Transcript Document

Introduction to United Devices
10 July06
UD CONFIDENTIAL
Company
History
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Founded in 1999, headquarters in Austin, Texas with offices in New Jersey, Ohio,
Houston, Chicago, London, Paris, Pune – India
• 5th generation today - 1st generation technology delivered in late 2000
• Focused on large enterprise customers
• 7 of the Top 10 pharmaceuticals worldwide
• Fortune 100 Manufacturing and Telecommunications
• Key Government Agencies
• August 5, 2005 – UD acquires GridXpert Meta-Scheduling technology;
Moves European HQ to Paris
What we do
• Our PC-Grid and DRM solutions are implemented across a wide variety of
industries and applications
• Provide Capacity Management solution to measure & monitor utilization
of your resources.
• Meta-Scheduling across industry legacy DRM solutions in a
heterogeneous environment
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Customer Successes
- Providing Grid Solutions since 1999
- 100+ customers
- Numerous Enterprise-wide grid deployments
Manufacturing
-Crash simulation
-Assembly interaction
- Product Design
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Life Sciences
-Drug discovery
-Clinical development
-Document rendering
- Financial Services
- Business Applications
Government
-Materials modeling
-Decryption
-Bio-terrorism
Education
-General research
-Application development
Other
-Telecom
-Oil & Gas
-Philanthropic grids
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2005 Transaction Profile
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Problem
HPC Evolution
1980s
Supercomputer
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Mini-Supercomputer
1990s
Multi-Processor Servers
2000s
Proprietary Clusters
Commodity Clusters
We are repeating history
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Heterogeneous and massively distributed environment
Reduced user collaboration and consistency
1,400,000
Ad-hoc IT procedures
1,200,000
1,000,000
Independent and peak-based procurement
800,000
Low utilization of resources
600,000
Minimal resource sharing across user groups 400,000
200,000
Skyrocketing TCO
0
2003 2004 2005 2006 2007 2008
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Units Sold
Source: IDC 2005
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Islands of HPC
HPC instance A
Dept A
Project A
Dept C
Project C
HPC instance C
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HPC instance B
Dept B
Project B
Dept D
Project D
 Departmental usage promotes
independent HPC solutions
 A separate installation required
to integrate these instances
 Addition of more nodes requires
manual installation
 Fast depreciating and
expensive resources are mostly
underutilized
 Sub-optimal allocation of jobs
and application to resources
 Relies on a variety of OS
services being available
HPC instance D
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The HPC Grid Computing Model
Dept A
Project A
Dept C
Project C
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Dept B
Project B
Dept D
Project D
 10x reduction in cost – millions
of dollars in savings
 Nearly 100% resource
utilization
 Enterprise-wide use for
appropriate applications
 Preserve departmental control
of policies and access controls
 Single virtual system view for
application developers,
administrators, users
 Asset appreciates over time –
Moore’s Law
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Fundamental Capabilities from UD
Product:
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Security to pass the most stringent
audits
Scalability across heterogeneity
Flexible application framework
Dynamic, policy based provisioning
ROI centric software and business
design decisions
Methodology:
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UD Services that align with grid adoption
curve
Incremental wins with demonstrable ROI
Balances departmental autonomy and
centralized control
HPC Collaboration
Experience:
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Deploying and operating the world’s
largest grids
Distributed computing domain expertise
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Activity Types
• Planning
• Deployment
• Infrastructure
• Application
Grid Adoption
Initial
Engagement
Departmental
Adoption
Business
Value
Effort
Consolidation
Enterprise
Expansion
Future
Innovation
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Where UD Grids Help Today - Industry Applications
Life Sciences
• Proteomics
• Genomics
• High-Throughput
Screening
• Clinical Trials
• Bio-Medical Engineering
• Data Mining
Geosciences
• Reservoir Modeling
• Seismic processing
Financial Services
• Risk Analysis
• Valuation
• Pricing Derivatives
• Commodity Pricing
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Defense & Security
– Decryption
– Language Translation
– Battle simulation
– Intelligence gathering
– Image analysis
– Pattern recognition
– Network warfare
– Vulnerability Analysis
– Steganography
Data Analysis
– ETL
– Statistics
– ERP
– Fraud Detection
Manufacturing
– Finite element analysis
– Computational fluid
dynamics
– Crash test simulations
– Inventory management
Computer-Aided Design
– Verification simulation
– Package Design
– Synthesis
– FPGA design
Multimedia
– Library encoding
– Graphics rendering
– Image compression
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Infrastructure Layer
Management Layer
HPC Management Solutions
Job Setup and Submission
Portal
Workflow
Scheduling
API
HPC Synergy (Meta Scheduling)
Balancing
Licensing
Optimizer
Overflow
HPC Insight
Capacity
Utilization
Forecasting
Chargeback
LSF
LSF
LSF
VC
Clusters
PC Grids
Overflow
Dedicated Nodes
Non-dedicated
Utility
LSF
LSF
PBS
VC
Insight
Data
Warehouse
LSF
LSF
LL
LSF
LSF
SGE
LSF
LSF
Condor
LSF
LSF
CS
LSF
PCLSF
Grid
VC
Firewall
Other Non-dedicated Nodes
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Meta Scheduling Solution – HPC Synergy
• Multi-domain meta-scheduler
• Job & Application Abstraction Layer
• Job Workflow Model
• Collaborative Data Management
• License Management
HPC Synergy (Meta Scheduling)
LSF
LSF
LSF
LSF
LSF
PBS
LSF
LSF
LL
Balancing
Licensing
LSF
LSF
SGE
LSF
LSF
Condor
Free
Distribution
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Optimizer
LSF
LSF
CS
Overflow
LSF
PC LSF
Grid
Open
Source
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Today’s Computing Environments
A complex puzzle for business end users
Resource n
Resource m
Location p
Which login?
Data transfers?
How much does it cost?
User j
Which location?
Location q
Which script syntax?
Which resource?
User i
Location v
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Location u
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Configuring Applications
Users
Jobs
Jobs
Application
Directory
Project
Business
Project
Application
Definition
Application
Services
Services
Services
Application
Services
Services
Application
Services
Application
Resources
Data Space
Platform
LANL
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Data Space
Platform
Platform
Data Space
SNL
LLNL
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HPC Synergy – Job Submission
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HPC Synergy Differentiators
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Heterogeneous – Works with all legacy DRM products:
LSF, PBS, SGE, Condor, ……
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Ease of use - Homogeneous way for end-users to submit
jobs and processes on different computing environments
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Meta-scheduling - Dynamic distribution of jobs over
heterogeneous compute environments and Data transfer &
access optimization
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Virtualization - Resources, applications, data and
associated workflows
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Dynamic management - Add/remove platforms/resources
depending on availability and load, change queuing, etc.
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Monitoring of resources, applications, licenses, and
charge back capabilities
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Virtual Cluster Solution
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GridMP runs underneath LSF, SGE, or PBS
Non-disruptive to the User – still uses legacy DRM scripts
No UD imposed node limit or OS type restrictions
No UD form factor restrictions (servers, workstations, clusters, PC’s)
Access to unlimited capacity – anywhere in the enterprise
Free
Distribution
LSF
LSF
LSF
LSF
LSF
PBS
VC
VC
LSF
LSF
LL
LSF
LSF
SGE
Open
Source
LSF
LSF
Condor
LSF
LSF
CS
DW
VC
Other Non-dedicated Nodes
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Virtual Cluster Overview
Job Submitted
No Impact to Current
User Processes
• Option to use
existing job
submission layer or
United Devices DRM
• Access to extensive
operational data and
analytics
Ethernet
Head Node
• Job Scheduler
• Cluster Mgmt
• United Devices
Resource Manager
and DRM Bridge
Non-Dedicated Nodes
Interconnect
Real-time and
Historical
Analytics
Dedicated Nodes
Utilize Processing Analytics
Drill Down into Operational
Analytics
• Data from grid- and non-grid enabled
computing nodes aggregated for
unified reporting
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Application Processing
Virtual Cluster Speeds Processing and Throughput
• Existing dedicated and non-dedicated nodes employed to accelerate processing and increase
throughput
• Reduced purchasing of new nodes
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Capacity Management Solution
HPC Insight
• Aggregated capacity & utilization from disparate HPC resources
• Unobtrusive access scaling to fully managed
• Can export data to other reporting utilities
• Integrates w/ HPC Synergy to optimize application/resource bindings
• Stand-alone utility or full integration / feedback loop to Synergy
HPC Synergy (Meta Scheduling)
Balancing
Licensing
Optimizer
Overflow
HPC Insight
Capacity
Utilization
Forecasting
Chargeback
LSF
LSF
SGE
LSF
LSF
Condor
LSF
LSF
LSF
LSF
LSF
PBS
LSF
LSF
LL
Free
Distribution
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LSF
LSF
CS
LSF
PC LSF
Grid
Open
Source
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HPC Analytics Solution
ApplicationsUsers
HPC Insight
Professional
Services
ApplicationsUsers
ApplicationsUsers
Heterogeneous
HPC Environments
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Capacity
Optimization Plan
- Inventory
- Capacity
- Utilization
- VP of R&D IT
- VP of HPC Strategy
- Finance
HPC Insight product and professional services
• Step 1: Measure what you have
• Step 2: Understand/Interpret infrastructure and usage information
• Step 3: Create a plan for capacity optimization
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How HPC Insight Works
HPC Insight
Automated Actions
• Collect Data
• Aggregate
• Transform
• Normalize
• Summarize
• Report
Data Collected
Capacity/Utilization
• Users
• Applications
• Jobs
• Clusters and Grids
• Compute Nodes
Data
Warehouse
HPC Insight
Adapter
Native
Artifacts
Users Applications
Grid MP A
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Adapter
Native
Artifacts
Users Applications
Grid MP B
Adapter
Native
Artifacts
Users Applications
Legacy Grid Products
• SunSGE
• Platform LSF
• Altair PBS Pro
• Altair Open PBS
• Condor
• UD Grid MP
Other
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HPC Insight – Capacity Summary
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Drill Down to Detail Reports
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Utilization Summary
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Application Utilization
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Share Data With Other Tools
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MP Insight
A capacity analysis and reporting product providing a
detailed view of Grid MP utilization activity
MP Insight
Users
Users Applications
Users Applications
Workstations
- Grid Performance
- Utilization Details
- Capacity Planning
- IT Managers
- System Administrators
- Application Developers
Servers
Clusters
Grid MP-Managed
Enterprise Grid Environment
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Identify Capacity and Utilization Trends
Inform Grid Performance and Workload Optimization
Provide Highly Detailed Job Accounting and Auditing
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Accounting
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Detailed Grid Usage Chargeback Reports
Track Job Execution Details
Resource Utilization Detail for Compliance Audits
• Capture where jobs ran to document compliant machine usage
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Performance and Workload Optimization
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Review Grid Workload Over Any Time Period
• By Application
• By User
• By Resources
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Review Performance Metrics
• By Application, Job, Device Group
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Drive Grid Utilization
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Review Capacity Reports with IT Managers and Prospective
Users
Review Utilization Reports with IT Managers and User
Prospects
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Capacity - Summary
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United Devices Partnership
A Strategic Contribution to your Success
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Most practical method of providing a centralized HPC service
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Heterogeneous philosophy is unique and an absolute
requirement
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Unprecedented level of visibility and control over the HPC
environment
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Guaranteed certainty that the optimal set of resources are
applied to each request – every time
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Proven methodology to incrementally move towards centralized
HPC management while retaining end-user autonomy
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Testimonials
“If you look at dedicated clusters versus a more flexible [grid] environment where you can take
advantage of both new acquisitions and existing systems through a single tool, it’s just a better
way to invest your money.” – Jeff Mathers, Director of Research Innovation at Johnson &
Johnson PRD, where they saved US$1 million in first year of production
“It will help us avoid other investments in highperformance computing data servers.” Steffen
Neumann, Project Manager for Consulting and
Partnerships DaimlerChrysler Research
“The Grid MP platform keeps track of all the data
related to our job runs – where the job was executed,
what type of machine, how long it took. So not only
does the grid save us time, but in automating this
function it allows us to define a validated process
for job execution. That goes a long way toward
achieving FDA compliance.”
Mark Sale, Global Director of Research Modeling and
Simulation - GlaxoSmithKline
“We invested roughly $400K in grid
software and figure we saved at least
$2 million” – Manuel Peitsch,
Novartis
“[Grid MP] allows us to simulate very high-speed
electronics, without investing in expensive new
hardware. Because the performance is faster
with the grid, we are able to produce even
higher quality results for our clients while
passing on the cost savings this speed of
production allows."
Brian Von Herzen, President - Rapid Prototypes
“We're running between 8000 and 10,000 runs a day – an amount that was impossible before,
without using our supercomputers.” Dr. David Moffet, Associate VP of Research Computing
Purdue University
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