Studying Protein Folding on the Grid: Experiences Using CHARMM on NPACI Resources under Legion

University of VirginiaAnand Natrajan

Marty A. HumphreyAnthony D. Fox

Andrew S. Grimshaw

Scripps (TSRI)Michael Crowley

Charles L. Brooks III

SDSCNancy Wilkins-Diehr

http://legion.virginia.edu

anand@virginia.edu

Outline

• CHARMM– Issues

• Legion• The Run

– Results– Lessons

• Portals• Summary

CHARMM

• Routine exploration of folding landscapes helps in search for protein folding solution

• Understanding folding critical to structural genomics, biophysics, drug design, etc.

• Key to understanding cell malfunctions in Alzheimer’s, cystic fibrosis, etc.

• CHARMM and Amber benefit majority (>80%) of bio-molecular scientists

• Structural genomic & protein structure predictions

Folding Free Energy LandscapeMolecular

Dynamics Simulations

100-200 structures to sample

(r,Rgyr ) space

Rgyr

Folding of Protein L

• Immunoglobulin-binding protein– 62 residues (small), 585 atoms

– 6500 water molecules, total 20085 atoms

– Each parameter point requires O(106) dynamics steps

– Typical folding surfaces require 100-200 sampling runs

• CHARMM using most accurate physics available for classical molecular dynamics simulation

• Multiple 16-way parallel runs - maximum efficiency

Application Characteristics

• Parameter-space study– Parameters correspond to structures along

& near folding path

• Path unknown - could be many or broad– Many places along path sampled for

determining local low free energy states– Path is valley of lowest free energy states

from high free energy state of unfolded protein to lowest free energy state (folded native protein)

Application Characteristics

• Many independent runs

– 200 sets of data to be simulated in two sequential runs

• Equilibration (4-8 hours)

• Production/sampling (8 to 16 hours)

• Each point has task name, e.g., pl_1_2_1_e

Legion

Complete, Integrated Infrastructure for Secure

Distributed Resource Sharing

Grid OS Requirements

• Wide-area• High Performance• Complexity

Management• Extensibility• Security• Site Autonomy• Input / Output• Heterogeneity

• Fault-tolerance• Scalability• Simplicity• Single Namespace• Resource

Management• Platform

Independence• Multi-language• Legacy Support

Transparent System

npacinet

The Run

Computational Issues

• Provide improved response time• Access large set of resources

transparently– geographically distributed– heterogeneous– different organisations

6 organisations6 queue types

10 queues6 architectures

~1000 processors

IBM Blue HorizonSDSC

375MHz Power3512/1184

IBM Blue HorizonSDSC

375MHz Power3512/1184

Resources Available

HP SuperDomeCalTech

440 MHz PA-8700128/128

HP SuperDomeCalTech

440 MHz PA-8700128/128

IBM SP3UMich

375MHz Power324/24

IBM SP3UMich

375MHz Power324/24

IBM AzureUTexas

160MHz Power232/64

IBM AzureUTexas

160MHz Power232/64

Sun HPC 10000SDSC

400MHz SMP32/64

Sun HPC 10000SDSC

400MHz SMP32/64

DEC AlphaUVa

533MHz EV5632/128

DEC AlphaUVa

533MHz EV5632/128

Scientists Using Legion

• Binaries for each type• Script for dispatching

jobs• Script for keeping

track of results

• Script for running binary at site– optional feature in

Legion

• Abstract interface to resources– queues, accounting,

firewalls, etc.

• Binary transfer (with caching)

• Input file transfer• Job submission• Status reporting• Output file transfer

Mechanics of Runs

Leg

ion

Register binaries

Create taskdirectories &specification

Dispatchequilibration

Dispatchequilibration& production

71%

24%

1%

2%

1%

1%

0%SDSC IBMCalTech HPUTexas IBMUVa DECSDSC CraySDSC SunUMich IBM

Distribution of CHARMM Work

LEGION

• Network slowdowns– Slowdown in the middle of the run– 100% loss for packets of size ~8500 bytes

• Site failures– LoadLeveler restarts– NFS/AFS failures

• Legion– No run-time failures– Archival support lacking– Must address binary differences

Problems Encountered

UVa

SDSC

UMich 01101

Successes

• Science accomplished faster– 1 month on 128 SGI Origins @Scripps– 1.5 days on national grid with Legion

• Transparent access to resources– User didn’t need to log on to different machines– Minimal direct interaction with resources

• Problems identified• Legion remained stable

– Other Legion users unaware of large runs

• Large grid application run at powerful resources by one person from local resource

• Collaboration between natural and computer scientists

Portal Interface

Easy Interface to Grid

• Simple point-and-click interface to Grids– Familiar access to distributed file system– Enables & encourages sharing

• Application portal model for HPC– AmberGrid– RenderGrid– Accounting

Legion GUIs

Transparent Accessto Remote Resources

Intended Audience isScientists

Logging in tonpacinet

View ofcontexts(DistributedFile System)

Control Panel

RunningAmber

RunStatus(Legion)

GraphicalView(Chime)

Summary

• CHARMM Run– Succeeded in starting big runs– Encountered problems– Learnt lessons for future

• AmberGrid– Showed proof-of-concept - grid portal– Need to resolve licence issues