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Rich Loft Director, Technology Development Computational and Information Systems LaboratoryPowerPoint Presentation

Rich Loft Director, Technology Development Computational and Information Systems Laboratory

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### Climate Change Research Epochs

### Dr. Henry Tufoand myself with “frost”(2005)

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

Rich Loft

Director, Technology Development

Computational and Information Systems Laboratory

National Center for Atmospheric Research

Main Points and Computing Technology We Need to Make Critical Climate Predictions in Time?

- 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.

Options for Application Acceleration and Computing Technology We Need to Make Critical Climate Predictions in Time?

- 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

- Bigger Timesteps
- 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

- Architecture - old tricks, new tricks… magic tricks

- More Threads

A Very Grand Challenge: and Computing Technology We Need to Make Critical Climate Predictions in Time? Coupled Models of the Earth System

~150 km

air column

water column

Viner (2002)

Typical Model Computation:

- 15 minute time steps

- 1 peta-flop per model year

There are 3.5 million timesteps in a century

Multicomponent Earth System Model and Computing Technology We Need to Make Critical Climate Predictions in Time?

Coupler

Land

Atmosphere

Ocean

Sea Ice

C/N

Cycle

Dyn.

Veg.

Land

Use

Ecosystem

& BGC

Gas chem.

Prognostic

Aerosols

Upper

Atm.

Ice

Sheets

- Software Challenges:
- Increasing Complexity
- Validation and Verification
- Understanding the Output

Key concept: A flexible coupling framework is critical!

Climate Change and Computing Technology We Need to Make Critical Climate Predictions in Time?

Credit: Caspar Amman

NCAR

IPCC AR4 - 2007 and Computing Technology We Need to Make Critical Climate Predictions in Time?

- IPCC AR4: “Warming of the climate system is un-equivocal” …
- …and it is “very likely” caused by human activities.
- Most of the observed changes over the past 50 years are now simulated by climate models adding confidence to future projections.
- Model Resolutions: O(100 km)

2007 and Computing Technology We Need to Make Critical Climate Predictions in Time?

Assess regional impacts

Simulate adaptation strategies

Simulate geoengineering solns

Reproduce historical trends

Investigate climate change

Run IPCC Scenarios

Before IPCCAR4 After

Curiosity Driven

Policy Driven

Where we want to go: and Computing Technology We Need to Make Critical Climate Predictions in Time? The Exascale Earth System Model Vision

Coupled 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

ESSL - The Earth & Sun Systems Laboratory

YIKES!

Compute Factors for ultra-high resolution Earth System Model and Computing Technology We Need to Make Critical Climate Predictions in Time?

(courtesy of John Drake, ORNL)

Why run-length: and Computing Technology We Need to Make Critical Climate Predictions in Time? global thermohaline circulation timescale: 3,000 years

Ocean component of CCSM (Collins et al, 2006) : O(1 km)

Eddy-resolving POP (Maltrud & McClean,2005)

Why High Resolution in the Ocean?1˚

0.1˚

High Resolution and the Land Surface : O(1 km)

Performance Improvements are not coming fast enough! : O(1 km)

…suggests 1010 to 1012 improvement will take 40 years

ITRS Roadmap: : O(1 km)feature size dropping 14%/year

By 2050 reaches the size of an atom – oops!

National Security Agency : O(1 km) - The power consumption of today's advanced computing systems is rapidly becoming the limiting factor with respect to improved/increased computational ability."

Chip Level Trends: Stagnant Clock Speed : O(1 km)

- 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

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

Moore’s Law -> More’s Law: : O(1 km)Speed-up through increasing parallelism

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

NCAR and University Colorado Partner : O(1 km)

to Experiment with Blue Gene/L

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

Current high resolution CCSM runs Modeling

- 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

- Current

168 sec. Modeling

120 sec.

52 sec.

ATM

[np=1664]

CPL

[np=384]

LND

[np=16]

ICE

[np=1800]

21 sec.

91 sec.

5416 processors

Current 0.5 CCSM “fuel efficient” configuration [franklin]OCN

[np=3600]

168 sec. Modeling

120 sec.

52 sec.

ATM

[np=1664]

OCN

[np=3600]

CPL

[np=384]

LND

[np=16]

ICE

[np=1800]

21 sec.

91 sec.

5416 processors

Efficiency issues in current 0.5 CCSM configurationUse Space Filling Curves (SFC) in POP, reduce processor count by 13%.

Load Balancing: Partitioning with Space Filling Curves Modeling

Partition for 3 processors

Space-filling Curve Partitioning Modelingfor Ocean Model running on 8 Processors

Static Load Balancing…

Key concept: no need to compute over land!

Ocean Model 1/10 Degree performance Modeling

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

168 sec. Modeling

120 sec.

52 sec.

ATM

[np=1664]

OCN

[np=3600]

CPL

[np=384]

LND

[np=16]

ICE

[np=1800]

21 sec.

91 sec.

5416 processors

Efficiency issues in Current CCSM 0.5 configurationUse wSFC in CICE, reduce Execution time by 2x.

Large domains @ low latitudes Modeling

Small domains @ high latitudes

Static, Weighted Load Balancing Example:Sea Ice Model CICE4 @ 1° on 20 processorsCourtesy of John Dennis

168 sec. Modeling

120 sec.

52 sec.

ATM

[np=1664]

OCN

[np=3600]

CPL

[np=384]

LND

[np=16]

ICE

[np=1800]

21 sec.

91 sec.

5416 processors

Efficiency issues in current 0.5 CCSM configuration: CouplerUnresolved scalability issues in Coupler – Options: Better interconnect,Nested grids,

PGAS language paradigm

168 sec. Modeling

120 sec.

52 sec.

ATM

[np=1664]

OCN

[np=3600]

CPL

[np=384]

LND

[np=16]

ICE

[np=1800]

21 sec.

91 sec.

5416 processors

Efficiency issues in current 0.5 CCSM configuration: atmospheric componentScalability limitation in 0.5° fv-CAM[MPI] – shift to hybrid OpenMP/MPI version

62 sec. Modeling

62 sec.

31 sec.

ATM

[np=5200]

CPL

[np=384]

LND

[np=40]

ICE

[np=8120]

21 sec.

10 sec.

19460 processors

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

[np=6100]

Action: Run hybrid atmospheric model

62 sec. Modeling

62 sec.

31 sec.

ATM

[np=5200]

CPL

[np=384]

LND

[np=40]

ICE

[np=8120]

21 sec.

10 sec.

14260 processors

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

[np=6100]

Action: Thread ice model

Ne=16 Cube Sphere Modeling

Showing degree of

non-uniformity

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

Scalable Numerical Method: ModelingHigh-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

HOMME: Computational Mesh Modeling

- 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

Partitioning a cube-sphere on Modeling8 processors

Partitioning a cubed-sphere on Modeling8 processors

Aqua-Planet CAM/HOMME Dycore Modeling

Full CAM Physics/HOMME Dycore

Parallel 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

60 sec. Modeling

60 sec.

47 sec.

HOMME ATM

[np=24000]

CPL

[np=3840]

LND

[np=320]

ICE

[np=16240]

8 sec.

5 sec.

30000 processors

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

[np=6000]

Action: insert scalable atmospheric dycore

Using a bigger parallel machine Modelingcan’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 Modeling

- 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

- Bigger Timesteps
- 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

- Architecture - old tricks, new tricks… magic tricks

- More Threads

Accelerator Research Modeling

- 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

DG+NH+AMR Modeling

- 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

SLIM ocean model Modeling

- 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

Meanwhile - the clock is ticking (SIParCS)

Contributors: (SIParCS)

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 Grant

NCAR

University of Colorado

IBM (SUR) program

BGW Consortium Days

IBM research (Watson)

LLNL

Stony Brook & BNL

CRAY XT3/4 time:

ORNL

Sandia

The Size of the Interdisciplinary/Interagency Team Working on Climate ScalabilityThanks! (SIParCS) Any Questions?

Q. If you had a petascale computer (SIParCS)what would you do with it?

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

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