11/18/08 1

An Inconvenient Question: Are We Going to Get the Algorithms and Computing Technology We Need to Make Critical Climate

Predictions in Time?

Rich LoftDirector, Technology Development

Computational and Information Systems Laboratory

National Center for Atmospheric Researchloft@ucar.edu

Main Points• Nature of the climate system makes it a grand

challenge computing problem.• We are at a critical juncture: we need regional

climate prediction capabilities!• Computer clock/thread speeds are stalled:

massive parallelism is the future of supercomputing.

• Our best algorithms, parallelization strategies and architectures are inadequate to the task.

• We need model acceleration improvements in all three areas if we are to meet the challenge.

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Options for Application Acceleration

• Scalability– Eliminate bottlenecks– Find more parallelism – Load balancing algorithms

• Algorithmic Acceleration– Bigger Timesteps

• Semi-Lagrangian Transport• Implicit or semi-implicit time integration – solvers

– Fewer Points• Adaptive Mesh Refinement methods

• Hardware Acceleration– More Threads

• CMP, GP-GPU’s

– Faster threads • device innovations (high-K)

– Smarter threads• Architecture - old tricks, new tricks… magic tricks

– Vector units, GPU’s, FPGA’s

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Viner (2002)

A Very Grand Challenge:Coupled Models of the Earth

System

Typical Model Computation: - 15 minute time steps- 1 peta-flop per model year

~150 km

There are 3.5 million timesteps in a century

air column

water column

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Multicomponent Earth System Model

Atmosphere Ocean

Coupler

Sea IceLand

C/NCycle

Dyn.Veg.

Ecosystem & BGCGas chem. Prognostic

AerosolsUpperAtm.

LandUse

IceSheets Software Challenges:

•Increasing Complexity•Validation and Verification•Understanding the Output

Key concept: A flexible coupling framework is critical!

Climate Change

Credit: Caspar AmmanNCAR 11/18/08 6

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o IPCC AR4: “Warming of the climate system is un-equivocal” …

o …and it is “very likely” caused by human activities.

o Most of the observed changes over the past 50 years are now simulated by climate models adding confidence to future projections.

o Model Resolutions: O(100 km)

IPCC AR4 - 2007

Climate Change Research Epochs

Reproduce historical trends

Investigate climate change

Run IPCC Scenarios

Assess regional impacts

Simulate adaptation strategies

Simulate geoengineering solns

Before IPCC AR4 After

Curiosity Driven Policy Driven

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2007

11/18/08 9

ESSL - The Earth & Sun Systems Laboratory

Where we want to go:The Exascale Earth System

Model VisionCoupled Ocean-Land-Atmosphere Model

~1 km x ~1 km (cloud-resolving)

100 levels, whole atmosphere

Unstructured, adaptive grids

~100 m

10 levels

Landscape-resolving

~10 km x ~10 km (eddy-resolving)

100 levels

Unstructured, adaptive grids

Requirement: Computing power enhancement by as much as a factor of 1010-1012

YIKES!

Compute Factors for ultra-high resolution Earth System Model

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Spatial resolution Provide regional details

103-105

Model completeness

Add “new” science 102

New parameterizations

Upgrade to “better” science

102

Run length Long-term implications

102

Ensembles, scenarios

Range of model variability

10

Total Compute Factor

1010-1012

(courtesy of John Drake, ORNL)

Why run-length:global thermohaline

circulation timescale: 3,000 years

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Why resolution: Atmospheric convective (cloud) scales in

the : O(1 km)

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Why High Resolution in the Ocean?

Ocean component of CCSM (Collins et al, 2006)

Eddy-resolving POP (Maltrud & McClean,2005)

1˚ 0.1˚

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High Resolution and the Land Surface

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Performance Improvements are not coming fast enough!

…suggests 1010 to 1012 improvement will take 40 years

ITRS Roadmap: feature size dropping

14%/year

By 2050 reaches the size of an atom – oops!

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National Security Agency - The power consumption of today's advanced computing systems is rapidly becoming the limiting factor with respect to improved/increased computational ability." 

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Chip Level Trends: Stagnant Clock Speed

Source: Intel, Microsoft (Sutter) and Stanford (Olukotun, Hammond)

• Chip density is continuing increase ~2x every 2 years– Clock speed is

not– Number of cores

are doubling instead

• There is little or no additional hidden parallelism (ILP)

• Parallelism must be exploited by software

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Moore’s Law -> More’s Law: Speed-up through increasing

parallelism

How long can we double the number of cores per chip?

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Dr. Henry Tufoand myself with “frost”

(2005)

Characteristics:•2048 Processors/5.7 TF•PPC 440 (750 MHz) •Two processors/node•512 MB memory per node•6 TB file system

NCAR and University Colorado Partner to Experiment with Blue Gene/L

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Status and immediate plans for high resolution Earth System Modeling

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Current high resolution CCSM runs

• 0.25 ATM,LND + 0.1 OCN,ICE [ATLAS/LLNL]– 3280 processors– 0.42 simulated years/day (SYPD)– 187K CPU hours/year

• 0.50 ATM,LND + 0.1 OCN,ICE [FRANKLIN/NERSC]– Current

• 5416 processors • 1.31 SYPD• 99K CPU hours/year

– “Efficiency Goal• 4932 processors• 1.80 SYPD• 66K CPU hours/year

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Current 0.5 CCSM “fuel efficient” configuration [franklin]

5416 processors

168 sec.

OCN[np=3600]

120 sec.

ATM[np=1664]

52 sec.

CPL[np=384]

21 sec.

LND[np=16]

ICE[np=1800]

91 sec.

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Efficiency issues in current 0.5 CCSM configuration

120 sec.

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Load Balancing: Partitioning with Space Filling Curves

Partition for 3 processors

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Space-filling Curve Partitioning for Ocean Model running on 8

Processors

Key concept: no need to compute over land!

Static Load Balancing…

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Ocean Model 1/10 Degree performance

Key concept: You need routine access to > 1k procs to discover true scaling behaviour!

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Efficiency issues in Current CCSM 0.5 configuration

LND[np=16]

ICE[np=1800]

91 sec.

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Static, Weighted Load Balancing Example:Sea Ice Model CICE4 @ 1° on 20 processors

Small domains @ high latitudes

Large domains @ low latitudes

Courtesy of John DennisCourtesy of John Dennis

11/18/08 30

Efficiency issues in current 0.5 CCSM configuration:

Coupler

CPL[np=384]

21 sec.

Unresolved scalability issues in Coupler – Options: Better interconnect, Nested grids,PGAS language paradigm

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Efficiency issues in current 0.5 CCSM configuration:

atmospheric component

ATM[np=1664]

52 sec.

Scalability limitation in 0.5° fv-CAM [MPI] – shift to hybrid OpenMP/MPI version

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Projected 0.5 CCSM “capability” configuration: 3.8 years/day

19460 processors

62 sec.

OCN[np=6100]

62 sec.

ATM[np=5200]

31 sec.

CPL[np=384]

21 sec.

LND[np=40]

ICE[np=8120]

10 sec.

Action: Run hybrid atmospheric model

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Projected 0.5 CCSM “capability” configuration - version 2: 3.8

years/day

14260 processors

62 sec.

OCN[np=6100]

62 sec.

ATM[np=5200]

31 sec.

CPL[np=384]

21 sec.

LND[np=40]

ICE[np=8120]

10 sec.Action: Thread ice model

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Scalable Geometry Choice: Cube-Sphere

• Sphere is decomposed into 6 identical regions using a central projection (Sadourny, 1972) with equiangular grid (Rancic et al., 1996).

• Avoids pole problems, quasi-uniform.

• Non-orthogonal curvilinear coordinate system with identical metric terms

Ne=16 Cube SphereShowing degree of

non-uniformity

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Scalable Numerical Method:High-Order Methods

• Algorithmic Advantages of High Order Methods– h-p element-based method on quadrilaterals (Ne

x Ne)– Exponential convergence in polynomial degree

(N)

• Computational Advantages of High Order Methods– Naturally cache-blocked N x N computations– Nearest-neighbor communication between

elements (explicit)– Well suited to parallel µprocessor systems

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HOMME: Computational Mesh

• Elements:– A quadrilateral “patch” of N x N

gridpoints– Gauss-Lobatto Grid– Typically N={4-8}

• Cube – Ne = Elements on an edge– 6 x Ne x Ne elements total

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Partitioning a cube-sphere on 8 processors

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Partitioning a cubed-sphere on

8 processors

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Aqua-Planet CAM/HOMME Dycore

Full CAM Physics/HOMME DycoreParallel I/O library used for physics aerosol input and

input data ( work COULD NOT have been done without Parallel IO)Work underway to couple to other CCSM components

5 years/day

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Projected 0.25 CCSM “capability” configuration - version 2: 4.0 years/day

30000 processors

60 sec.

OCN[np=6000]

60 sec.

HOMME ATM[np=24000]

47 sec.

CPL[np=3840]

8 sec.

LND[np=320]

ICE[np=16240]

5 sec.Action: insert scalable atmospheric dycore

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Using a bigger parallel machine

can’t be the only answer • Progress in the Top 500 list is not fast enough• Amdahl’s Law is formidable opponent• Dynamical timestep goes like N-1

– Merciless effect of Courant limit– The cost of dynamics relative to physics increases as

N– e.g. if dynamics takes 20% at 25 km it will take 86%

of the time at 1 km

• Traditional parallelization of horizontal leaves N2 per thread cost (vertical x horizontal)– Must inevitably slow down with stalled thread

speeds

Options for Application Acceleration

• Scalability– Eliminate bottlenecks– Find more parallelism – Load balancing algorithms

• Algorithmic Acceleration– Bigger Timesteps

• Semi-Lagrangian Transport• Implicit or semi-implicit time integration – solvers

– Fewer Points• Adaptive Mesh Refinement methods

• Hardware Acceleration– More Threads

• CMP, GP-GPU’s

– Faster threads • device innovations (high-K)

– Smarter threads• Architecture - old tricks, new tricks… magic tricks

– Vector units, GPU’s, FPGA’s

11/18/08 42

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Accelerator Research

• Graphics Cards – Nvidia 9800/Cuda– Measured 109x on WRF microphysics on

9800GX2• FPGA – Xilinx (data flow model)

– 21.7x simulated on sw-radiation code• IBM Cell Processor - 8 cores• Intel Larrabee

43

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DG+NH+AMR

•Curvilinear elements

•Overhead of parallel AMR at each time-step: less than 1%

Idea based on Fischer, Kruse, Loth (02)

Courtesy of Amik St. Cyr

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SLIM ocean model•Louvain la Neuve University

•DG, implicit, AMR unstructured To be coupled to prototype

unstructured ATM model

(Courtesy of J-F Remacle LNU)

NCAR Summer Internships in Parallel Computational Science

(SIParCS)2007-2008

• Open to:– Upper division undergrads– Graduate students

• In Disciplines such as: – CS, Software Engineering– Applied Math, Statistics– ES Science

• Support:– Travel, Housing, Per diem– 10 weeks salary

• Number of interns selected:– 7 in 2007– 11 in 2008

http://www.cisl.ucar.edu/siparcs

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Meanwhile - the clock is ticking

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The Size of the Interdisciplinary/Interagency Team

Working on Climate Scalability• Contributors:

D. Bailey (NCAR)F. Bryan (NCAR)T. Craig (NCAR)A. St. Cyr (NCAR)J. Dennis (NCAR)J. Edwards (IBM)B. Fox-Kemper (MIT,CU)E. Hunke (LANL)B. Kadlec (CU)D. Ivanova (LLNL)E. Jedlicka (ANL)E. Jessup (CU)R. Jacob (ANL)P. Jones (LANL)S. Peacock (NCAR)K. Lindsay (NCAR)W. Lipscomb (LANL)R. Loy (ANL)J. Michalakes (NCAR)A. Mirin (LLNL)M. Maltrud (LANL)J. McClean (LLNL)R. Nair (NCAR)M. Norman (NCSU)T. Qian (NCAR)M. Taylor (SNL)H. Tufo (NCAR)M. Vertenstein (NCAR)P. Worley (ORNL)M. Zhang (SUNYSB)

• Funding:– DOE-BER CCPP Program Grant

• DE-FC03-97ER62402• DE-PS02-07ER07-06• DE-FC02-07ER64340• B&R KP1206000

– DOE-ASCR• B&R KJ0101030

– NSF Cooperative Grant NSF01– NSF PetaApps Award

• Computer Time:– Blue Gene/L time:

NSF MRI GrantNCARUniversity of ColoradoIBM (SUR) program

BGW Consortium DaysIBM research (Watson)

LLNLStony Brook & BNL

– CRAY XT3/4 time:ORNLSandia

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Thanks! Any Questions?

11/18/08 50

Q. If you had a petascale computer

what would you do with it?

A. Use it as a prototype of an exascale computer.