Case Study: FEM Using Parallel SQL Server
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Transcript Case Study: FEM Using Parallel SQL Server
FEM with Parallel SQL
Server: A Case Study
Gerd Heber
Cornell Theory Center
Cornell Fracture Group
Thank You
Dan Fay (MSR)
Jim Gray (MSR)
Todd Needham (MSR)
Alexander Szalay (JHU)
Outline
Parallel SQL Server
Context
Examples
Infrastructure
Application
Complexity
Issues
Conclusions
Parallel SQL Server
Hardware
SMP
Distributed memory
Software
Query level parallelism
Partitioned views (LDPV)
Linked servers
Distributed partitioned views (DPV)
When to Use DPV
Scale-out (DPV) vs. scale-up (SMP)
Good performance on commodity
hardware
Data (and queries) must be suitable for
partitioning
Increased application complexity
Go to the server with most, or all, of the data
For reliability consider failover
clustering
No support for parallel (bulk) inserts
CTC’s Infrastructure Today
Clusters
Tape Robot
Domain
Database Servers
Active Directory
Scheduler
Certificate Server
Web Servers
File Servers
Login Nodes
Cave
SCTS
Basic FEM Analysis
Preprocessing
Solution
Topology/Geometry generation
Mesh generation
Apply boundary conditions
Material properties
Equation solving
Error analysis
Post processing
Data analysis
Visualization
How We Used To Do Things
100% file-based
Monolithic (brittle) code
Disconnected
No data-sharing, except copy
Hard to debug
Plenty of non problem oriented code
Where We Use SQL Server
Data storage
Analysis
Debugging
Visualization
Processing
Checkpoint / restart
Web service state management
Data virtualization
XML repository
Input
100 MB - 1 GB (today)
Files ASCII (incl. XML), binary
Topology, geometry, mesh
Initial / boundary conditions
Material properties
Input may or may not be partitioned
Output
Physical fields
Temperature (1x double per node)
Displacement (3x double per node)
Stress, strain (6x double per Gauss point)
State variables
Tetrahedron: 5 / 11 Gauss points
Hexahedron: 27 / 64 Gauss points
Mises plasticity (13x double per Gauss point)
Polycrystal plasticity (>= 30x double per GP)
…
Produce 10 - 1000 times the input size
Datasets
10-3
10-6
10-9
m|s
Examples
l
Pictures provided by Paul Wawrzynek, Cornell Fracture Group
Visual SQL
DDSim
ES7000
Pictures provided by John Emery, Cornell Fracture Group
Spatial Search
Jim Gray et al., There Goes the Neighborhood:
Relational Algebra for Spatial Data Search, MSR-TR2004-32
Web Services
Adaptive Software Project
NSF-ITR #0085969:
Adaptive Software for
Field-driven
Simulations (09/01/00)
Implement a system
for multi-physics
multi-scale adaptive
CSE simulations
Computational fracture
mechanics
Chemically-reacting
flow simulation
Understand principles
of implementing
adaptive software
systems
Adaptivity in CSE Simulations
Application-level adaptivity
Algorithm-level adaptivity
Change in modeling / governing equations
Example: Elasticity PDE’s vs. molecular-scale
interactions, symmetry
Change in solution method for governing
equations
Example: Finite-element vs. wavelet bases
System-level adaptivity
Response to changing resource availability
Example: Processor / link failure
Test Problems
(Substantial) Infrastructure
Metadata management
State management
Event logging
Data virtualization
Accounting
Transactions
Yukon
NET CLR integration
Stored procedures, user-defined
functions, and triggers in .NET languages
(and T-SQL)
Call unmanaged (unsafe) code
User defined aggregates and types
Native XML data type (schema support)
XQuery support
Database logic can be invoked as Web
service
XML Repository
Polycrystal Generation
Comments / Issues / Wishes
SQL Libraries
Better management tools for linked servers
Embedded SQL renaissance
WSE 2.0 and Yukon
WS interface for WMI
Template(s) for WS state and event
management
O’SOAP
Visual SQL
Virtualization not there (yet)
Data grids
Conclusion
“Language shapes the way we
think, and determines what we
can think about.” (B.L. Whorf)
It’s a slow process
Most engineers are conservatives
Legacy
Sponsors
DARPA
Intel
Microsoft
Microsoft Research
NASA
NSF
Northrop Grumman
Unisys