inovação e tendências em computação de alto desempenho: a … · 2014-11-24 · hp 17,4% sgi...

© 2014 IBM Corporation

IBM Platform Computing

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Inovação e tendências em "Computação de Alto Desempenho": a visão da IBM

WORKSHOP EM COMPUTAÇÃO CIENTÍFICACENAPAD-SP – 20 ANOS

Marcelo L. BraunsteinLA Platform Computing Sales Exec



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Anniversary Wall

Special thanks to Alan Brenner and Ed Seminaro!



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IBM CENAPAD/FAPESP 2010

• 40 nodes SMP IBM Power 7 (750) with 1280 Processingcores

• 32 cores Power 7(3,55 GHz), 128 GB RAM

• 6 nós GPU Nvidia Tesla• Storage 224 TB

• Performance: 42 TFlops• CPU 36 TFlops• GPU 6 Tflops

Second best in Brasil in 2010 !

Operational sinceDecember 2010



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IBM supercomputing leadership

Source: www.top500.org

Semiannual independent ranking of the top 500

supercomputers in the world

June 2014 TOP500 Aggregate Performance

Dawning0,5%

NUDT13,7%

Cray18,2%

Oracle0,3%

Dell2,5%

Bull2,6%

HP17,4%

SGI4,0%

IBM32,0%

Other8,9%

June 2014 TOP500 EntriesHP leads, followed by IBM

Other; 44Bull ; 16

Cray; 51

Oracle; 2

Dell; 8

HP; 184

SGI; 19

IBM; 176

IBM supercomputing leadership ...�Most installed aggregate throughput with over 88 Petaflops out of 274 Petaflops (32%) (Cray: 50/18%,

HP 46/17%, NUDT 37/14%)

Lead for 30 Lists in a row

�Most in TOP 10 with 4 (#3 LLNL-Sequoia BG/Q, #5 ANL-Mira BG/Q, #8 Juelich–JUQUEEN BG/Q, #9 LLNL-Vulcan BG/Q)

�Most in TOP 20 with 7, most in TOP 100 with 32

�Most 1 Petaflop or greater systems with 12 out of 37

�13 of 20 most energy-efficient systems



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IBM TOP100 Club – June 2014


Indicates new or improved entries

TOP500 Installation Processor Rmax TF/s3 DOE/NNSA/LLNL – Sequoia Blue Gene/Q, 1.6 GHz BQC, 96 racks 17173

5 DOE/SC/Argonne National Lab – Mira Blue Gene/Q, 1.6 GHz BQC, 48 racks 8587

8 Forschungszentrum Juelich (FZJ) - JUQUEEN Blue Gene/Q, 1.6 GHz BQC, 28 racks 5009

9 DOE/NNSA/LLNL – Vulcan Blue Gene/Q, 1.6 GHz BQC, 24 racks 4293

11 Exploration & Production –ENI HPC2 iDataPlex IvyBridge and Nvidia K20x 3003

12 LRZ-SuperMUC iDataPlex 18 islands of 512 nodes Sandy Bridge 2897

17 CINECA-Fermi Blue Gene/Q, 1.6 GHz BQC, 10 racks 1789

22 Power 775 IBM Development Engineering Power 775, 248 drawers 1515

23 Daresbury Laboratory BlueGene/Q, (8 racks) 1431

27 Max-Planck-Gesellschaft MPI/IPP iDataPlex DX360M4, Xeon E5-2680v2 10C 2.8GHz, FDR 1283

29 NCAR (National Center for Atmospheric Research) iDataPlex DX360M4, Xeon E5-2670 8C 2.600GHz, FDR 1258

33 University of Edinburgh BlueGene/Q, Power BQC 16C 1.60GHz, Custom 1073

41 Barcelona Supercomputing Center iDataPlex DX360M4, Xeon E5-2670 8C 2.600GHz, FDR 925

43 Rensselaer Polytechnic Institute BlueGene/Q, Power BQC 16C 1.60GHz, Custom 894

52 Indian Institute of Tropical Meteorology iDataPlex DX360M4, Xeon E5-2670 8C 2.600GHz, FDR 719

53 CNRS/IDRIS-GENCI BlueGene/Q, Power BQC 16C 1.60GHz, Custom 716

54 DOE/SC/Argonne National Laboratory BlueGene/Q, (4 racks) 716

55 EDF R&D BlueGene/Q, Power BQC 16C 1.60GHz 716

56 Swiss National Supercomputing Centre (CSCS) BlueGene/Q, Power BQC 16C 1.600GHz 716

57 Victorian Life Sciences Computation Initiative BlueGene/Q, Power BQC 16C 1.60GHz, Custom 716

58 Max-Planck-Gesellschaft MPI/IPP iDataPlex DX360M4, IVB 10C 2.8GHz, IB , NVIDIA K20x 710

60 ECMWF Power 775, POWER7 8C 3.836GHz 635

61 ECMWF Power 775, POWER7 8C 3.836GHz 635

68 High Energy Accelerator Research Org. /KEK BlueGene/Q, Power BQC 16C 1.600GHz 537

69 High Energy Accelerator Research Org./KEK BlueGene/Q, Power BQC 16C 1.60GHz, 537

78 United Kingdom Meteorological Office Power 775, POWER7 8C 3.836GHz 476

83 Exploration & Production - Eni S.p.A. iDataPlex DX360M4, Xeon E5-2670 8C 2.600GHz, FDR 454

84 Saudi Aramco iDataPlex DX360M4, Xeon E5-2670 8C 2.600GHz, QDR 442

85 China Meteorological Administration IBM Flex System p460, POWER7 8C 3.550GHz, QDR 435

86 China Meteorological Administration IBM Flex System p460, POWER7 8C 3.550GHz, QDR 435

92 NASA Center for Climate Simulation iDataPlex DX360M4, SB 2.8C 2.6GHz, QDR, Xeon Phi 417

98 United Kingdom Meteorological Office Power 775, POWER7 8C 3.836GHz 397



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Rm

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erfo

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TF

lop

s)TOP500 Performance Trend

Even though there is some stepping of the performance of the #1 system,

the relentless growth of the #500 clip level, #10 clip level and Total Aggregate

performance all are fairly straight line trends when plotted on log scale.


274 PF

33.86 PF

3.14 PF

134 TF



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06/23/2014 7

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ce (

TF

lop

s)TOP500 Performance Trend

Over the long haul IBM has demonstrated continued leadership in various

TOP500 metrics, even as the performance continues it’s relentless growth.


Blue Square Markers Indicate IBM Leadership (Most Aggregate; #1 System; Most in TOP10; Most entries on TOP500 list)

IBM has most aggregate performance for last 30 listsIBM has #1 system for 11 out of last 20 lists (14 in total)

IBM has most in Top10 for 19 out of last 22 listsIBM has most systems 19 out of last 31 lists

33.86 PF

3.14 PF

134 TF

274 PF



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Waves of Technology

Back-OfficeComputing

Client-Server PC - 1981

World Wide Web and eBusiness

Confluence of Social, Mobile, Cloud, Big Data / Analytics

90’s80’s60’s We are here



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Nova era de IT

Cloud Social

Analytics Mobile

Security

Garantindo a segurançadas transações

Eficiência e entregade novos produtosde forma rápida.

Manipulando grandesvolumes de dados e gerando insights emtempo real

Revolucionando o compartilhamento de informações.



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Explosion of data

Inflexible IT infrastructures

Escalating IT complexity

How to spot trends, predict outcomes and take meaningful actions?

How to manage inflexible, siloedsystems and business processes to improve business agility?

How to manage IT costs and complexity while speeding time-to-market for new services?

With this focus, IBM is addressing critical IT trends for

Technical Computing users



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Application

Businesses Need to Overcome Infrastructure Limitations To maximize the value of compute and data-intensive applications

ApplicationExamples• Simulation• Analysis• Design• Big data

IT constrained• Long wait times• Low utilization• IT Sprawl

Platform Computing

Application B (Custom, MPI)

Application A (Packaged ISV)

Application C (Big Data)

Today Future

Benefits• High utilization• Throughput• Performance• Prioritization• Reduced cost

Repeated for many

applications and groups

• Clusters• Grid• HPC Cloud

Faster time

to results

Use fewer

resources

Faster time

to results

Use fewer

resources

Faster time

to results

Repeated for many


Faster time

to results

Use fewer

resources

Faster time

to results

Use fewer

resources

Faster time

to results



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Optimization throughout the hardware and software stack from data ingest, thru run-time and output enables architectural tradeoffs to deliver business value

Low

Medium

High

# Cores DAS

External I/OInterconnect

Low

Medium

High

# Cores DAS


Low

Medium

High

# Cores DAS


Low

Medium

High

# Cores DAS


+ =+

Traditional HPC High Velocity Workloads High Volume WorkloadsWorkflow-Optimized Big

Data/Big Workloads

+ =+

Future architecture must handle data-centric locality issues



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Data Centric ComputingMoving Data is Expensive in Time and Energy

� Architecture and Solutions that support HPC (High Performance Computing) and HPA (High Performance Analytics) and Workflows Including Both

� Roadmap to Exascale for Data, Analytics and Modeling

� Putting Processing Near the Memory (in the Memory): Hybrid/Active Memory Cube

Data lives on disk and tapeMove data to CPU as neededDeep Storage Hierarchy

Data lives in persistent memoryMany CPU’’’’s surround and useShallow/Flat Storage Hierarchy

Old Compute-Centric Model New Data-Centric Model

Huge impact on hardware, systems software, and application design !

Flash Phase Change

Manycore FPGA

Massive ParallelismPersistent Memory



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The Complete Data Management Solution for Enterprise Environments

• Enabling a secure, reliable, transparent cloud storage tier in GPFS

• Single namespace for tiering, backup, DR and multi-cluster access• Transparently migrate data to cloud storage

• Allow for more than one tier of cloud storage possibly using multiple provider

Elastic Storage



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Building collaboration and innovation at all levels

Welcoming new members in all areas of the ecosystem

100+ inquiries and numerous active dialogues underway

Boards / Systems

I/O / Storage / Acceleration

Chip / SOC

System / Software / Services

Implementation / HPC / Research



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What are Software Defined Environments (SDE)?

With IBM’s Software Defined Environment,

infrastructure is fully programmable to

rapidly deploy workloads on optimal

resources and to instantly respond to

changing business demands

Software

Defined

Environments

Abstracted and virtualized IT infrastructure resources managed by software

Applications that define infrastructure requirements and configuration

IT infrastructure that extends multiple environments to go beyond the data center

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Why OpenStack for Cloud?

� Provides an Open alternative to proprietary cloud stacks

� Protects clients current investment with simple path to new technology

� Open APIs provides great flexibility and agility



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Platform Computing – one integral solution for all your high performance application middleware requirements

Workload and Resource

Management

Data Management

Infrastructure Management

Platform LSF Family

Batch, MPI workloads with process mgmt, monitoring,

analytics, user portal, license mgmt

Platform HPC

Simplified, integrated HPC management software for

batch, MPI workloads integrated with systems

Platform Symphony Family

High throughput, near ‘real time’ parallel compute and Big Data / MapReduce workloads

Big Data / Hadoop

Simulation / Modeling

AnalyticsApplications

Heterogeneous

Resources

Compute Storage Network

Virtual, Physical, Desktop, Server, Cloud

Platform Cluster Manager Family (inc xCat)Provision and manage Single Cluster to Dynamic Clouds

Elastic Storage (GPFS)High performance, distributed parallel file system

Common Technology Foundation



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Platform LSF & Platform Symphony SaaS

IBM Platform Computing Cloud Service (SaaS)

IBM Platform LSF IBM Platform Symphony

24X7 CloudOps Support

Simulation / Modeling Analytics

IBM Elastic Storage (GPFS)



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Platform Computing Cloud Services

IBM Confidential20



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Customer examplesLeading Formula 1 race team• Complex simulations and analysis with short time frames and

strict regulations on cluster size• Ease-of-use, application license optimization, high IT productivity

Large global financial services provider• Global grid supporting market analysis, pricing and risk analytics/

compliance for 200+ applications• 14 business units sharing grid across 4 data centers

Leader in market driven innovation and science• Global research for HPC, big data and analytics with dynamic

compute and Hadoop clusters• Highly secure HPC cloud shareable across domains

Leading major aircraft manufacturer• Complete aircraft design and simulation across global teams• Reducing time to market with highly automated workflow driven

processes, application license optimization, heterogeneous shared services and optimal job placement

Repeated for many


Faster time

to results

Use fewer

resources

Faster time

to results

Use fewer

resources

Faster time

to results



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Watson is cognitive computing

Understandsnatural

language

Generates and

evaluates hypotheses

Adaptsand learns

Existing technologies fall short of expectations� Business rules have to be created

manually

� System knows only what has been coded

� Manual monitoring and intervention required to update system

Watson is different■You don’t program Watson, you train and teach it

■There is an unlimited capacity to scale Watson’s knowledge

■Watson learns and adapts based on usage over time

© 2014 International Business Machines Corporation

Customers want self-serve solutions now



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MD Anderson Cancer Center Oncology Expert AdvisorTM

Powered by IBM Watson and Power Systems

Patient Evaluation Rx and Management Plan Care Pathway Advisor Patient Driven Research



2525 LTC & Others

Human Systems

Service Systems

IBM Research Brazil: Research Areas

Natural Resources

Good morning.Today we will be talking about an specific set of mathematic rules:

School X –Classroom

Transcription Systems Good

morning.Today we will be talking about an specific set of mathematic rules: trigonometry.

School X –Classroom

Transcription Systems

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Smart Devices



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Marcelo L. BraunsteinLatin America Platform Computing Sales Exec

[email protected]

Cel: (55-21) 99222-3500

For further information, please contact:

inovação e tendências em computação de alto desempenho: a … · 2014-11-24 · hp 17,4% sgi...

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