VLab: A Collaborative Cyberinfrastructure for

Computations of Materials Properties at High Pressures

and Temperatures

VLab: A Collaborative Cyberinfrastructure for

Computations of Materials Properties at High Pressures

and Temperatures

Cesar R. S. da Silva1

Pedro R. C. da Silveira1

Renata M. Wentzcovitch1,2

1Minnesota Supercomputing Institute, University of Minnesota 2Department of Chemical Engineering and Materials Science, University of Minnesota

Work Sponsored by NSF grant ITR-0426757

-“VLab is a cyberinfrastructure aimed to facilitate execution of complex calculations - mostly parameter sampling workflows - of materials at high pressures and temperatures.”

-Parameter Sampling Workflows - High P,T Cij as example

-Basic Problem: -Job deluge -Proposed Solution: - Features - Performance-Overall Requirements-Workflow Support Specific Requirements-Service Oriented Architecture

Outline

Thermodynamic Method

• VDoS and F(T,V) within the QHA

Fitted at several temperatures either by- Vinet EOS, or - N-th (N=3,4,5…) order isothermal (eulerian) finite strain EoS

equilibrium structure

(Pn)

kl

re-optimize

Thermoelastic constant tensor CijS(T,P)

Basic Problem

- Wow can High (P,T) Materials Computations be improved?

Demand for Extensive Parameter Sampling

€

⇒Typical High (P,T) study(ex. Thermal Properties) {Pn}x{qi} => ~102 jobs

€

⇒Huge High (P,T) study( Cij(P,T) ) {Pn}x{i}x{qj} => ~103-4 jobs

• 102-104 Jobs to prepare, submit and monitor• Manual work is prone to human errors• First Principles => Sheer number (1015-1020) of operations (Today) => Well over 1022 in 3-5 years

The VLab- Consolidated Web Interface (Portal) to a set of tools:

- Quantum ESPRESSO Package tools - Input preparation for pwscf, phonon, workflows, etc … - Data Analysis Tools - Visualization Tools (VTK/OpenGL) - etc. …

- Workflow Management

Leverages computing capabilities of distributed resources (TeraGrid, Compute Farms, scattered resources, other grids)

Collaboration through shared access to resources

- Task Distribution and Data Recollection

The Big Challenge of Performance

Proposed Solution:Leveraging Concurrent Computing for

features and performance

High Performance Parallel Computing

High Throughput Distributed Processing

•Scale-up approach is difficult•Limited number of processors in a single system•Even using the fastest vector processors is not enough •Trend is towards denser processing, not faster single-thread execution

•MPP systems are not cost effective for this class of problems•FFT and matrix transposition: Limited scalability or•Low performance per processor

Vlab - Not Just a Client/Server

The Client/Server Approach:

-The portal and the supporting modules have access to a large central multi-processor system.

-Can work as a facilitator but lacks other important features found in VLab.

-No Flexibility of Scheduling -No redundancy => Poor availability-No choice for cost (usually High)

Vlab - Not Just a Client/Server

The VLab Distributed System Approach:

-Distributed resources are replicated for:

- Redundancy- Performance- Flexibility

-No central system to fail and bring everything down!

-More Flexible Scheduling for:- Cost- Turnaround Time - Job Throughput- Workload Balance- System Throughput

VLAB requirements•Workflow management => Facilitator

•Support for distributed computations

•Ease of use

•Support for collaboration

•Flexibility (update/add tools, new features)

•Fault tolerance

•Diversity of tools–analysis, visualization, data reduction, storage, etc .

VLab Workflows Typical VLab workflows, like

the High-T Cij calculation involve iterations through the following steps:

1) Prepare inputs for tasks, and generate execution packages containing required files.

2) Dispatch the execution packages to compute nodes for execution.

3) Gather results for analysis and eventually iterate steps 1-3.

- Results always return to the input sources => Tree-like service architecture

VLab Service Oriented Architecture On the Web:

http://dasilveira.msi.umn.edu:8080/vlab/Usage oriented view of VLab SOA

=> Tree-like structure in 4 layers: 1) User Interface (Portal)2) Workflow control and monitoring (Project Executor / Interaction)3) Task Dispatching / Interaction, task data retrieving, Auxiliary Services4) Heavy computations and Visualization resources layer.

Fault Tolerance

- Reactive: We have not identified any need for proactive FT.

- Registry Based: Persistent sessions are registered and must periodically inform the registry about its "alive" state.

- Redundant Registry and Metadata DB for data persistence

- Fully Journaling (data and metadata) of Critical Transactions for data and metadata integrity. This guarantee the state of any persistent session can be restored in case of failure.

• Only Project Executor sessions and few user and project interaction sessions are required to be persistent. Therefore, a simple approach to Fault Tolerance (FT) is possible:

SchedulingThe usual approach: -Use agents that interact with the broker

Problem: Agents are not stateless! -More complicated to develop -Persistence must be guaranteed

The VLab approach: -Use an independent WS to monitor workload. -Persistence of data is provided by a local DB. -Compute WS and Workload Monitor are stateless!

VLab in Action

Watch a demonstration movie at vlab.msi.umn.edu -> Follow the links “portal” -> “movie”

Calculation of High P,T Thermodynamic Properties Cubic MgO 2 atom cell Static + Lattice Dynamics calculation {Pn}x{i} sampling

Show distributed computing capabilities Ability to integrate visualization and data analysis tools

VLab Workflows

Left: Extensive High-T Cij

Right: Detailed View of Cij and phonon