Future of Scientific Computing
Marvin Theimer
Software Architect
Windows Server High Performance Computing Group
Microsoft Corporation
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
Molecular Biologist’s Workstation
High-end workstation with internal
cluster nodes
8 Opteron, 20 Gflops workstation/cluster
for O($10,000)
Turn-key system purchased from a
standard OEM
Pre-installed set of bioinformatics
applications
Run interactive workstation
applications that offload
computationally intensive tasks to
attached cluster nodes
Run workflows consisting of
visualization and analysis programs
that process the outputs of
simulations running on attached
cluster nodes
The Future: Supercomputing on a Chip
IBM Cell processor
256 Gflops today
4 node personal cluster => 1 Tflops
32 node personal cluster => Top100
Intel many-core chips
“100’s of cores on a chip in 2015” (Justin
Rattner, Intel)
“4 cores”/Tflop => 25 Tflops/chip
The Continuing Trend Towards Decentralized,
Dedicated Resources
Grids of personal &
departmental clusters
Personal workstations &
departmental servers
Minicomputers
Mainframes
The Evolving Nature of HPC
Scenario
Focus
Scheduling multiple users’
applications onto scarce
compute cycles
Departmental Cluster
Conventional scenario
IT owns large clusters due to 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
Cluster systems
administration
Manual, batch
execution
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
Interactive Computation
and Visualization
Compute grids: distributed
systems management
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, “wholesystem” workflows
SQL
Data grids: distributed data
management
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
*Material courtesy of Jim Gray
Anticipated HPC Grid Topology
Islands of high connectivity
Simulations done on personal &
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
Debugging/profiling/tracing/analysis
Domain science
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)
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 collaboration-enhanced
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
Metaphor:
Physical experimentation
“Do it yourself”
Lots of disparate
systems/pieces
Future
Buy hardware & software
Automatic provenance
Workflow with 3rd party
domain packages
Excel & Access/Sql-Server
Office tool suite with
collaboration support
Metaphor:
Digital experimentation
Turn-key desktop
supercomputer
Single integrated system