mcell usage scenario project #7 cse 260 ucsd nadya williams nwilliams@ucsd.edu
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MCell Usage Scenario
Project #7
CSE 260
UCSDNadya Williams
nwilliams@ucsd.edu
Outline
• What is MCell ?
• How to run MCell ?
• Resources
• Usage Scenario
• Summary
What is MCell ?
A General Monte Carlo Simulator of Cellular Microphysiology
… MCell now makes it possible to incorporate high resolution ultrastructure into models of
ligand diffusion and signaling …
Thomas M. Bartol Jr.Computational Neurobiology
Laboratory
The Salk Institute
Joel R. StilesNeurobiology & Behavior
Cornell University
What is MCell ?
What is MCell ?
MCell uses
• Monte Carlo diffusion
• Chemical reaction algorithms in 3D
MCell simulates
• Release of ligands in solution
• Creation/destruction of ligands
• Ligand diffusion within spaces
• Chemical reactions undergone by ligand and effector
What is MCell ?
What is MCell ?
What is MCell ?
Main biochemical interactions
• 3D diffusion of ligand moleculesbased on Brownian motion
• the average net flux from one region of space to another
depends on molecules mobility
depends on 3D concentration gradient between the regions
What is MCell ?Different approaches to computing 3D gradients
With Voxels
Assume well-mixed condition
Use PDEs for average net changes
PROS:
• correct average system behavior
CONS:
• too complex for realistic structures
• output has no direct stochastic information
Monte Carlo approach
• Directly approximate the Brownian movements of the individual ligand
• Chemical reaction rates are solution rate const
PROS: • events are considered on a
molecule-by-molecule basis • the simulation results include
realistic stochastic noise
CONS: • complexity
How to run MCell ?
Simulate the system behavior
• Running the same computation with different seeds
• Averaging all the instances
Each instance has • A pre-defined number of time
steps
• Input data
Input Data consists of • one or more MDL scripts
• files describing elements of the simulation
spatial geometry
effector location
chemicals' repartitions
Output files • resulting stochastic model
• visualization files
Resources
Typical run now:
• 5 MBytes of input data per task
• 1000 tasks• 1 MBytes 2-D output files per
task• 10 MBytes 3-D output files
per task• usually 100 MBytes of RAM • require on the order of 10
minutes of processing on today's most powerful CPUs.
• Modeling ligands exchange, diffusion
Run envisioned:
• 50 MBytes of input data per task
• 1,000,000 tasks
• Tens of GBytes 2-D and 3-D output files per task
• RAM not easily available to an average user
• CPUs of MPPs.
• Modeling entire cells
Resources
Salk Institute UCSD U. of Tennessee
Bartol and Sejnowski Casanova and Berman Dongarra and Wolski
MCell executes multiple instances of a given code on different
parameter set and collects (and perhaps processes) the results.
PROS:
each instance is independent from the others
each instance can be executed anywhere
Challenges:
1 tasks share common files 2 resource discovery
3 fault detection 4 fault recovery
5 scheduling
Usage Scenario
Usage Scenario
Security Requirements• data confidentiality• need for digital signatures, encryption, authorization• public vs. private information on application status and
executionPerformance Requirements
• network bandwidth• latency and jitter• CPU load• information service query time• disk capacity, speed• application timing formats
Usage Scenario
Programming Model
• user interfaces (submit, monitor, steer runs)
• support for data analysis and visualization
Information Service Requirements
• frequency of information access
• application preferences on location, structure,
• representation, and format of IS information :
CPU RAM Disk
Network Queue waiting time
Usage Scenario
Scheduling Requirements• resource reservation • application components, computation• data, intermediate files• remote instruments • tolerance to delays during execution
Remote Data Access requirements• publication, management, storage• streaming vs. batch processing
User Services• system status, its format• application needs for system services and tools
Summary
The MCell development contributions: • larger problem size model for a class of science
applications
• parameter sweep application model for the Grid.
MCell needs:
• large-scale MCell runs
• further improvement and development of application scheduling mechanism
Milestones
1. What are current problems and bottlenecks ?
2. Can one improve basic usage scenario ?
3. Current needs of application from GIS
4. What are requirements for
– job scheduling,
– job control
– storage infrastructure
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