1. Nimrod-G and Virtual Lab Tools for Data Intensive Computing on Grid: Drug Design Case Study
Rajkumar Buyya
Melbourne, Australia

3. Contents Introduction Resource Management challenges Nimrod-G Toolkit
SPMD/Parameter-Study Creation Tools
Grid enabling Drug Design Application
Nimrod-G Grid Resource Broker
Scheduling Experiments on World Wide Grid
Conclusions
Scheduling
Economics
Grid
Economy Grid

4. A typical Grid environment and Players
Resource Broker
Application
Resource Broker

5. Grid Characteristics Heterogeneous Distributed
Resource Types: PC, WS, Clusters
Resource Architecture: CPU Arch, OS
Applications: CPU/IO/message intensive
Users and Owners Requirements
Access Price: different for different users, resources and time.
Availability: varies from time to time.
Distributed
Resources
Ownership
Users
Each have their own (private) policies and objectives.
Very much similar to heterogeneity and decentralization that is present in “human economies” (democratic and capitalist world).
Hence, we propose the use of “economics” as a metaphor for resource management and scheduling. It regulates supply and demand for resources and offers incentive for resource owners for contributing resources to the Grid.

6. Grid Tools for Handling
Uniform Access
System Management
Computational Economy
Security
Resource Discovery
Resource Allocation
& Scheduling
Data locality
Network Management
Application Development

7. Nimrod-G: Grid Resource Broker
A resource broker for managing, steering, and executing task farming (parametric sweep/SPMD model) applications on Grid based on deadline and computational economy.
Based on users’ QoS requirements, our Broker dynamically leases services at runtime depending on their quality, cost, and availability.
Key Features
A single window to manage & control experiment
Persistent and Programmable Task Farming Engine
Resource Discovery
Resource Trading
Scheduling & Predications
Generic Dispatcher & Grid Agents
Transportation of data & results
Steering & data management
Accounting

8. Parametric Processing
Parameters
Magic Engine for
Manufacturing Humans!
Multiple Runs
Same Program
Multiple Data
Killer Application for the Grid!
Courtesy: Anand Natrajan, University of Virginia

9. Sample P-Sweep Applications
Bioinformatics: Drug Design / Protein Modelling
Combinatorial Optimization:
Meta-heuristic parameter estimation
Ecological Modelling: Control Strategies for Cattle Tick
Sensitivity experiments on smog formation
Data Mining
High Energy Physics: Searching for Rare Events
Electronic CAD: Field Programmable Gate Arrays
Computer Graphics: Ray Tracing
Finance: Investment Risk Analysis
VLSI Design: SPICE Simulations
Civil Engineering:
Building Design
Automobile:
Crash Simulation
Network Simulation
Aerospace: Wing Design
astrophysics

10. Virtual Drug Design: Data Intensive Computing on Grid
A Virtual Laboratory for “Molecular Modelling for Drug Design” on Peer-to-Peer Grid.
It provides tools for examining millions of chemical compounds (molecules) in the Protein Data Bank (PDB) to identify those having potential use in drug design.
In collaboration with:
Kim Branson, Structural Biology, Walter and Eliza Hall Institute (WEHI)

11. Molecule to be screened
Dock input file
score_ligand yes
minimize_ligand yes
multiple_ligands no
random_seed
anchor_search no
torsion_drive yes
clash_overlap
conformation_cutoff_factor 3
torsion_minimize yes
match_receptor_sites no
random_search yes
maximum_cycles
ligand_atom_file S_1.mol2
receptor_site_file ece.sph
score_grid_prefix ece
vdw_definition_file parameter/vdw.defn
chemical_definition_file parameter/chem.defn
chemical_score_file parameter/chem_score.tbl
flex_definition_file parameter/flex.defn
flex_drive_file parameter/flex_drive.tbl
ligand_contact_file dock_cnt.mol2
ligand_chemical_file dock_chm.mol2
ligand_energy_file dock_nrg.mol2
Molecule to be screened

12. Parameterize Dock input file (use Nimrod Tools: GUI/language)
score_ligand $score_ligand
minimize_ligand $minimize_ligand
multiple_ligands $multiple_ligands
random_seed $random_seed
anchor_search $anchor_search
torsion_drive $torsion_drive
clash_overlap $clash_overlap
conformation_cutoff_factor $conformation_cutoff_factor
torsion_minimize $torsion_minimize
match_receptor_sites $match_receptor_sites
random_search $random_search
maximum_cycles $maximum_cycles
ligand_atom_file ${ligand_number}.mol2
receptor_site_file $HOME/dock_inputs/${receptor_site_file}
score_grid_prefix $HOME/dock_inputs/${score_grid_prefix}
vdw_definition_file vdw.defn
chemical_definition_file chem.defn
chemical_score_file chem_score.tbl
flex_definition_file flex.defn
flex_drive_file flex_drive.tbl
ligand_contact_file dock_cnt.mol2
ligand_chemical_file dock_chm.mol2
ligand_energy_file dock_nrg.mol2
Molecule to be screened

13. Create Dock PlanFile 1. Define Variable and their value
parameter database_name label "database_name" text select oneof "aldrich" "maybridge" "maybridge_300" "asinex_egc" "asinex_epc" "asinex_pre" "available_chemicals_directory" "inter_bioscreen_s" "inter_bioscreen_n" "inter_bioscreen_n_300" "inter_bioscreen_n_500" "biomolecular_research_institute" "molecular_science" "molecular_diversity_preservation" "national_cancer_institute" "IGF_HITS" "aldrich_300" "molecular_science_500" "APP" "ECE" default "aldrich_300";
parameter score_ligand text default "yes";
parameter minimize_ligand text default "yes";
parameter multiple_ligands text default "no";
parameter random_seed integer default 7;
parameter anchor_search text default "no";
parameter torsion_drive text default "yes";
parameter clash_overlap float default 0.5;
parameter conformation_cutoff_factor integer default 5;
parameter torsion_minimize text default "yes";
parameter match_receptor_sites text default "no";
parameter random_search text default "yes";
parameter maximum_cycles integer default 1;
parameter receptor_site_file text default "ece.sph";
parameter score_grid_prefix text default "ece";
parameter ligand_number integer range from 1 to 2000 step 1;
Molecules to be screened

14. Create Dock PlanFile 2. Define Task that jobs need to do
task nodestart
copy ./parameter/vdw.defn node:.
copy ./parameter/chem.defn node:.
copy ./parameter/chem_score.tbl node:.
copy ./parameter/flex.defn node:.
copy ./parameter/flex_drive.tbl node:.
copy ./dock_inputs/get_molecule node:.
copy ./dock_inputs/dock_base node:.
endtask
task main
node:substitute dock_base dock_run
node:substitute get_molecule get_molecule_fetch
node:execute sh ./get_molecule_fetch
node:execute $HOME/bin/dock.$OS -i dock_run -o dock_out
copy node:dock_out ./results/dock_out.$jobname
copy node:dock_cnt.mol2 ./results/dock_cnt.mol2.$jobname
copy node:dock_chm.mol2 ./results/dock_chm.mol2.$jobname
copy node:dock_nrg.mol2 ./results/dock_nrg.mol2.$jobname

15. Use Nimrod-G
Submit & Play!

16. A Nimrod/G Monitor Cost Deadline Legion hosts Globus Hosts
Bezek is in both
Globus and Legion Domains
A Nimrod/G Monitor
Cost
Deadline

17. Adaptive Scheduling Algorithms
Discover More Resources
Discover Resources
Establish Rates
Compose & Schedule
Evaluate & Reschedule
Meet requirements ? Remaining Jobs, Deadline, & Budget ?
Distribute Jobs

18. Scheduling Experiment on World Wide Grid Testbed
WW Grid
Scheduling Experiment on World Wide Grid Testbed
Cardiff/UK
Portsmoth/UK
TI-Tech/Tokyo
ETL/Tsukuba
AIST/Tsukuba
ANL/Chicago
USC-ISC/LA
UTK/Tennessee
UVa/Virginia
Dartmouth/NH
BU/Boston
EUROPE:
ZIB/Germany
PC2/Germany
AEI/Germany Lecce/Italy
CNR/Italy
Calabria/Italy
Pozman/Poland
Lund/Sweden
CERN/Swiss
Kasetsart/Bangkok
Monash/Melbourne
VPAC/Melbourne
Santiago/Chile

19. Deadline and Budget Constrained Scheduling Experiment
Workload:
165 jobs, each need 5 minute of CPU time
Deadline: 2 hrs. and budget: units
Strategy: minimise time / cost
Execution Cost with cost optimisation
Optimise Cost: (G$) (finished in 2hrs.)
Optimise Time: (G$) (finished in 1.25 hr.)
In this experiment: Time-optimised scheduling run costs double that of Cost-optimised.
Users can now trade-off between Time Vs. Cost.

20. World Wide Grid (WWG) Internet Australia North America Monash Uni.:
WW Grid
World Wide Grid (WWG)
Australia
North America
Monash Uni.:
ANL: SGI/Sun/SP2
USC-ISI: SGI
UVa: Linux Cluster
UD: Linux cluster
UTK: Linux cluster
Nimrod/G
Linux cluster
Globus+Legion
GRACE_TS
Solaris WS
Globus/Legion
GRACE_TS
Internet
WW Grid
Asia/Japan
Europe
Tokyo I-Tech.:
ETL, Tuskuba
ZIB/FUB: T3E/Mosix
Cardiff: Sun E6500
Paderborn: HPCLine
Lecce: Compaq SC
CNR: Cluster
Calabria: Cluster
CERN: Cluster
Pozman: SGI/SP2
Linux cluster
Globus +
GRACE_TS
Chile: Cluster
Globus +
GRACE_TS
Globus +
GRACE_TS
South America

21. Resources Selected & Price/CPU-sec.
Resource & Location
Grid services & Fabric
Cost/CPU sec. or unit
No. of Jobs Executed
Time_Opt
Cost_Opt
Linux Cluster-Monash, Melbourne, Australia
Globus, GTS, Condor 2 64
153
Linux-Prosecco-CNR, Pisa, Italy
Globus, GTS, Fork 3 7 1
Linux-Barbera-CNR, Pisa, Italy 4 6
Solaris/Ultas2
TITech, Tokyo, Japan 9
SGI-ISI, LA, US 8 37 5
Sun-ANL, Chicago,US 42
Total Experiment Cost (G$)
237000
115200
Time to Complete Exp. (Min.) 70
119

22. DBC Scheduling for Time Optimization

23. DBC Scheduling for Cost Optimization

24. Conclusions
P2P and Grid Computing is emerging as a next generation computing platform for solving large scale problems through sharing of geographically distributed resources.
Resource management is a complex undertaking as systems need to be adaptive, scalable, competitive,…, and driven by QoS.
We proposed a framework based on “computational economies” and discussed several economic models for resource allocation and for regulating supply-and-demand for resources.
Scheduling experiments on World Wide Grid demonstrate our Nimrod-G broker ability to dynamically lease or rent services at runtime based on their quality, cost, and availability depending on consumers QoS requirements.
Easy to use tools for composing applications to run on Grid are essential to attracting and getting application community on board.
Economics paradigm for QoS driven resource management is essential to push P2P/Grids into mainstream computing!

25. Download Software & Information
Nimrod & Parameteric Computing:
Economy Grid & Nimrod/G:
Virtual Laboratory/Virtual Drug Design:
Grid Simulation (GridSim) Toolkit (Java based):
World Wide Grid (WWG) testbed:
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