Mais conteúdo relacionado Semelhante a Seminario Paolo Maggi, 24-05-2012 (20) Mais de CRS4 Research Center in Sardinia (20) Seminario Paolo Maggi, 24-05-20121. Tecnologie Cloud per la fornitura di applicazioni
ambientali HPC in modalità SaaS
Paolo Maggi <paolo.maggi@nice-software.com>
R&D Manager
Copyright © NICE - 2012
2. Tecnologie Cloud per la fornitura di applicazioni
ambientali HPC in modalità SaaS
Software
High
as Performance
a Computing
Service
Copyright © NICE - 2012
3. What is High Performance Computing?
Refers to the use of supercomputers or clusters of
computers to solve difficult computational problems that
typically arise through scientific inquiry.
Computer users turn to HPC when a problem is too
large to solve on a conventional laptop or workstation or
runs too slowly
– it requires too much memory or disk space
– the algorithm is complex
– the dataset is large
– data access is slow
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4. When we need HPC?
To do a time-consuming operation in less time
– I am an aircraft engineer
– I need to run a simulation to test the stability of the wings at high
speed
– I’d rather have the result in 5 minutes than in 5 days so that I
can complete the aircraft final design sooner
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5. When we need HPC?
To do an operation before a tighter deadline
– I am a weather prediction agency
– I am getting input from weather stations/sensors
– I’d like to make the forecast for tomorrow before tomorrow
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6. When we need HPC?
To do a high number of operations per seconds
– I am an engineer of ACME.com
– My Web server gets 1,000 hits per seconds
– I’d like my web server and my databases to handle 1,000
transactions per seconds so that customers do not experience
bad delays
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7. How to get my answer faster?
Work harder
– Get faster hardware
Work smarter
– Use optimized algorithms (libraries!)
– Write faster code (adapt to match hardware)
– Trade convenience for performance (e.g. compiled program vs.
script program)
Delegate parts of the work
– Parallelize code
– Use accelerators (GPGPU/CUDA)
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9. Leveraging parallelism
Functional parallelism
Different people are performing different tasks at the same time
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10. Leveraging parallelism
Data parallelism
Different people are performing the same task, but on different (equivalent) objects
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11. What a cluster is…
A cluster needs:
– Several computers, nodes, often in special cases for easy
mounting in a rack
– One or more networks (interconnections) to hook the nodes
together
– Software that allows the nodes to communicate with each other
(e.g. MPI)
– Software, called Distributed Resource Manager (DRM), that
manage resources and assign them to individual users and
computational jobs (e.g. LSF, PBS, SGE, …)
A cluster is: all of those components working together to
form one big computer (to give you answers in a fast way)
Copyright © NICE - 2012
14. A working definition of Cloud Computing (NIST)
Cloud computing is a model for enabling convenient,
on-demand network access to a shared pool of
configurable computing resources (e.g., networks,
servers, storage, applications, and services) that can
be rapidly provisioned and released with minimal
management effort or service provider interaction.
This cloud model promotes availability
Composed of
– five essential characteristics
– three service models
– four deployment models
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15. The NIST Cloud definition framework
On Demand Self-Service
Essential
Broad Network Access Rapid Elasticity
Characteristics
Resource Pooling Measured Service
Service Software as a Platform as a Infrastructure as a
Models Service (SaaS) Service (PaaS) Service (IaaS)
Hybrid Clouds
Deployment
Models Private Community
Public Cloud
Cloud Cloud
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17. About NICE
Pioneer in Technical and
Engineering Cloud solutions
14 years experience with enterprise
Grid/Cloud solutions throughout all
industries
Worked closely with ISV and HW vendors
Headquarters: Italy since the beginning
Offices: USA, Germany Strong relationship with Research and
Academia
Core business: Access to Grid / HPC
solutions and Remote Visualization
EnginFrame Grid Portal product line
DCV Remote Visualization technology
Other relevant competencies
Distributed Resource Management
Grid Intelligence
Visualization technology integration
Copyright © NICE - 2012
18. NICE Customers and Market Segments
Energy Aerospace & Manufacturing
Addax Petroleum, AECL, Hess, Bayerngas, AIRBUS, Air Products and Chemicals,
BHP Billiton, Beicip, British Gas, Centrica, Procter&Gamble, SelexGalileo, Goodrich
Chevron, Conoco-Phillips, Dong, Dowell, Aerospace, Hamilton Sunstrand, Kimberly
DSC-Libya, ENI/Agip, GazPromNeft, GDF, Clarke, Magellan Aerospace, Nordam,
Logelco, Maersk Oil, Marathon Oil, Nexen, Northrop Grumman, Raytheon, Thales
Novatek, Papuan Oil, Rosneft,
Schlumberger, Shengli Oil, Sonatrach, Automotive & Industrial Equipment
Statoil, Talisman Energy, TNK-BP, TNNC,
Audi, ARRK, Bridgestone, Bosch, Daimler,
TOTAL, WesternGeco, Xinjiang Oil
Delphi, Dow Chemical, FIAT, Ferrari, Honda,
Hyundai, Jaguar-Land Rover, Lear, Magneti
Life Sciences Marelli, McLaren, P+Z, PSA, Tata Steel,
Bayer, Biolab, DEISA project, Swiss Institute Toyota, TRW, Volkswagen
for Bioinformatics, Partners Healthcare,
Pharsight, M.D. Anderson Cancer Center Research & Education
ASSC, Beihang Uni, CCLRC, CILEA, CNR,
Others CNRS/IN2P3, ENEA, Georgia State Uni,
Accent, Samsung SDI, SensorDynamics, INFN, RMSC, Harvard Uni, Messina Uni,
Bank of Italy, Deutsche Bank Huazhong Normal Uni, Yale Uni
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19. The NICE Grand Vision
Internal
Resources
Administrators Partner
& Managers
Supplier
Customer
Multi-Tenancy &
Collaboration services
HPC Centers,
Public Clouds,
…
User
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20. NICE Products & Solutions
NICE EnginFrame
NICE DCV
NICE EnginFrame Views
Solutions for vertical markets
markets:
Life Science
Oil&Gas
Automotive
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21. NICE Products Overview
Project Mgr End Users Developers Developers End Users End Users
Customer 1 (Corporate network) Customer 2 (VPN) ... Customer N
Service A Service B Service C Service X
(HPC) (3D application) (SaaS)
SaaS)
... (workflow)
workflow)
Service Presentation Layer Access Control
(Web, API, Command-line, ...) AD, LDAP, NIS, …
Vertical solutions
Views DataGate
SLB-RE
HPC …
Desktop Cloud
Visualization
Infrastructure model abstraction
GridML ViewsML CloudML Accounting
driver driver driver & Billing
LSF, Lava, SGE, DCV, VNC, RGS, VMWARE, EC2, RDBMS
PBS, MOAB, … TAW, … MOAB, ISF, …
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22. Our architecture
Access Self-Service Offering Resources
Thin viewer
DCV protocol Linux &
Collaborators,
Windows
Support staff
3D
Visualization sessions
Servers
HTTP(S)
HPC schdulers
End Users
HPC jobs
Command-line
Orchestration &
Provisioning
SOAP
Storage
Developers,
Integrators
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23. User Experience in the Technical Cloud
Enterprise Open Grid
Grid Portal
Desktop
Scavenging
Commercial
HPC Cloud
HPC
Clusters
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24. NICE EnginFrame key features
Lightweight Web front-end for HPC systems
Best in class data management
WebServices API and command-line interface
Controlled, user friendly application submission
Supports all mainstream job schedulers
Workload, resource, license monitoring
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25. Batch job / workflow submission
User friendly,
Application-oriented
Job submission
Hide complexity of
Underlying scheduler
Flexible and efficient
Input file management
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26. Application Data Management
Application data
can be organized
into projects
Application data
can be marked as
starred
Metadata can be
associated to
application data
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28. Data transfers & file management
The file manager component allows to seamless navigate and
access server-side files from the web browser
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29. SOA-enabled job submission
WS-I interface
Java / .NET client library and
command line interface
Simplifies integration with
client-side applications
(optimization, workflow, etc.)
for power-users
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30. What is a 3D Cloud?
"Cloud computing is a model for enabling convenient,
on-demand network access to a shared pool of
configurable computing resources (e.g., networks,
servers, storage, applications, and services) that can be
rapidly provisioned and released with minimal
management effort or service provider interaction.” (NIST)
3D Clouds will enable on-demand network access to
interactive 3D applications (like visualization
applications for scientific data, CAD applications, etc.)
Why NICE customers need a 3D Cloud?
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31. A typical scenario in the CAE computing sector
1 - Simulation submission
Win
2 - Data Transfer to visualize results
Server/Data
Center
3 - Collaboration
Needs Data exchange CAE Workstations
• Can run small to medium serial analysis
• Requires high-end GPU for rendering
Win • May represent IT management
challenges (tens to hundreds of seats)
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32. Problems associated to this model
NETWORK
Network overload leading to poor performance and response times all round
COST
Expensive, dedicated workstations (GPU, memory, …) with short lifecycle
IT MANAGEMENT
Support, update and replace tens to tens of thousands of workstations
WORKSTATION SIZING
Workstations have to be sized for the largest expected models
SECURITY
Moving sensitive data around (in/out organization) is always risky
WORKFORCE
Current models do not support a diverse mobile workforce
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33. Moving technical applications closer to the data
WS PC
HPC job
Collaboration Remote
submission and
management Visualization
Linux
Job submission
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34. Benefits of the HPC Cloud model
NETWORK
Network is no more a bottleneck and data loads faster
COST
Centralized & Shared servers are less expensive to buy & manage
IT MANAGEMENT
Support, update and replacement are more efficient & do not affect users
WORKSTATION SIZING
Resources are dynamically sized based on users needs
SECURITY
Sensitive data remain within protected data center with full access control
WORKFORCE
Users can virtually connect & collaborate from anywhere with any client
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35. Enabling Technologies for the 3D Clouds
Technologies providing secure remote network access to
interactive 3D applications leveraging server-side graphic
hardware acceleration (GPUs)
A software stack that allows the end-user to easily launch and
access remote interactive applications and takes care of managing
and load balancing applications and desktop sessions running
within a Visualization Farm
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36. NICE EnginFrame Views
NICE EnginFrame (Portal and Gateway)
Job Scheduler & Distributed Resource Manager
2D & 3D Applications
VirtualGL RealVNC NICE DCV HP RGS NX, RDP, ...
LINUX
WINDOWS
SOLARIS
Computing Infrastructure
Storage Infrastructure
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37. How Does It Work?
The user connects to EnginFrame to
create a new Session
EnginFrame
Job sch. selects the appropriate
node and starts the session
1
Win
2
3
User gets connected to the session
on the selected node
Job Sch.
64GBytes, Linux
4GB, Win/Lin blades
8GB, Windows
Heterogeneous infrastructure: HW, OS, middle-wares
middle-
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38. Benefits for the End Users
Access to Applications as a Service
– Hide infrastructure details (site, platform, etc…)
– Session allocation can be influenced by memory requirements, data
location affinity and other customer-specific parameters
– Intelligent load balancing of sessions, based on the Job Scheduler
• Memory-aware, Memory reservation, Application license-aware
– Fewer or better optimized data transfers
Collaboration, session sharing
– The session owner can generate a “URL” that can be sent by email /
instant message to invite a collegue to join a given session, without
disclosing the user’s password
Easy management of active session
– Create new sessions with user-specific preferences (resolution, etc...)
– List, reconnect and kill existing sessions
Seamless Access to the sessions and Single Sign-On
– Automation of session-level password create/destroy
• E.g. login via NTLM / ActiveDirectory credentials and map to Linux user
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39. Benefits for the Administrators
Increased level of service provided to users
– Sessions are load balanced by the Job Scheduler to match user needs
– Memory reservation and Data locality scheduling
– Reduce help desk calls
– Exposed services can be personalized per user/group/project
Accounting
– Sessions are jobs, so the resource usage accounting by user, group,
project can be collected through any Analytics tool
Monitoring
– The load and usage of the login farm is monitored via EnginFrame
– Node loading conditions, active sessions
– Administrators can control and manage users’ idle or stuck sessions
Support
– Administrators can connect to user’s sessions to provide support
Security
– Easy integration into identity services, SSO, Enterprise portals
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40. NICE DCV (Desktop Cloud Visualization)
Originated in IBM Research in 2004, acquired by NICE in summer
2010
Provides efficient remote access to graphic-intensive, professional
OpenGL applications
A central (graphics-enabled) server is accessed by remote users
Users only need low-end machines with network connectivity to the
server in order to view and interact with remote application
One or more users (collaborators) can simultaneously access the
server
Is the key technology for delivery of real-time 3D graphics but relies
on other software to provide the collaborative environment
(RealVNC Visualization Edition)
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41. NICE DCV – Main Features
Works on Windows and Linux, leveraging high-end NVIDIA GPUs
Support GPU sharing across multiple users, multiple OS
– First product on the market to allow the sharing of physical GPUs
between multiple Windows VMs while maintaining full OpenGL
acceleration and workstation-class performance
Bandwidth optimized balancing of quality Vs. frame rate
High network latency tolerance
Validated and optimized for Technical Computing applications
Perfectly integrated with NICE EnginFrame for session
management
Copyright © NICE - 2012