9. 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

10. 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 10 8 km 2  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

11. 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 10 11 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

12. Weather Forecasting Computer Visualisation of a Hurricane

13. High Resolution Climate Modeling on NERSC-3 – P. Duffy, et al., LLNL

20. 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.

21. Protein Folding Researchers at the Pittsburgh Computing Center tracked the folding of a small protein (300 amino acids) in water ( ~ 32000 atoms ). Folding time: 1 millisecond #FLOPS required: 3 x 10 22 With a PetaFLOP computer the simulation would take a year. IBM are funding a $100M project called The Blue Gene Project to build a 1 PetaFLOP/s computer ( PetaScale Computing – 10 15 Flops/s)

23. The Production of Toy Story 140,000 frames rendered for full-length feature film. 10,000 seconds required to render each frame. ~ 10 17 operations Operations were distributed over dozens of Sun workstations, ~ 10 MIPS ( millions of instructions per second ) per Sun.

33. 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?

34. 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?

37. Architectural Trends Greatest 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

38. How far will ILP go? Infinite resources and fetch bandwidth, perfect branch prediction and renaming –real caches and non-zero miss latencies