kajnyida.liu.se

1
GridModelica High Level Modeling on the Grid Kaj
Nyström Dept. of Computer and Information
Science, Linköping University MathCore
Engineering AB kajny_at_ida.liu.se
kajny_at_ida.liu.se
2
Modeling The way of doing things

• Modeling (like talk) is cheap, considering the
  alternatives
• Modeling is almost always possible
• Modeling gives extensive data even where
  physical measuring might not always be
  possible, or is too expensive.
• Modeling is fast, especially on large systems
• Limiting factors
• Model complexity
• Computational power

kajny_at_ida.liu.se
3
Modeling The way of doing things(2)

• Factors limiting usage of modeling
• Conservative engineers
• Domain knowledge
• Model complexity
• Computational power

kajny_at_ida.liu.se
4
Modeling Dealing with complexity

• Traditionally high performance models have been
  written in C or Fortran.
• The Modelica language provides an object
  oriented approach to modeling.
• Main benefits from our point of view
• Acausal No need to do the mathematical
  dirtywork yourself anymore.
• Connection oriented Well defined interface
  between all components (very useful for
  parallelization).
• Reusable Extensive object libraries exists.

kajny_at_ida.liu.se
5
Modeling Dealing with complexity(2)
Example The DC-motor

kajny_at_ida.liu.se
6
Modeling Dealing with complexity(3)
Example The DC-motor, modelica source code

model dcmotor Modelica.Electrical.Analog.Basic.R
esistor Resistor1 Modelica.Electrical.Analog.Ba
sic.Ground Ground1 Modelica.Electrical.Analog.B
asic.Inductor Inductor1 Modelica.Electrical.Ana
log.Basic.EMF EMF1 Modelica.Electrical.Analog.S
ources.ConstantVoltage ConstantVoltage1
Modelica.Electrical.Analog.Basic.Resistor
Resistor2(R10) equation connect(Resistor1.p,
ConstantVoltage1.p) connect(Resistor1.n,
Inductor1.p) connect(Inductor1.n, EMF1.p)
connect(ConstantVoltage1.n, Ground1.p)
connect(EMF1.n, Ground1.p) connect(EMF1.flange_
b, Inertia1.flange_a) connect(Resistor2.p,
Resistor1.p) connect(Resistor2.n,
Resistor1.n) end dcmotor
kajny_at_ida.liu.se
7
Modeling Dealing with complexity(4)
Another example A robot, including visualization
kajny_at_ida.liu.se
8
Problem 2 Computational Power

• The Grid is the solution (?) Computational
  power for the masses.
• Nordugrid middleware
• Resources Nordugrid and our own cluster
• This provides the computational power we needbut
  it also just brings us to the next (big) problem

kajny_at_ida.liu.se
9
Problem no 2 Computational Power

• The Grid is the solution (?) Computational
  power for the masses.
• Nordugrid middleware
• Resources Nordugrid and our own cluster
• This provides the computational power we needbut
  it just brings us to the next (big) problem
• Partitioning!

kajny_at_ida.liu.se
10
Partitioning models on the Grid
An example abcd10abc2c-d-2ab-d4 How
would you solve this system?
kajny_at_ida.liu.se
11
Partitioning models on the Grid (2)

• The grid has special features/misfeatures
• Latency, latency, latency, latency
• Heterogenity
• Close to infinite resources
• Unpredictability
• Unreliability

kajny_at_ida.liu.se
12
Partitining the models

• We attack the problem on different levels
• High level partitioning - New Modelica
  constructs to enable parallelism.
• Medium level partitioning - Shared level memory
  model (GridNestStep).
• Low level partitioning Parallelizing the
  generated C-code.

kajny_at_ida.liu.se
13
High Level Partitioning

• Partitioning at Modelica model level
• Done by adding partitioning constructs to the
  language and modification of the Modelica
  compiler.
• Considered methods are
• Weak Operators/Variables (Francesco Casella)
• Transmission Line Modeling method (Christos
  Christopoulos)
• Modelica specific ideas

kajny_at_ida.liu.se
14
High Level Partitioning (2)

• The weak variables method
• Partition the model into physical domains
• Identify boundary variables
• Treat these variables as parameters on both
  sides of boundary.
• Use fast fixed step solvers but with different
  step-size for both subsystems

kajny_at_ida.liu.se
15
High Level Partitioning (3)

• The transmission line modeling method
• Partition the model fast and slow subsystems
• Insert delaying TLM-element between subsystems
• Calculate approximate loss in precision and
  compensate if possible

kajny_at_ida.liu.se
16
Medium Level Partitioning

• Grid level parallelization with GridNestStep
• Shared memory model for the grid
• Specific adaption for the grid is neccesary,
  maximize time between syncs is absolutely
  vital.

kajny_at_ida.liu.se
17
Low Level Partitioning

• Primarily by task merging and common
  parallelization techniques in parallel solvers
• Task merging
• Partition code very fine grained
• Achive parallelism by a sequence of duplicating
  tasks and merging very small jobs, depending on
  computational and communication cost.
• This can be done adaptively for large jobs

kajny_at_ida.liu.se
18
Wrap up

• Parallelizing solving of large equation systems
  is hard.
• Latency is our worst enemy
• Different variants of speculative execution
  might be our best friends
• The grid might be a hype, but it is a useful
  hype.

kajny_at_ida.liu.se
19
Thank you for your attention Questions?
kajny_at_ida.liu.se