Developing HPC Scientific and Engineering Applications: From the Laptop to the Grid

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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
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Outline 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)

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Introduction
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Outline

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

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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/isnt 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

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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

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High Capacity Computing Want to Compute What
Happens in Nature!
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Computation Needs 3D Numerical Relativity
t0
Get physicists CS people together Find
Resource (TByte, TFlop crucial) Initial Data 4
coupled nonlin. elliptics Choose Gauge
(elliptic/hyperbolic) Evolution hyperbolic
evolution coupled with elliptic eqs. Find
Resource . Analysis Interpret, Find AH, etc
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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 volume? 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??!!

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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??!!

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High 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

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Large 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

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Grand Challenge CollaborationsGoing Large Scale
Needs Dwarf Capabilities

• NASA Neutron Star Grand Challenge
• 5 US Institutions
• Solve problem of colliding neutron stars (try)

• NSF Black Hole Grand Challenge
• 8 US Institutions, 5 years
• Solve problem of colliding BH (try)

• EU Network Astrophysics
• 10 EU Institutions, 3 years, 1.5M
• Continue these problems
• Entire Community becoming Grid enabled

• 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

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Growth of Computing Resources (from Dongarra)
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Not just Growth, Proliferation

• Systems getting larger by 2-3-4x per year!
• Moores 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

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Todays Computational Resources

• PDAs
• 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

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The Same Application
Laptop
Super Computer
The Grid
Application
Application
Application
Middleware
Middleware
Middleware
No network!
Biggest machines!
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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
• Different operating systems (but now, at least
  everything is (nearly) unix!)
• Parallelism
• I/O
• Visualization
• Batch systems
• Portability compilers, datatypes and associated
  tools

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Requirements 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 dont 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

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Requirements 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 application code
  should not change, only layers underneath
• Well give strategies for doing this, on todays
  machines, and on the Grid of tomorrow

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Where is This All Going?

• Dangerous to predict, but
• Resources will continue to grow for some time
• Machines will get larger at this rate TeraFlop
  now, PetaFlop tomorrow
• Collections of resources into Grids is happening
  now, will be routine tomorrow
• Very heterogeneous environments
• Data explosion will be exponential
• Mixture of real-time 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!