1. 4/17/2017 5:03 AM © 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

2. 4/17/2017 5:03 AM
The Microsoft Perspective On Where High Performance Computing Is Heading
Kyril Faenov
Director of HPC
Windows Server Division
Microsoft Corporation
© 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

3. Talk Outline Market/technology trends Personal supercomputing
Grid computing
Leveraging IT industry investments
Decoupling domain science from Computer Science

4. Top 500 Supercomputer Trends
4/17/2017 5:03 AM
Top 500 Supercomputer Trends
Clusters over 50%
Industry usage rising
x86 is winning
GigE is gaining
© 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

5. <$250K – 97% of systems, 52% of revenue
4/17/2017 5:03 AM
HPC Market Trends
Report of the High-End Computing Revitalization Task Force, 2004
(Office of Science and Technology Policy, Executive Office of the President)
“Make high-end computing easier and more productive to use. Emphasis should be placed on time to solution, the major metric of value to high-end computing users… A common software environment for scientific computation encompassing desktop to high-end systems will enhance productivity gains by promoting ease of use and manageability of systems.”
2004 Systems
CAGR
Capability, Enterprise $1M+
1,167
4.2%
Divisional $250K-$1M
3,915
5.7%
Top Challenges to Implementing Clusters (IDC 2004, N=229)
22,712
Departmental $50-250K
7.7%
System management capability
18%
Apps availability
17%
Parallel algorithm complexity
14%
Space, power, cooling
11%
Interconnect BW/latency
10%
I/O performance 9%
Interconnect complexity
Other
12%
127,802
Workgroup <$50K
13.4%
<$250K – 97% of systems, 52% of revenue
In 2004 clusters grew 96% to 37% by revenue Average cluster size nodes
Source: IDC, 2005
© 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

6. 4/17/2017 5:03 AM
Major Implications
Market pressures demand accelerated innovation cycle, overall cost reduction, and thorough outcome modeling
Leverage volume markets of industry standard hardware and software
Rapid procurement, installation and integration of systems
Workstation-Cluster integrated applications accelerating market growth
Engineering
Bioinformatics
Oil and Gas
Finance
Entertainment
Government/Research
The convergence of affordable high performance hardware and commercial apps is making supercomputing personal
© 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

7. Supercomputing Goes Personal
4/17/2017 5:03 AM
Supercomputing Goes Personal
1991
1998
2005
System
Cray Y-MP C916
Sun HPC10000
NewEgg.com
Architecture
16 x Vector 4GB, Bus
24 x 333MHz Ultra-SPARCII, 24GB, SBus
4 x 2.2GHz x64 4GB, GigE OS
UNICOS
Solaris 2.5.1
Windows Server 2003 SP1
GFlops
~10
Top500 # 1
500
N/A
Price
$40,000,000
$1,000,000 (40x drop)
< $4,000 (250x drop)
Customers
Government Labs
Large Enterprises
Every Engineer & Scientist
Applications
Classified, Climate, Physics Research
Manufacturing, Energy, Finance, Telecom
Bioinformatics, Materials Sciences, Digital Media
© 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

8. The Future Supercomputing on a Chip
IBM Cell processor
256 Gflops today
4 node personal cluster => 1 Tflops
32 node personal cluster => Top100
Microsoft Xbox
3 custom PowerPCs + ATI graphics processor
1 Tflops today
$300
8 node personal cluster => “Top100” for $2500 (ignoring all that you don’t get for $300)
Intel many-core chips
“100’s of cores on a chip in 2015” (Justin Rattner, Intel)
“4 cores”/Tflop => 25 Tflops/chip

9. The Continuing Trend Towards Decentralized, Networked Resources
Grids of personal and
departmental clusters
Personal workstations &
departmental servers
Minicomputers
Mainframes

10. Key To Evolution Tackling system complexity
4/17/2017 5:03 AM
Key To Evolution Tackling system complexity
Scenario
Focus
Departmental Cluster
Conventional scenario
IT owns large clusters due to cost and complexity and allocates resources on per job basis
Users submit batch jobs via scripts
In-house and ISV apps, many based on MPI
Scheduling multiple users’ applications onto scarce compute cycles
Cluster systems administration
Personal/Workgroup Cluster
Emerging scenario
Clusters are pre-packaged OEM appliances, purchased and managed by end-users
Desktop HPC applications transparently and interactively make use of cluster resources
Desktop development tools integration
Interactive applications
Workstation clusters, accelerator appliances
Distributed, policy-based management and security
HPC Application Integration
Future scenario
Multiple simulations and data sources integrated into a seamless application workflow
Network topology and latency awareness for optimal distribution of computation
Structured data storage with rich meta-data
Applications and data potentially span organizational boundaries
Data-centric, “whole-system” workflows
Rapid prototyping of HPC applications
Grids: Distributed application, systems, and data management
Interoperability
IT Mgr
Manual, batch execution
Interactive Computation and Visualization
SQL
© 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

11. “Grid Computing” A catch-all marketing term
“Grid” Computing means many different things to many different people/companies
Desktop cycle-stealing
Managed HPC clusters
Internet access to giant, distributed repositories
Virtualization of data center IT resources
Out-sourcing to “utility data centers” …
Originally this was all called “Distributed Systems”

12. HPC Grids And Web Services
HPC Grid ~ Compute Grid + Data Grid
Compute grid
Forest of clusters and workstations within an organization
Coordinated scheduling of resources
Data grid
Distributed storage facilities within an organization
Coordinated management of data
Web Services
The means to achieve interoperable Internet-scale computing, including federation of organizations
Loosely-coupled, service-oriented architecture

13. Service-Oriented Architectures
Lessons learned
Boundaries are explicit
Services are autonomous and loosely coupled
Services share schema and contract (not classes)
Services establish explicit relationships
Service compatibility is based on policy
Virtual Organizations versus Service-Oriented Architectures
Virtual organizations tend to be tightly-coupled
Commercial organizations want controlled interactions with each other by means of a loosely-coupled SOA

14. Computational Grid Economics*
What $1 will buy you (roughly):
Computers cost $1000 (roughly)
 1 cpu day (~ 10 Tera-ops) == $1
(roughly, assuming 3 yr use cycle)
 10TB network transfer costs == $1
(roughly, assuming 1Gbps interconnect)
Internet bandwidth costs roughly 100 $/mbps/month (not including routers and management)
 1GB network transfer costs == $1 (roughly)
Some observations
HPC cluster communication is 10,000x cheaper than WAN communication
Break-even point for instructions computed per byte transferred:
Cluster: O(1) instrs/byte => many parallel applications are economical to run on a cluster or across a GigE LAN
WAN: O(10,000) instrs/byte => few parallel applications are economical to run across the Internet
*Computational grid economics material courtesy of Jim Gray

15. Exploding Data Sizes Experimental data: TBs  PBs Modeling data Today
10’s to 100’s of GB per simulation is the common case
Applications mostly run in isolation
Tomorrow
10’s to 100’s of TBs, all of it to be archived
Whole-system modeling and multi-application workflows

16. How Do You Move A Terabyte?*
Time/TB
$/TB Sent
$/Mbps
Rent $/month
Speed Mbps
Context
6 years
3,086
1,000 40
0.04
Home phone
5 months
360
117 70
0.6
Home DSL
2 months
2,469
800
1,200
1.5 T1
2 days
2,010
651
28,000 43 T3
14 hours
976
316
49,000
155
OC3
14 minutes
617
200
1,920,000
9600
OC 192
24 hours 50
100
FedEx
LAN Setting
1 day
100
100 Mpbs
2.2 hours
1000
Gbps
13 minutes
10000
10 Gpbs
*Material courtesy of Jim Gray

17. Anticipated HPC Grid Topology
Islands of high connectivity
Simulations done on personal and workgroup clusters
Data stored in data warehouses
Data analysis best done inside the data warehouse
Wide-area data sharing/replication via FedEx?
Personal
cluster
Data warehouse
Workgroup
cluster

18. Data Analysis And Mining
Traditional approach
Keep data in flat files
Write C or Perl programs to compute specific analysis queries
Problems with this approach
Imposes significant development times
Scientists must reinvent DB indexing and query technologies
Have to copy the data from the file system to the compute cluster for every query
Results from the astronomy community
Relational databases can yield speed-ups of one to two orders of magnitude
SQL + application/domain-specific stored procedures greatly simplify creation of analysis queries

19. Is That The End Of The Story?
Personal
cluster
Relational Data warehouse
Workgroup
cluster

20. Distributed systems issues:
Too Much Complexity
2004 NAS supercomputing report: O(35) new computational scientists graduated per year
Parallel application development
Chip-level, node-level, cluster-level, LAN grid-level, WAN grid-level parallelism
OpenMP, MPI, HPF, Global Arrays, …
Component architectures
Performance configuration & tuning
Debugging/profiling/tracing/analysis
Digital experimentation:
Experiment management
Provenance (data & workflows)
Version management (data & workflows)
Domain science
Distributed systems issues:
Security
System management
Directory services
Storage management
Personal
cluster
Relational Data warehouse
Workgroup
cluster

21. (Partial) Solution Leverage IT Industry’s Existing R&D
Parallel applications development
High-productivity IDEs
Integrated debugging/profiling/tracing/analysis
Code designer wizards
Concurrent programming frameworks
Platform optimizations
Dynamic, profile-guided optimization
New programming abstractions
Distributed systems issues
Web Services & HPC grids
Security
Interoperability
Scalability
Dynamic Systems Management
Self (re)configuration & tuning
Reliability & availability
RDMS + data mining
Ease-of-use
Advanced indexing & query processing
Advanced data mining algorithms
Digital experimentation
Collaboration-enhanced Office productivity tools
Structure experiment data and derived results in a manner appropriate for human reading/reasoning (as opposed to optimizing for query processing and/or storage efficiency)
Enable collaboration among colleagues
(Scientific) workflow environments
Automated orchestration
Visual scripting
Provenance

22. Separating The Domain Scientist From The Computer Scientist
Parallel/distributed file systems, relational data warehouses, dynamic systems management, Web Services & HPC grids
Concrete workflow
Concrete concurrency
Abstract concurrency
Computational scientist
Parallel domain application development
Abstract workflow
(Interactive) scientific workflow, integrated with collaboration-enhanced office automation tools
Domain
scientist
Example:
Write scientific paper
(Word)
Record experiment data
(Excel)
Individual experiment run
(Workflow orchestrator)
Share paper with co-authors
(Sharepoint)
Analyze data
(SQL-Server)
Collaborate with co-authors
(NetMeeting)

23. Scientific Information Worker Past and future
Buy lab equipment
Keep lab notebook
Run experiments by hand
Assemble & analyze data (using stat pkg)
Collaborate by phone/ ; Write up results with Latex
Metaphor
Physical experimentation
“Do it yourself”
Lots of disparate systems/pieces
Future
Buy hardware and software
Automatic provenance
Workflow with 3rd party domain packages
Excel and Access/Sql-Server
Office tool suite with collaboration support
Metaphor
Digital experimentation
Turn-key desktop supercomputer
Single integrated system

24. Microsoft Strategy Reducing barriers to adoption for HPC clusters
4/17/2017 5:03 AM
Microsoft Strategy
Reducing barriers to adoption for HPC clusters
Easy to develop
Familiar Windows dev environment + key HPC extensions (MPI, OpenMP, Parallel Debugger)
Best of breed Fortran, numerical libraries, performance analysis tools through partners
Long-term, strategic investments in developer productivity
Easy to use
Familiarity/intuitiveness of Windows
Cluster computing integrated into the workstation applications, user workflow
Easy to manage and own
Integration with AD and the rest of IT infrastructure
Lower TCO through integrated turnkey clusters
Price/performance advantage of industry standard hardware components
Application support in three key HPC verticals
Engagement with the top HPC ISVs
Enabling Open Source applications via University relationships
Leveraging a breadth of standard knowledge-management tools
Web Services, SQL, Sharepoint, Infopath, Excel
Focused Approach to Market
Enabling broad HPC adoption and making HPC into a high volume market
© 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

25. © 2005 Microsoft Corporation. All rights reserved.
4/17/2017 5:03 AM
© 2005 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.
© 2004 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.