance Ideal, healthy system One degraded IME server, Adaptive Amdahl s Law in action! One degraded IME server, Non-adaptive
19 19 Real-Time IME Adaptive vs. Non-adaptive WRITE Performance Adaptive heuristic learns quickly 4x Performance Lost with Non-adaptive
20 20 Use of Log Structuring in IME What does this give us? Near line rate performance regardless of output pattern.
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 informationLeveraging Software-Defined Storage to Meet Today and Tomorrow s Infrastructure Demands
Leveraging Software-Defined Storage to Meet Today and Tomorrow s Infrastructure Demands Unleash Your Data Center s Hidden Power September 16, 2014 Molly Rector CMO, EVP Product Management & WW Marketing
More informationIME Infinite Memory Engine Technical Overview
1 1 IME Infinite Memory Engine Technical Overview 2 Bandwidth, IOPs single NVMe drive 3 What does Flash mean for Storage? It's a new fundamental device for storing bits. We must treat it different from
More informationHPC Storage Use Cases & Future Trends
Oct, 2014 HPC Storage Use Cases & Future Trends Massively-Scalable Platforms and Solutions Engineered for the Big Data and Cloud Era Atul Vidwansa Email: atul@ DDN About Us DDN is a Leader in Massively
More informationStore 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 informationDDN. DDN Updates. Data DirectNeworks Japan, Inc Shuichi Ihara. DDN Storage 2017 DDN Storage
DDN DDN Updates Data DirectNeworks Japan, Inc Shuichi Ihara DDN A Broad Range of Technologies to Best Address Your Needs Protection Security Data Distribution and Lifecycle Management Open Monitoring Your
More informationUsing DDN IME for Harmonie
Irish Centre for High-End Computing Using DDN IME for Harmonie Gilles Civario, Marco Grossi, Alastair McKinstry, Ruairi Short, Nix McDonnell April 2016 DDN IME: Infinite Memory Engine IME: Major Features
More information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 informationDDN About Us Solving Large Enterprise and Web Scale Challenges
1 DDN About Us Solving Large Enterprise and Web Scale Challenges History Founded in 98 World s Largest Private Storage Company Growing, Profitable, Self Funded Headquarters: Santa Clara and Chatsworth,
More informationApplying DDN to Machine Learning
Applying DDN to Machine Learning Jean-Thomas Acquaviva jacquaviva@ddn.com Learning from What? Multivariate data Image data Facial recognition Action recognition Object detection and recognition Handwriting
More informationDDN. DDN Updates. DataDirect Neworks Japan, Inc Nobu Hashizume. DDN Storage 2018 DDN Storage 1
1 DDN DDN Updates DataDirect Neworks Japan, Inc Nobu Hashizume DDN Storage 2018 DDN Storage 1 2 DDN A Broad Range of Technologies to Best Address Your Needs Your Use Cases Research Big Data Enterprise
More informationThe 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 informationAnalyzing 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 informationDDN and Flash GRIDScaler, Flashscale Infinite Memory Engine
1! DDN and Flash GRIDScaler, Flashscale Infinite Memory Engine T. Cecchi - September 21 st 2016 HPC Advisory Council 2! DDN END-TO-END DATA LIFECYCLE MANAGEMENT BURST & COMPUTE SSD, DISK & FILE SYSTEM
More informationApplication 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 informationI/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 informationIntroduction 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 informationInfinite 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 informationAnalyzing 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 informationIntroduction 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 informationDELL 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 informationWrite 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 informationSFA12KX and Lustre Update
Sep 2014 SFA12KX and Lustre Update Maria Perez Gutierrez HPC Specialist HPC Advisory Council Agenda SFA12KX Features update Partial Rebuilds QoS on reads Lustre metadata performance update 2 SFA12KX Features
More informationNVMFS: A New File System Designed Specifically to Take Advantage of Nonvolatile Memory
NVMFS: A New File System Designed Specifically to Take Advantage of Nonvolatile Memory Dhananjoy Das, Sr. Systems Architect SanDisk Corp. 1 Agenda: Applications are KING! Storage landscape (Flash / NVM)
More informationA 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 informationCA485 Ray Walshe Google File System
Google File System Overview Google File System is scalable, distributed file system on inexpensive commodity hardware that provides: Fault Tolerance File system runs on hundreds or thousands of storage
More informationEarly Evaluation of the "Infinite Memory Engine" Burst Buffer Solution
Early Evaluation of the "Infinite Memory Engine" Burst Buffer Solution Wolfram Schenck Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Bielefeld, Germany Salem El Sayed,
More informationpnfs, 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 informationDDN s Vision for the Future of Lustre LUG2015 Robert Triendl
DDN s Vision for the Future of Lustre LUG2015 Robert Triendl 3 Topics 1. The Changing Markets for Lustre 2. A Vision for Lustre that isn t Exascale 3. Building Lustre for the Future 4. Peak vs. Operational
More informationNext-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 informationECE7995 (7) Parallel I/O
ECE7995 (7) Parallel I/O 1 Parallel I/O From user s perspective: Multiple processes or threads of a parallel program accessing data concurrently from a common file From system perspective: - Files striped
More informationImproving I/O Bandwidth With Cray DVS Client-Side Caching
Improving I/O Bandwidth With Cray DVS Client-Side Caching Bryce Hicks Cray Inc. Bloomington, MN USA bryceh@cray.com Abstract Cray s Data Virtualization Service, DVS, is an I/O forwarder providing access
More informationHDF5 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 informationLustre* is designed to achieve the maximum performance and scalability for POSIX applications that need outstanding streamed I/O.
Reference Architecture Designing High-Performance Storage Tiers Designing High-Performance Storage Tiers Intel Enterprise Edition for Lustre* software and Intel Non-Volatile Memory Express (NVMe) Storage
More informationAn Evolutionary Path to Object Storage Access
An Evolutionary Path to Object Storage Access David Goodell +, Seong Jo (Shawn) Kim*, Robert Latham +, Mahmut Kandemir*, and Robert Ross + *Pennsylvania State University + Argonne National Laboratory Outline
More informationAccelerating Real-Time Big Data. Breaking the limitations of captive NVMe storage
Accelerating Real-Time Big Data Breaking the limitations of captive NVMe storage 18M IOPs in 2u Agenda Everything related to storage is changing! The 3rd Platform NVM Express architected for solid state
More informationFeedback on BeeGFS. A Parallel File System for High Performance Computing
Feedback on BeeGFS A Parallel File System for High Performance Computing Philippe Dos Santos et Georges Raseev FR 2764 Fédération de Recherche LUmière MATière December 13 2016 LOGO CNRS LOGO IO December
More informationLeveraging Burst Buffer Coordination to Prevent I/O Interference
Leveraging Burst Buffer Coordination to Prevent I/O Interference Anthony Kougkas akougkas@hawk.iit.edu Matthieu Dorier, Rob Latham, Rob Ross, Xian-He Sun Wednesday, October 26th Baltimore, USA Outline
More informationAn 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 informationIsilon 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 informationQuobyte The Data Center File System QUOBYTE INC.
Quobyte The Data Center File System QUOBYTE INC. The Quobyte Data Center File System All Workloads Consolidate all application silos into a unified highperformance file, block, and object storage (POSIX
More informationHewlett Packard Enterprise HPE GEN10 PERSISTENT MEMORY PERFORMANCE THROUGH PERSISTENCE
Hewlett Packard Enterprise HPE GEN10 PERSISTENT MEMORY PERFORMANCE THROUGH PERSISTENCE Digital transformation is taking place in businesses of all sizes Big Data and Analytics Mobility Internet of Things
More informationFlashGrid Software Enables Converged and Hyper-Converged Appliances for Oracle* RAC
white paper FlashGrid Software Intel SSD DC P3700/P3600/P3500 Topic: Hyper-converged Database/Storage FlashGrid Software Enables Converged and Hyper-Converged Appliances for Oracle* RAC Abstract FlashGrid
More informationStructuring 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 informationDatabase Architecture 2 & Storage. Instructor: Matei Zaharia cs245.stanford.edu
Database Architecture 2 & Storage Instructor: Matei Zaharia cs245.stanford.edu Summary from Last Time System R mostly matched the architecture of a modern RDBMS» SQL» Many storage & access methods» Cost-based
More informationIncreasing Performance of Existing Oracle RAC up to 10X
Increasing Performance of Existing Oracle RAC up to 10X Prasad Pammidimukkala www.gridironsystems.com 1 The Problem Data can be both Big and Fast Processing large datasets creates high bandwidth demand
More informationTuning I/O Performance for Data Intensive Computing. Nicholas J. Wright. lbl.gov
Tuning I/O Performance for Data Intensive Computing. Nicholas J. Wright njwright @ lbl.gov NERSC- National Energy Research Scientific Computing Center Mission: Accelerate the pace of scientific discovery
More informationBIG DATA AND HADOOP ON THE ZFS STORAGE APPLIANCE
BIG DATA AND HADOOP ON THE ZFS STORAGE APPLIANCE BRETT WENINGER, MANAGING DIRECTOR 10/21/2014 ADURANT APPROACH TO BIG DATA Align to Un/Semi-structured Data Instead of Big Scale out will become Big Greatest
More informationDesigning elastic storage architectures leveraging distributed NVMe. Your network becomes your storage!
Designing elastic storage architectures leveraging distributed NVMe Your network becomes your storage! Your hosts from Excelero 2 Yaniv Romem CTO & Co-founder Josh Goldenhar Vice President Product Management
More informationIBM 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 informationData 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 informationIntel Enterprise Edition Lustre (IEEL-2.3) [DNE-1 enabled] on Dell MD Storage
Intel Enterprise Edition Lustre (IEEL-2.3) [DNE-1 enabled] on Dell MD Storage Evaluation of Lustre File System software enhancements for improved Metadata performance Wojciech Turek, Paul Calleja,John
More informationEnosis: 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 informationI/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 informationThe Leading Parallel Cluster File System
The Leading Parallel Cluster File System www.thinkparq.com www.beegfs.io ABOUT BEEGFS What is BeeGFS BeeGFS (formerly FhGFS) is the leading parallel cluster file system, developed with a strong focus on
More informationParallel File Systems. John White Lawrence Berkeley National Lab
Parallel File Systems John White Lawrence Berkeley National Lab Topics Defining a File System Our Specific Case for File Systems Parallel File Systems A Survey of Current Parallel File Systems Implementation
More informationSGI Overview. HPC User Forum Dearborn, Michigan September 17 th, 2012
SGI Overview HPC User Forum Dearborn, Michigan September 17 th, 2012 SGI Market Strategy HPC Commercial Scientific Modeling & Simulation Big Data Hadoop In-memory Analytics Archive Cloud Public Private
More informationExperiences with the Parallel Virtual File System (PVFS) in Linux Clusters
Experiences with the Parallel Virtual File System (PVFS) in Linux Clusters Kent Milfeld, Avijit Purkayastha, Chona Guiang Texas Advanced Computing Center The University of Texas Austin, Texas USA Abstract
More informationData 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 informationAutomatic 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 informationRAIDIX 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 informationPowerVault MD3 SSD Cache Overview
PowerVault MD3 SSD Cache Overview A Dell Technical White Paper Dell Storage Engineering October 2015 A Dell Technical White Paper TECHNICAL INACCURACIES. THE CONTENT IS PROVIDED AS IS, WITHOUT EXPRESS
More informationParallel 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 informationCRFS: A Lightweight User-Level Filesystem for Generic Checkpoint/Restart
CRFS: A Lightweight User-Level Filesystem for Generic Checkpoint/Restart Xiangyong Ouyang, Raghunath Rajachandrasekar, Xavier Besseron, Hao Wang, Jian Huang, Dhabaleswar K. Panda Department of Computer
More informationNEXTGenIO 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 informationInnovator, Disruptor or Laggard, Where will your storage applications live? Next generation storage
Innovator, Disruptor or Laggard, Where will your storage applications live? Next generation storage Bev Crair, Vice President and General Manager, Storage Group Intel The world is changing Information
More informationlibhio: 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 informationScaling Internet TV Content Delivery ALEX GUTARIN DIRECTOR OF ENGINEERING, NETFLIX
Scaling Internet TV Content Delivery ALEX GUTARIN DIRECTOR OF ENGINEERING, NETFLIX Inventing Internet TV Available in more than 190 countries 104+ million subscribers Lots of Streaming == Lots of Traffic
More informationSoftware Defined Storage at the Speed of Flash. PRESENTATION TITLE GOES HERE Carlos Carrero Rajagopal Vaideeswaran Symantec
Software Defined Storage at the Speed of Flash PRESENTATION TITLE GOES HERE Carlos Carrero Rajagopal Vaideeswaran Symantec Agenda Introduction Software Technology Architecture Review Oracle Configuration
More informationBrent 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 informationCS6453. Data-Intensive Systems: Rachit Agarwal. Technology trends, Emerging challenges & opportuni=es
CS6453 Data-Intensive Systems: Technology trends, Emerging challenges & opportuni=es Rachit Agarwal Slides based on: many many discussions with Ion Stoica, his class, and many industry folks Servers Typical
More informationLustre Parallel Filesystem Best Practices
Lustre Parallel Filesystem Best Practices George Markomanolis Computational Scientist KAUST Supercomputing Laboratory georgios.markomanolis@kaust.edu.sa 7 November 2017 Outline Introduction to Parallel
More informationHarmonia: An Interference-Aware Dynamic I/O Scheduler for Shared Non-Volatile Burst Buffers
I/O Harmonia Harmonia: An Interference-Aware Dynamic I/O Scheduler for Shared Non-Volatile Burst Buffers Cluster 18 Belfast, UK September 12 th, 2018 Anthony Kougkas, Hariharan Devarajan, Xian-He Sun,
More informationUK 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 informationA New Key-Value Data Store For Heterogeneous Storage Architecture
A New Key-Value Data Store For Heterogeneous Storage Architecture brien.porter@intel.com wanyuan.yang@intel.com yuan.zhou@intel.com jian.zhang@intel.com Intel APAC R&D Ltd. 1 Agenda Introduction Background
More informationIBM 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 informationSystem 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 informationIBM FlashSystem. IBM FLiP Tool Wie viel schneller kann Ihr IBM i Power Server mit IBM FlashSystem 900 / V9000 Storage sein?
FlashSystem Family 2015 IBM FlashSystem IBM FLiP Tool Wie viel schneller kann Ihr IBM i Power Server mit IBM FlashSystem 900 / V9000 Storage sein? PiRT - Power i Round Table 17 Sep. 2015 Daniel Gysin IBM
More informationToward a Memory-centric Architecture
Toward a Memory-centric Architecture Martin Fink EVP & Chief Technology Officer Western Digital Corporation August 8, 2017 1 SAFE HARBOR DISCLAIMERS Forward-Looking Statements This presentation contains
More informationFast 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 informationI/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 informationAerospike Scales with Google Cloud Platform
Aerospike Scales with Google Cloud Platform PERFORMANCE TEST SHOW AEROSPIKE SCALES ON GOOGLE CLOUD Aerospike is an In-Memory NoSQL database and a fast Key Value Store commonly used for caching and by real-time
More informationThe Fastest And Most Efficient Block Storage Software (SDS)
The Fastest And Most Efficient Block Storage Software (SDS) StorPool: Product Summary 1. Advanced Block-level Software Defined Storage, SDS (SDS 2.0) Fully distributed, scale-out, online changes of everything,
More informationA Talari Networks White Paper. Turbo Charging WAN Optimization with WAN Virtualization. A Talari White Paper
A Talari Networks White Paper Turbo Charging WAN Optimization with WAN Virtualization A Talari White Paper Turbo Charging WAN Optimization with WAN Virtualization 2 Introduction WAN Virtualization is revolutionizing
More informationDeep Learning Performance and Cost Evaluation
Micron 5210 ION Quad-Level Cell (QLC) SSDs vs 7200 RPM HDDs in Centralized NAS Storage Repositories A Technical White Paper Rene Meyer, Ph.D. AMAX Corporation Publish date: October 25, 2018 Abstract Introduction
More informationThe Oracle Database Appliance I/O and Performance Architecture
Simple Reliable Affordable The Oracle Database Appliance I/O and Performance Architecture Tammy Bednar, Sr. Principal Product Manager, ODA 1 Copyright 2012, Oracle and/or its affiliates. All rights reserved.
More informationExtreme 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 informationNFS, GPFS, PVFS, Lustre Batch-scheduled systems: Clusters, Grids, and Supercomputers Programming paradigm: HPC, MTC, and HTC
Segregated storage and compute NFS, GPFS, PVFS, Lustre Batch-scheduled systems: Clusters, Grids, and Supercomputers Programming paradigm: HPC, MTC, and HTC Co-located storage and compute HDFS, GFS Data
More information2012 HPC Advisory Council
Q1 2012 2012 HPC Advisory Council DDN Big Data & InfiniBand Storage Solutions Overview Toine Beckers Director of HPC Sales, EMEA The Global Big & Fast Data Leader DDN delivers highly scalable & highly-efficient
More informationFast and Easy Persistent Storage for Docker* Containers with Storidge and Intel
Solution brief Intel Storage Builders Storidge ContainerIO TM Intel Xeon Processor Scalable Family Intel SSD DC Family for PCIe*/NVMe Fast and Easy Persistent Storage for Docker* Containers with Storidge
More informationFunctional Partitioning to Optimize End-to-End Performance on Many-core Architectures
Functional Partitioning to Optimize End-to-End Performance on Many-core Architectures Min Li, Sudharshan S. Vazhkudai, Ali R. Butt, Fei Meng, Xiaosong Ma, Youngjae Kim,Christian Engelmann, and Galen Shipman
More informationLustreFS and its ongoing Evolution for High Performance Computing and Data Analysis Solutions
LustreFS and its ongoing Evolution for High Performance Computing and Data Analysis Solutions Roger Goff Senior Product Manager DataDirect Networks, Inc. What is Lustre? Parallel/shared file system for
More informationExtremely Fast Distributed Storage for Cloud Service Providers
Solution brief Intel Storage Builders StorPool Storage Intel SSD DC S3510 Series Intel Xeon Processor E3 and E5 Families Intel Ethernet Converged Network Adapter X710 Family Extremely Fast Distributed
More informationFLASHARRAY//M Business and IT Transformation in 3U
FLASHARRAY//M Business and IT Transformation in 3U TRANSFORM IT Who knew that moving to all-flash storage could help reduce the cost of IT? FlashArray//m makes server and workload investments more productive,
More informationGuidelines for Efficient Parallel I/O on the Cray XT3/XT4
Guidelines for Efficient Parallel I/O on the Cray XT3/XT4 Jeff Larkin, Cray Inc. and Mark Fahey, Oak Ridge National Laboratory ABSTRACT: This paper will present an overview of I/O methods on Cray XT3/XT4
More informationNetApp: Solving I/O Challenges. Jeff Baxter February 2013
NetApp: Solving I/O Challenges Jeff Baxter February 2013 1 High Performance Computing Challenges Computing Centers Challenge of New Science Performance Efficiency directly impacts achievable science Power
More informationTaming Parallel I/O Complexity with Auto-Tuning
Taming Parallel I/O Complexity with Auto-Tuning Babak Behzad 1, Huong Vu Thanh Luu 1, Joseph Huchette 2, Surendra Byna 3, Prabhat 3, Ruth Aydt 4, Quincey Koziol 4, Marc Snir 1,5 1 University of Illinois
More informationLustre A Platform for Intelligent Scale-Out Storage
Lustre A Platform for Intelligent Scale-Out Storage Rumi Zahir, rumi. May 2003 rumi.zahir@intel.com Agenda Problem Statement Trends & Current Data Center Storage Architectures The Lustre File System Project
More informationHigh-Performance Lustre with Maximum Data Assurance
High-Performance Lustre with Maximum Data Assurance Silicon Graphics International Corp. 900 North McCarthy Blvd. Milpitas, CA 95035 Disclaimer and Copyright Notice The information presented here is meant
More informationSmall File I/O Performance in Lustre. Mikhail Pershin, Joe Gmitter Intel HPDD April 2018
Small File I/O Performance in Lustre Mikhail Pershin, Joe Gmitter Intel HPDD April 2018 Overview Small File I/O Concerns Data on MDT (DoM) Feature Overview DoM Use Cases DoM Performance Results Small File
More information