1. CSE 260 Parallel Computation Allan Snavely, Henri Casanova asnavely@cs.ucsd.edu casanova@cs.ucsd.edu http://www.sdsc.edu/~allans/cs260/cs260.htm

2. Outline Introductions Why we need powerful computers Why powerful computers are parallel Issues in parallel performance Parallel computers, yesterday and today Class organization

3. Introductions Instructors: Allan Snavely, asnavely@cs.ucsd.edu, www.sdsc.edu/~allans Henri Casanova, casanova@cs.ucsd.edu, www.cs.ucsd.edu/~casanova/.edu www.sdsc.edu/~allans casanova@cs.ucsd.eduwww.cs.ucsd.edu/~casanova/ T.A.: Michael McCracken, mike@cs.ucsd.edu@cs.ucsd.edu Course web page: http://www.sdsc.edu/~allans/cs260/cs260.htm http://www.sdsc.edu/~allans/cs260/cs260.htm HPCS experiment: more at end of class today. Thanks to Kathy Yelick and Jim Demmel, and John Gilbert at UCB for some of these slides.

4. Why do we need powerful computers?

5. Simulation: The Third Pillar of Science Simulation: The Third Pillar of Science Traditional scientific and engineering paradigm: 1)Do theory or paper design. 2)Perform experiments or build system. Limitations: Too difficult -- build large wind tunnels. Too expensive -- build a throw-away passenger jet. Too slow -- wait for climate or galactic evolution. Too dangerous -- weapons, drug design, climate experiments. Computational science paradigm: 3)Use high performance computer systems to simulate the phenomenon. Base on known physical laws and efficient numerical methods.

6. Some Challenging Computations Science Global climate modeling Astrophysical modeling Biology: genomics; protein folding; drug design Computational Chemistry Computational Material Sciences and Nanosciences Engineering Crash simulation Semiconductor design Earthquake and structural modeling Computation fluid dynamics (airplane design) Combustion (engine design) Business Financial and economic modeling Transaction processing, web services and search engines Defense Nuclear weapons -- test by simulation Cryptography

7. Units of Measure in HPC High Performance Computing (HPC) units are: Flops: floating point operations Flop/s: floating point operations per second Bytes: size of data (double precision floating point number is 8) Typical sizes are millions, billions, trillions… MegaMflop/s = 10 6 flop/secMbyte = 10 6 byte (also 2 20 = 1048576) GigaGflop/s = 10 9 flop/secGbyte = 10 9 byte (also 2 30 = 1073741824) TeraTflop/s = 10 12 flop/secTbyte = 10 12 byte (also 2 40 = 10995211627776) PetaPflop/s = 10 15 flop/secPbyte = 10 15 byte (also 2 50 = 1125899906842624) ExaEflop/s = 10 18 flop/secEbyte = 10 18 byte

8. Global Climate Modeling Problem 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 Uses: -Predict major events, e.g., El Nino -Use in setting air emissions standards Source: http://www.epm.ornl.gov/chammp/chammp.html

9. Global Climate Modeling Computation 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

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

11. A 1000 Year Climate Simulation Warren Washington and Jerry Meehl, National Center for Atmospheric Research; Bert Semtner, Naval Postgraduate School; John Weatherly, U.S. Army Cold Regions Research and Engineering Lab Laboratory et al. http://www.nersc.gov/aboutnersc/pubs/bigsplash.pdf Demonstration of the Community Climate Model (CCSM2) A 1000-year simulation shows long-term, stable representation of the earth’s climate. 760,000 processor hours used Temperature change shown

12. Climate Modeling on the Earth Simulator System  Development of ES started in 1997 with the goal of enabling a comprehensive understanding of global environmental changes such as global warming.  26.58 Tflops on a global atmospheric circulation code. 26.58 Tflops on a global atmospheric circulation code.  35.86 Tflops (87.5% of peak performance) on Linpack benchmark.  Construction was completed February, 2002 and practical operation started March 1, 2002

13. Why are powerful computers parallel?

14. Tunnel Vision by Experts “I think there is a world market for maybe five computers.” Thomas Watson, chairman of IBM, 1943. “There is no reason for any individual to have a computer in their home” Ken Olson, president and founder of Digital Equipment Corporation, 1977. “640K [of memory] ought to be enough for anybody.” Bill Gates, chairman of Microsoft,1981. Slide source: Warfield et al.

15. Technology Trends: Microprocessor Capacity Moore’s Law: #transistors/chip doubles every 1.5 years Moore’s Law Microprocessors have become smaller, denser, and more powerful. Gordon Moore (co-founder of Intel) predicted in 1965 that the transistor density of semiconductor chips would double roughly every 18 months. Slide source: Jack Dongarra

16. How fast can a serial computer be? Consider the 1 Tflop sequential machine data must travel some distance, r, to get from memory to CPU to get 1 data element per cycle, this means 10^12 times per second at the speed of light, c = 3e8 m/s so r < c/10^12 =.3 mm Now put 1 TB of storage in a.3 mm^2 area each word occupies ~ 3 Angstroms^2, the size of a small atom r =.3 mm 1 Tflop 1 TB sequential machine

17. Scaling microprocessors What happens when feature size shrinks by a factor of x? Clock rate goes up by x actually a little less Transistors per unit area goes up by x 2 Die size also tends to increase typically another factor of ~x Raw computing power of the chip goes up by ~ x 4 ! of which x 3 is devoted either to parallelism or locality

18. “Automatic” Parallelism in Modern Machines Bit level parallelism within floating point operations, etc. Instruction level parallelism multiple instructions execute per clock cycle Memory system parallelism overlap of memory operations with computation OS parallelism multiple jobs run in parallel on commodity SMPs There are limits to all of these -- for very high performance, user must identify, schedule and coordinate parallel tasks

19. Number of transistors per processor chip

20. Bit-Level Parallelism Instruction-Level Parallelism Thread-Level Parallelism?

21. Issues in parallel performance

22. Locality and Parallelism Large memories are slow, fast memories are small Storage hierarchies are large and fast on average Parallel processors, collectively, have large, fast cache the slow accesses to “remote” data we call “communication” Algorithm should do most work on local data Proc Cache L2 Cache L3 Cache Memory Conventional Storage Hierarchy Proc Cache L2 Cache L3 Cache Memory Proc Cache L2 Cache L3 Cache Memory potential interconnects

23. Finding Enough Parallelism: Amdahl’s Law Suppose only part of an application seems parallel Amdahl’s law Let s be the fraction of work done sequentially, so (1-s) is the fraction parallelizable Let P = number of processors Speedup(P) = Time(1)/Time(P) <= 1/(s + (1-s)/P) <= 1/s Even if the parallel part speeds up perfectly, the sequential part limits overall performance.

24. Load Imbalance Load imbalance is the time that some processors in the system are idle due to insufficient parallelism (during that phase) unequal size tasks Examples of the latter adapting to “interesting parts of a domain” tree-structured computations fundamentally unstructured problems Algorithm needs to balance load

25. Parallel computers, yesterday and today

26. Dead supercomputers Top 500 list Flashmob computing (!?)

27. Parallel Computing Today Small class Beowulf cluster Japanese Earth Simulator machine

28. Course organization

29. Course overview Key ideas: Algorithms Programming models Performance Course outline – see home page

30. Resources Course home page: http://www.sdsc.edu/~allans/cs260/cs260.htm http://w Computing resources: 128-multi-streaming processor (MSP) Cray X1 with 512 GB of memory and 21 terabytes of disk. The X1, named Klondike at Arctic Region Supercomputing Center (ARSC) 1632 processor IBM Power4 SP: DataStar (SDSC) Return the course questionnaire so we can create accounts! No textbook – see course homepage for references

31. Requirements Four 2-week homework assignments First one is assigned today!!!!! Individual effort 40% of course grade Final project Significant parallel programming project Teams of three Teams should be interdisciplinary (this is how real parallel software is built) 50% of course grade Scribe notes for one lecture Due one week after lecture Sign up for a day to scribe 10% of course grade for scribing and class participation