developing hpc scientific and engineering applications: from the laptop to the grid

www.cactuscode.org www.gridlab.org

Developing HPC Scientific and Engineering

Applications: From the Laptop to the Grid

Gabrielle Allen, Tom Goodale, Thomas Radke, Ed Seidel

Max Planck Institute for Gravitational Physics, Germany John Shalf

Lawrence Berkeley Laboratory, USA

These slides: http://www.cactuscode.org/Tutorials.html


Outline for the DayOutline for the Day

Introduction (Ed Seidel, 30 min) Issues for HPC (John Shalf, 60 min) Cactus Code (Gabrielle Allen, 90

min) Demo: Cactus, IO and Viz (John, 15 min) LUNCH Introduction to Grid Computing (Ed, 15 min) Grid Scenarios for Applications (Ed, 60 min) Demo: Grid Tools (John, 15 min) Developing Grid Applications Today (Tom Goodale, 60

min) Conclusions (Ed, 5 min)


Introduction


OutlineOutline

Review of application domains requiring HPC Access and availability of computing resources Requirements from end users Requirements from application developers The future of HPC


What Do We Want to Achieve?What Do We Want to Achieve? Overview of HPC Applications and Techniques Strategies for developing HPC applications to be:

Portable: from Laptop to Grid Future proof Grid ready

Introduce Frameworks for HPC Application development

Introduce the Grid: What is/isn’t it? What will be? Grid Toolkits: How to prepare/develop apps for Grid,

today & tomorrow What are we NOT doing?

Application specific algorithms Parallel programming Optimizing Fortran, etc


Who uses HPC?Who uses HPC? Scientists and Engineers

Simulating Nature: Black Hole Collisions, Hurricanes, Ground water flow

Modeling processes: space shuttle entering atmosphere Analyzing data: lots of it!

Financial Markets Modeling currencies

Industry Airlines, insurance companies Transaction, data, etc

All face similar problems Computational need not met Remote facilities Heterogeneous and changing systems

Look now at three types: High-Capacity, Throughput, Data Computing


Teraflop Computation, AMR, Elliptic-Hyperbolic, ???

Numerical Relativity

High Capacity Computing: Want to Compute What Happens in Nature!High Capacity Computing: Want to Compute What Happens in Nature!

Perturbative


Computation Needs: 3D Numerical RelativityComputation Needs: 3D Numerical Relativity

Get physicists + CS people together Find Resource (TByte, TFlop crucial)Initial Data: 4 coupled nonlin. ellipticsChoose Gauge (elliptic/hyperbolic…) Evolution “hyperbolic” evolution coupled with elliptic eqs.

Find Resource ….Analysis: Interpret, Find AH, etc

t=0

t=100


Any Such Computation Requires Incredible Mix of Varied Technologies and Expertise!Any Such Computation Requires Incredible Mix of Varied Technologies and Expertise!

Many Scientific/Engineering ComponentsPhysics, astrophysics, CFD, engineering,...

Many Numerical Algorithm Components Finite difference methods? Finite elements? Elliptic equations: multigrid, Krylov subspace, preconditioners,... Mesh Refinement?

Many Different Computational Components Parallelism (HPF, MPI, PVM, ???) Architecture Efficiency (MPP, DSM, Vector, PC Clusters, ???) I/O Bottlenecks (generate gigabytes per simulation,

checkpointing…) Visualization of all that comes out!

Scientist/eng. wants to focus on top, but all required for results...

Such work cuts across many disciplines, areas of CS… And now do it on a Grid??!!


How to Achieve This?How to Achieve This?Any Such Computation Requires Incredible Mix of

Varied Technologies and Expertise! Many Scientific/Engineering Components

Physics, astrophysics, CFD, engineering,... Many Numerical Algorithm Components

Finite difference methods? Finite elements? Elliptic equations: multigrid, Krylov subspace, preconditioners,... Mesh Refinement?

Many Different Computational Components Parallelism (HPF, MPI, PVM, ???) Architecture Efficiency (MPP, DSM, Vector, PC Clusters, ???) I/O Bottlenecks (generate gigabytes per simulation,

checkpointing…) Visualization of all that comes out!

Scientist/eng. wants to focus on top, but all required for results... Such work cuts across many disciplines, areas of CS… And now do it on a Grid??!!


High Throughput Computing: Task farmingHigh Throughput Computing: Task farming

Running hundreds - millions ++ of jobs as quickly as possible

Collecting statistics, doing ensemble calculations, surveying large parameter space, etc

Typical Characteristics Many small, independent jobs: must be managed! Usually not much data transfer Sometimes jobs can be moved from site to site

Example Problems: climatemodeling.com, NUG30 Example Solutions: Condor, SC02 demos, etc Later: examples that combine “capacity” and

“throughput”


Large Data ComputingLarge Data Computing Data: more and more the “killer app” for the Grid

Data mining: Looking for patterns in huge databases distributed over the world E.g. Genome analysis

Data analysis: Large astronomical observatories Particle physics experiments Huge amounts of data from different locations to be correlated, studied

Data generation Resources Grow: Huge simulations will each generate TB-PB to be studied

Visualization How to visualize such large data, here, at a distance, distributed

Soon: Dynamic combinations of all types of computing, data & on grids

Our Goal is to give strategies for dealing with all types of computing


Grand Challenge CollaborationsGoing Large Scale: Needs Dwarf Capabilities Grand Challenge CollaborationsGoing Large Scale: Needs Dwarf Capabilities

Examples of Future of Science & Engineering

Require Large Scale Simulations, beyond reach of any machine Require Large Geo-distributed Cross-Disciplinary Collaborations Require Grid Technologies, but not yet using them! Both Apps and Grids Dynamic…

NASA Neutron Star Grand Challenge

5 US Institutions Solve problem of colliding neutron stars (try…)

QuickTime™ and a decompressor

are needed to see this picture.

NSF Black Hole Grand Challenge

8 US Institutions, 5 years Solve problem of colliding BH (try…)

EU Network Astrophysics

10 EU Institutions, 3 years, €1.5MContinue these problemsEntire Community becoming Grid enabled


Growth of Computing Resources (from Dongarra)

Growth of Computing Resources (from Dongarra)


Not just Growth, ProliferationNot just Growth, Proliferation

Systems getting larger by 2-3-4x per year! Moore’s law (processor doubles each 18 months) Increasing parallelism: add more and more

processors More systems

Many more organizations recognizing need for HPC– Universities– Labs– Industry– Business

New kind of parallelism: Grid Harness these machines, which themselves are

growing Machines all different! Be prepared for next thing…


Today’s Computational Resources Today’s Computational Resources

PDA’s Laptops PCs SMPs

Shared memory up to now Clusters

Distributed memory, must use message passing or task farming “Traditional” supercomputers

SMPs of up to ~64+ processors Clustering above this Vectors

Clusters of large systems: metacomputing The Grid

• Everyone: uses PDAs - PCs• Industry: prefers traditional machines• Academia: clusters for price/perf• We show how to minimize effort to go between systems, prepare for Grid


The Same Application … The Same Application …

Application

Middleware

Application

Middleware

Application

Middleware

Laptop The GridSuper Computer

No network! Biggest machines!


What is Difficult About HPC?What is Difficult About HPC?

Many different architectures and operating systems Things change very rapidly Must worry about many things at same time

Single processor performance, caches, etc Different languages (but now, at least everything is (nearly)

unix!) Parallelism I/O Visualization Batch systems

Portability: compilers, datatypes and associated tools


Requirements of End UsersRequirements of End Users We have problems that need to be solved

Want to work at conceptual level Build on top of other things that have been solved for us

– Use libraries, modules, etc. We don’t want to waste time with…

Learning a new parallel layer Writing high performance I/O Learning a new batch system, etc…

We have collaborators distributed all over the world We want answers fast, on whatever machines are

available Basically, want to write simple Fortran or C code and

have it work…


Requirements of Application DevelopersRequirements of Application Developers

We must have access to latest technologies These should be available through simple interfaces and APIs They should be interchangeable with each other when same

functionality is available from different packages Code we develop must be as portable and as future

proof as possible Run on all these architectures we have today Easily adapted to those of tomorrow If possible, top level user app code should not change, only

layers underneath We’ll give strategies for doing this, on today’s

machines, and on the Grid of tomorrow


Where is This All Going?Where is This All Going?

Dangerous to predict, but: Resources will continue to grow for some time

– Machines will get larger at this rate: TF now, PF tomorrow– Collections of resources into Grids is happening now, will be

routine tomorrow– Very hetergenous environments

Data explosion will be exponential– Mixture of realtime simulation and data analysis will become

routine Bandwidth from point to point will allocatable on demand! Applications will become very sophisticated, able to adapt

to their changing needs, and to changing environment (on time scales of minutes to years)

We are trying today to help you prepare for this!

developing hpc scientific and engineering applications: from the laptop to the grid

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