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MCell Usage Scenario

MCell Usage Scenario. Project #7 CSE 260 UCSD Nadya Williams nwilliams@ucsd.edu. Outline. What is MCell ? How to run MCell ? Resources Usage Scenario Summary. Thomas M. Bartol Jr. Computational Neurobiology Laboratory The Salk Institute. Joel R. Stiles Neurobiology & Behavior

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MCell Usage Scenario

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  1. MCell Usage Scenario Project #7 CSE 260 UCSD Nadya Williams nwilliams@ucsd.edu

  2. Outline • What is MCell ? • How to run MCell ? • Resources • Usage Scenario • Summary

  3. Thomas M. Bartol Jr. Computational Neurobiology Laboratory The Salk Institute Joel R. Stiles Neurobiology & Behavior Cornell University 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 …

  4. What is MCell ?

  5. 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 ?

  6. What is MCell ?

  7. What is MCell ?

  8. What is MCell ? Main biochemical interactions • 3D diffusion of ligand molecules based 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

  9. 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 stochasticnoise CONS: complexity What is MCell ?Different approaches to computing 3D gradients

  10. 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 How to run MCell ?

  11. 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

  12. Resources Salk InstituteUCSDU. of Tennessee Bartol and SejnowskiCasanova and BermanDongarra and Wolski MCellexecutes 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 commonfiles 2 resource discovery 3 fault detection 4 fault recovery 5 scheduling

  13. Usage Scenario

  14. Usage Scenario Security Requirements • data confidentiality • need for digital signatures, encryption, authorization • public vs. private information on application status and execution Performance Requirements • network bandwidth • latency and jitter • CPU load • information service query time • disk capacity, speed • application timing formats

  15. 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 : CPURAMDisk Network Queue waiting time

  16. 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

  17. 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

  18. Milestones • What are current problems and bottlenecks ? • Can one improve basic usage scenario ? • Current needs of application from GIS • What are requirements for • job scheduling, • job control • storage infrastructure

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