Transcript The Microsoft Perspective on Where High Performance Computing

The Microsoft Perspective On
Where High Performance
Computing Is Heading
Kyril Faenov
Director of HPC
Windows Server Division
Microsoft Corporation
Talk Outline
Market/technology trends
Personal supercomputing
Grid computing
Leveraging IT industry investments
Decoupling domain science from
Computer Science
Top 500 Supercomputer Trends
Industry
usage
rising
Clusters
over 50%
GigE is
gaining
x86 is
winning
HPC Market Trends
Report of the High-End Computing
Revitalization Task Force, 2004
(Office of Science and Technology Policy,
Executive Office of the President)
2004 Systems
1,167
3,915
22,712
127,802
“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.”
2004-9 CAGR
Capability,
Enterprise
$1M+
Divisional
$250K-$1M
Departmental
$50-250K
Workgroup
<$50K
4.2%
5.7%
Top Challenges to Implementing Clusters
(IDC 2004, N=229)
7.7%
13.4%
<$250K – 97% of systems, 52% of revenue
In 2004 clusters grew 96% to 37% by revenue
Average cluster size 10-16 nodes
Source: IDC, 2005
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
9%
Other
12%
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
Supercomputing Goes Personal
1991
1998
2005
System
Cray Y-MP C916
Sun HPC10000
Shuttle @ NewEgg.com
Architecture
16 x Vector
4GB, Bus
24 x 333MHz UltraSPARCII, 24GB, SBus
4 x 2.2GHz x64
4GB, GigE
OS
UNICOS
Solaris 2.5.1
Windows Server 2003 SP1
GFlops
~10
~10
~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
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
Key To Evolution
Tackling system complexity
Scenario
Focus
Scheduling multiple users’
applications onto scarce
compute cycles
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
IT Mgr
Manual, batch
execution
Interactive applications
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 Computation
and Visualization
Workstation clusters,
accelerator appliances
Distributed, policy-based
management and security
Data-centric, “wholesystem” workflows
Rapid prototyping of HPC
applications
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 metadata
Applications and data potentially span
organizational boundaries
Cluster systems
administration
SQL
Grids: Distributed
application, systems, and
data management
Interoperability
“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”
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
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
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
How Do You Move A Terabyte?*
Speed
Mbps
Rent
$/month
$/Mbps
$/TB
Sent
Time/TB
Home phone
0.04
40
1,000
3,086
6 years
Home DSL
0.6
70
117
360
5 months
T1
1.5
1,200
800
2,469
2 months
T3
43
28,000
651
2,010
2 days
OC3
155
49,000
316
976
14 hours
OC 192
9600
1,920,000
200
617
14 minutes
FedEx
100
50
24 hours
LAN Setting
100 Mpbs
100
1 day
Gbps
1000
2.2 hours
10 Gpbs
10000
13 minutes
Context
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?
Data warehouse
Workgroup
cluster
Personal
cluster
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
Is That The End Of The Story?
Relational Data
warehouse
Workgroup
cluster
Personal
cluster
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
Domain science
 Debugging/profiling/tracing/analysis
Relational Data
warehouse
Workgroup
cluster
Personal
cluster




Distributed systems
issues:
Security
System management
Directory services
Storage management
Digital experimentation:
 Experiment
management
 Provenance (data &
workflows)
 Version management
(data & workflows)
(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
Separating The Domain Scientist
From The Computer Scientist
Parallel/distributed file systems, relational data warehouses,
dynamic systems management, Web Services & HPC grids
Concrete workflow
Computer
scientist
Concrete concurrency
Abstract concurrency
Computational
scientist
Parallel domain application development
Abstract workflow
(Interactive) scientific workflow, integrated with collaborationenhanced office automation tools
Example:
Write scientific paper
(Word)
Collaborate with co-authors
(NetMeeting)
Domain
scientist
Record experiment data
(Excel)
Individual experiment run
(Workflow orchestrator)
Share paper with co-authors
(Sharepoint)
Analyze data
(SQL-Server)
Scientific Information Worker
Past and future
Past
Buy lab equipment
Keep lab notebook
Run experiments by hand
Assemble & analyze data
(using stat pkg)
Collaborate by phone/email;
Write up results with Latex
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
Physical experimentation
“Do it yourself”
Lots of disparate
systems/pieces
Metaphor
Digital experimentation
Turn-key desktop
supercomputer
Single integrated system
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
© 2005 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.