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