e-infrastructures for Science and Industry

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e-Infrastructures

for Science and Industry

-Clusters, Grids, and Clouds –

Wolfgang Gentzsch, The DEISA Project and OGF

8th _{Int. Conference on Parallel Processing and Applied Mathematics} Wroclaw, Poland, Sep 13 – 16, 2009

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PPAM, Wroclaw, Sept 2009 Wolfgang Gentzsch 2

HPC Centers

• They are service providers

, for past 40 years

• For research, education, and industry

• Computing, storage, apps, data, services

• Very professional

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Grids

1998: The Grid: Blueprint for a

New Computing Infrastructure

2002: The Anatomy of the Grid

Ian Foster, Carl Kesselman, Steve Tuecke Ian Foster, Carl Kesselman

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Grids (Sun in 2001)

Departmental

Grids

Enterprise

Grids

Global

Grids

Clouds

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Public Clouds

• Outside corporate data center • Access over the Internet

• Virtual (Vmware, Xen,...) • Abstraction of the hardware

• Service oriented: SaaS, PaaS, IaaS, HaaS • Variable cost of services (QoS)

• Pay-per-use IT services • Scaling up/down

• IaaS, PaaS, SaaS

• Access

• Elasticity

• Abstraction

• Public, private, hybrid

• Capex => Opex

• Pay-per-use

• Scaling

• and

• we

• have

• all

• the

• components

• available

• today

Clouds

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Benefits of moving HPC to Grids

• Closer collaboration with your colleagues (VCs)

• More resources allow faster/more processing

• Different architectures serve more users

• Failover: move jobs to another system

. . . and Clouds

• No upfront cost for additional resources

• CapEx => OpEx, pay-per-use

• Elasticity, scaling up and down

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The Cloud of Cloud Companies

• Akamai • Areti Internet • Enki • Fortress ITX • Joyent • Layered Technologies • Rackspace • Terremark • Xcalibre • Manjrasoft / Aneka • GridwiseTech / Momentum • NICE/EnginFrame • Amazon • Google • Sun • Salesforce • Microsoft • IBM • Oracle • EMC • Cloudera • Cloudsoft

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NICE EnginFrame

Cluster/Grid/Cloud Portal

Remote, interactive, transparent, secure access to apps & data on corporate Intranet or Internet, or in the Cloud.

Interactive Applications Intranet Clients Win LX UX Mac Intranet Clients Win LX UX Mac Virtualized Data Center Clusters

Users Batch Applications Virtualized Storage Cloud Portal / Gateway Cloud Portal / Gateway Administrators Administrators Users Administrators Administrators Users S ta n d a rd p ro to c o ls S ta n d a rd p ro to c o ls Licenses

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A Scalable Data Cloud Infrastructure

Example:

GridwiseTech Momentum

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ANEKA Cloud Platform

Private Cloud LAN network Amazon Microsoft Google Sun Data Center Virtual Machines

Windows Mac with Mono Linux with Mono

A n e k a C lo u d P la tf o r m IaaS PaaS

SaaS _{Cloud applications}

Social computing, Enterprise, ISV, Scientific, CDNs, ...

Cloud Programming Models & SDK

Task Model Task Model Thread Model Thread Model Map Reduce Model Map Reduce Model Third Party Models Third Party Models

Core Cloud Services

SLA Management SLA Management VM Management VM Management VM Deployment VM Deployment QoS Negotiation QoS Negotiation Job Scheduling Job Scheduling Execution Management Execution Management Pricing

Pricing BillingBilling

Admission Control Admission Control Metering Metering Data Storage Data Storage Monitoring Monitoring Workflow Model Workflow Model Private Cloud LAN network Private Cloud Private Cloud Private Cloud LAN network Amazon Microsoft Google Sun Amazon Microsoft Google Sun Data Center Virtual Machines

Windows Mac with Mono Linux with Mono

Virtual Machines

Windows

Windows Mac with MonoMac with Mono Linux with MonoLinux with Mono

A n e k a C lo u d P la tf o r m IaaS PaaS

SaaS _{Cloud applications}

Social computing, Enterprise, ISV, Scientific, CDNs, ...

Cloud Programming Models & SDK

Task Model Task Model Thread Model Thread Model Map Reduce Model Map Reduce Model Third Party Models Third Party Models

Core Cloud Services

SLA Management SLA Management VM Management VM Management VM Deployment VM Deployment QoS Negotiation QoS Negotiation Job Scheduling Job Scheduling Execution Management Execution Management Pricing

Pricing BillingBilling

Admission Control Admission Control Metering Metering Data Storage Data Storage Monitoring Monitoring Workflow Model Workflow Model Courtesy: Manjrasoft

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Animoto EC2 image usage

Day 1 _{Day 8}

0 4000

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‚My‘ current project:

DEISA: Grid or Cloud ?

Distributed European Infrastructure for Supercomputing Applications

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DEISA1: May 1st, 2004 – April 30th, 2008

DEISA HPC Centers

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PPAM, Wroclaw, Sept 2009 Wolfgang Gentzsch 16 Gateway CSC Gateway ECMWF Gateway FZJ Gateway IDRIS Gateway SARA Gateway LRZ Gateway HPCX Gateway HLRS NJS CINECA IBM P5 IDB UUDB Gateway BSC Gateway CINECA NJS FZJ IBM IDB UUDB NJS RZG IBM IDB UUDB NJS ECMWF IBM P5 IDB UUDB NJS CSC Cray XT4/5 IDB UUDB NJS HPCX Cray XT4 IDB UUDB NJS LRZ SGI ALTIX IDB UUDB NJS HLRS NEC SX8 IDB UUDB CINECA user LRZ user job job NJS SARA IBM IDB UUDB NJS BSC IBM PPC IDB UUDB Gateway RZG NJS IDRIS IBM P6 IDB UUDB AIX LL-MC AIX LL LINUX PBS Pro Super-UX NQS II GridF TP LINUX Maui/Slurm UNICOS/lc PBS Pro LINUX LL AIX LL-MC AIX LL-MC UNICOS/lc PBS Pro AIX LL-MC

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Technologies req ues tssu pp ort Applications Operations off ers pro du ct re_q u es ts co_n fig_u ra tio_n o ffe rs se rv ic_e offers technology requests development

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PPAM, Wroclaw, Sept 2009 Wolfgang Gentzsch 18 DEISA Sites Unified Unified AAA AAA Network Network connectivity connectivity Data Data transfer transfer tools tools Data staging Data staging tools tools Job Job rerouting rerouting Single Single monitor monitor system system Co Co- -reservation reservation and co and co- -allocation allocation Workflow Workflow managemnt managemnt Multiple Multiple ways to ways to access access Common Common production production environmnt environmnt WAN WAN shared shared File system File system Network and AAA layers Job manag. layer and monitor. Presen-tation layer Data manag. layer

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AIX LL-MC AIX LL LINUX PBS Pro Super-UX NQS II GridF TP UNICOS/lc PBS Pro LINUX LL AIX, Linux LL-MC AIX, Linux LL-MC IBM P5

IBM P6 & BlueGene/P

IBM P6 & BlueGene/P IBM P6 Cray XT4/5 Cray XT4 SGI ALTIX NEC SX8 IBM P5+ / P6 IBM PPC

IBM P6 & BlueGene/P

UNICOS/lc

PBS Pro

AIX, Linux

LL-MC

DEISA Global File System

LINUX

Maui/Slurm

Global transparent file system based on the Multi-Cluster General Parallel File System (MC-GPFS of IBM)

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User management in DEISA

• A dedicated LDAP-based distributed repository administers DEISA users

• Trusted LDAP servers are authorized to access each other (based on X.509 certificates) and encrypted communication is used to maintain confidentiality

BSC CINECA CSC ECMWF EPCC FZJ HLRS IDRIS LRZ RZG SARA SARA

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DEISA: Grid or Cloud ?

• Built on top of

proven

, professional infrastructure of HPC centers with expertise in implementation, operation, services.

• Ecosystem of resources, middleware, applications is

respecting

administrative, cultural and political autonomy of partners.

• Globalizing existing HPC services from local to global -according to user requirements: revolution by

evolution

.

• User support: user-friendly access to resources,

porting

user apps onto turnkey architecture.

• After EU funding, DEISA HPC ecosystem will operate in a

sustainable

way, in the interest of the ‘global scientist’, as...

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There are still many

Challenges with Clouds

Sustainable Sustainable Competitive Competitive Advantage Advantage CULTURAL CULTURAL TECHNICAL TECHNICAL LEGAL & LEGAL & REGULATORY REGULATORY

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• Not all applications are cloud-ready or cloud-enabled

• Interoperability of clouds (standards ?)

• Sensitive data, sensitive applications (med.patient records) • Different organizations have different ROI

• Security: end-to-end from your resources to the cloud ! • Current IT culture is not predisposed to sharing resources • “Static” licensing model doesn’t embrace cloud

• Protection of intellectual property • Legal issues (FDA, HIPAA)

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A Cloud Checklist for HPC

When is your HPC app ready for the Cloud ?

_{... no issues with licenses, IP, secrecy, privacy, sensitive}

data and big data movement, legal or regulatory issues, trust, . . .

_{...your app is architecture independent, not optimized for}

specific architecture (single process, loosely-coupled low-level parallel, I/O-robust)

_{...it’s just one app and zillions of parameters} _{...latency and bandwidth are not an issue}

Ideally, your meta-scheduler knows your

requirements and schedules automatically

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Hybrid Grid/Cloud

Resource Management

External Cloud

Resources Department 1

Department 2

Department resource access Campus wide resource demand

Project A Team B Contractor X Project C User 1 User 2 Department 3 Department 4

Define policies according to priorities, budget, and time

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A Closer Look at HPC Load

_{Single parallel job, cpu-intensive, tightly-coupled,}

highly scalable, peta, exa,..

_{Single parallel job, cpu-intensive, weakly-scalable}

_{Capacity computing, throughput, parameter jobs}

_{Managing massive data sets, possibly}

geographically distributed

_{Analysis and visualization of data sets}

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Clouds and supercomputers:

Conventional wisdom?

Too slow

Too

expensive

Clouds/ clusters Super computers Loosely coupled applications Tightly coupled applications Courtesy Ian Foster

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3

Loosely coupled problems

• Ensemble runs to quantify climate model uncertainty

• Identify potential drug targets by screening a database of ligand structures against target proteins

• Study economic model sensitivity to parameters

• Analyze turbulence dataset from many perspectives

• Perform numerical optimization to determine optimal resource assignment in energy problems

• Mine collection of data from advanced light sources

• Construct databases of computed properties of chemical compounds

• Analyze data from the Large Hadron Collider

• Analyze log data from 100,000-node parallel computations

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