• Problem is to compute:f(latitude, longitude, elevation, time)

temperature, pressure, humidity, wind velocity

• Approach:– Discretize the domain, e.g., a measurement point every 10 km– Devise an algorithm to predict weather at time t+1 given t

Source: http://www.epm.ornl.gov/chammp/chammp.html

• Uses:- Predict major events,

e.g., El Nino

- Use in setting air emissions standards

Weather Forecasting

An accurate long-range forecast requires huge amounts of cells and hence computations.

Case Study: Global Climate Modelling earth’s surface is approximately 5 x 108 km2

Considering one cell per square km with 15 levels ( ground level up to 14 km high )

6 data values ( update once every minute ) : humidity, temperature, wind, latitude, longitude and height

Throughput: 3 Gigabytes of data per second

• One piece is modeling the fluid flow in the atmosphere– Solve Navier-Stokes problem

• Roughly 100 Flops per grid point with 1 minute timestep

• Computational requirements:– To match real-time, need 5x 1011 flops in 60 seconds = 8 Gflop/s

– Weather prediction (7 days in 24 hours) 56 Gflop/s

– Climate prediction (50 years in 30 days) 4.8 Tflop/s

– To use in policy negotiations (50 years in 12 hours) 288 Tflop/s

• To double the grid resolution, computation is at least 8x • State of the art models require integration of atmosphere,

ocean, sea-ice, land models, plus possibly carbon cycle, geochemistry and more

• Current models are coarser than this

Global Climate Modeling Computation

Weather Forecasting

Computer Computer VisualisatioVisualisation of a n of a HurricaneHurricane

High Resolution Climate Modeling on NERSC-3 – P. Duffy,

et al., LLNL

Protein Folding One of the major challenges in molecular biology. Proteins perform over a thousand different jobs.

( As enzymes they accelerate reactions. They also carry oxygen and antibodies to fight disease. )

Before proteins can go to work they must fold into the correct shape.( The string of amino acids in the protein twist and fold to form the final protein )

Scientists are using supercomputers to discover the rules that describe why a string of amino acids folds into a particular protein.

Protein FoldingResearchers at the Researchers at the Pittsburgh Computing Pittsburgh Computing Center tracked the Center tracked the folding of a small folding of a small protein (300 amino protein (300 amino acids) in water ( ~ acids) in water ( ~ 32000 atoms ).32000 atoms ).

Folding time: Folding time: 1 1 millisecondmillisecond

#FLOPS required: #FLOPS required: 3 x 3 x 10102222

With a With a PetaFLOPPetaFLOP computer the computer the simulation would take simulation would take a year.a year.

IBM are funding a $100M project called IBM are funding a $100M project called The Blue The Blue Gene ProjectGene Project to build a 1 PetaFLOP/s computer to build a 1 PetaFLOP/s computer ( PetaScale Computing – 10( PetaScale Computing – 101515 Flops/s) Flops/s)

The Production of Toy Story

• 140,000 frames rendered for full-length feature film.

• 10,000 seconds required to render each frame.

• ~ 1017 operations

• Operations were distributed over dozens of Sun workstations, ~ 10 MIPS ( millions of instructions per second ) per Sun.

What is Parallel Architecture?

A parallel computer is a collection of processing elements that cooperate to solve large problems fast

• Some broad issues:– Resource Allocation:

• how large a collection? • how powerful are the elements?• how much memory?

– Data access, Communication and Synchronization• how do the elements cooperate and communicate?• how are data transmitted between processors?• what are the abstractions and primitives for cooperation?

– Performance and Scalability• how does it all translate into performance?• how does it scale?

Parallelism:• Provides alternative to faster clock for

performance• Applies at all levels of system design• Is a fascinating perspective from which to view

architecture• Is increasingly central in information

processing

Why Study Parallel Architecture?

Architectural TrendsGreatest trend in VLSI generation is increase in

parallelism– Up to 1985: bit level parallelism: 4-bit -> 8 bit -> 16-bit

• slows after 32 bit

• adoption of 64-bit now under way, 128-bit far (not performance issue)

• great inflection point when 32-bit micro and cache fit on a chip

– Mid 80s to mid 90s: instruction level parallelism

• pipelining and simple instruction sets, + compiler advances (RISC)

• on-chip caches and functional units => superscalar execution

• greater sophistication: out of order execution, speculation, prediction

– to deal with control transfer and latency problems

– Next step: thread level parallelism

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How far will ILP go?

• Infinite resources and fetch bandwidth, perfect branch prediction and renaming

– real caches and non-zero miss latencies