1. 1 Lecture 1 Introduction to Parallel Computing Parallel Computing Fall 2008

2. 2 Contents Introduction to Parallel Computing Acknowledgments for today’s lecture Jack Dongarra (U. Tennessee) --- CS 594 slides from Spring 2008 — http://www.cs.utk.edu/%7Edongarra/WEB-PAGES/cs594- 2008.htm Kathy Yelick (UC Berkeley) --- CS 267 slides from Spring 2007 — http://www.eecs.berkeley.edu/~yelick/cs267_sp07/lectures Slides accompanying course textbook —http://www- users.cs.umn.edu/~karypis/parbook/ Vivek Sarkar(Rice University) – http://www.owlnet.rice.edu/~comp422/lecture-notes/comp422- lec1-s08-v1.pdf Alexandros Gerbessiotis (New Jersey Institute of Technology)

3. 3 Why parallel computing? – computational modeling and simulation “Computational modeling and simulation are among the most significant developments in the practice of scientific inquiry in the 20th Century. Within the last two decades, scientific computing has become an important contributor to all scientific disciplines. It is particularly important for the solution of research problems that are insoluble by traditional scientific theoretical and experimental approaches, hazardous to study in the laboratory, or time consuming or expensive to solve by traditional means” — “Scientific Discovery through Advanced Computing” DOE Office of Science, 2000

4. 4 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 experimentation. Computational science paradigm: 3) Use high performance computer systems to simulate the phenomenon – Base on known physical laws and efficient numerical methods.

5. 5 Some Particularly Challenging Computations Science —Global climate modeling —Biology: genomics; protein folding; drug design —Astrophysical modeling —Computational Chemistry —Computational Material Sciences and Nanosciences Engineering —Semiconductor design —Earthquake and structural modeling —Computation fluid dynamics (airplane design) —Combustion (engine design) —Crash simulation Business —Financial and economic modeling —Transaction processing, web services and search engines Defense —Nuclear weapons -- test by simulations —Cryptography

6. 6 Technology Trends: Microprocessor Capacity 2X transistors/Chip Every 1.5 years Called “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

7. 7 More Limits: How fast can a serial computer be? Consider the 1 Tflop/s sequential machine: Data must travel some distance, r, to get from memoryto CPU. To get 1 data element per cycle, this means 1012times per second at the speed of light, c = 3x108 m/s.Thus r < c/1012 = 0.3 mm. Now put 1 Tbyte of storage in a 0.3 mm x 0.3 mm area: Each bit occupies about 1 square Angstrom, or the size of a small atom. No choice but parallelism 1 Tflop/s, 1 Tbyte sequential machine r = 0.3 mm

8. 8 Why Parallelism is now necessary for Mainstream Computing Chip density is continuing increase ~2x every 2 years —Clock speed is not —Number of processor cores have to double instead There is little or no hidden parallelism (ILP) to be found Parallelism must be exposed to and managed by software Source: Intel, Microsoft (Sutter) and Stanford (Olukotun, Hammond)

9. 9 Fundamental limits on Serial Computing: Three “Walls” Power Wall —Increasingly, microprocessor performance is limited byachievable power dissipation rather than by the number of available integrated-circuit resources (transistors and wires). Thus, the only way to significantly increase the performance of microprocessors is to improve power efficiency at about the same rate as the performance increase. Frequency Wall —Conventional processors require increasingly deeper instruction pipelines to achieve higher operating frequencies. This technique has reached a pointof diminishing returns, and even negative returns if power is taken into account. Memory Wall —On multi-gigahertz symmetric processors --- even those with integrated memory controllers --- latency to DRAM memory is currently approaching 1,000 cycles. As a result, program performance is dominated by the activity of moving data between main storage (the effective-address space that includes main memory) and the processor.

10. 10 What is Parallel computing? Parallel computing involves performing parallel tasks using more than one computer. Example in real life with related principles -- book shelving in a library Single worker P workers with each worker stacking n/p books, but with arbitration problem(many workers try to stack the next book in the same shelf.) P workers with each worker stacking n/p books, but without arbitration problem (each worker work on a different set of shelves)

11. 11 Important Issues in parallel computing Task/Program Partitioning. How to split a single task among the processors so that each processor performs the same amount of work, and all processors work collectively to complete the task. Data Partitioning. How to split the data evenly among the processors in such a way that processor interaction is minimized. Communication/Arbitration. How we allow communication among different processors and how we arbitrate communication related conflicts.

12. 12 Challenges 1. Design of parallel computers so that we resolve the above issues. 2. Design, analysis and evaluation of parallel algorithms run on these machines. 3. Portability and scalability issues related to parallel programs and algorithms 4. Tools and libraries used in such systems.

13. 13 Units of Measure in HPC High Performance Computing (HPC) units are: —Flop: floating point operation —Flops/s: floating point operations per second —Bytes: size of data (a double precision floating point number is 8) Typical sizes are millions, billions, trillions… Mega Mflop/s = 106 flop/sec Mbyte = 220 = 1048576 ~ 106 bytes Giga Gflop/s = 109 flop/sec Gbyte = 230 ~ 109 bytes Tera Tflop/s = 1012 flop/sec Tbyte = 240 ~ 1012 bytes Peta Pflop/s = 1015 flop/sec Pbyte = 250 ~ 1015 bytes Exa Eflop/s = 1018 flop/sec Ebyte = 260 ~ 1018 bytes Zetta Zflop/s = 1021 flop/sec Zbyte = 270 ~ 1021 bytes Yotta Yflop/s = 1024 flop/sec Ybyte = 280 ~ 1024 bytes See www.top500.org for current list of fastest machines

14. 14 What is a parallel computer? A parallel computer is a collection of processors that cooperatively solve computationally intensive problems faster than other computers. Parallel algorithms allow the efficient programming of parallel computers. This way the waste of computational resources can be avoided. Parallel computer v.s. Supercomputer supercomputer refers to a general-purpose computer that can solve computational intensive problems faster than traditional computers. A supercomputer may or may not be a parallel computer.

15. 15 Parallel Computers: Past and Present 1980’s Cray supercomputer was 20-100 times faster than other computers(main frames, minicomputers) in use. (The price of supercomputer is 10 times other computers – worth it) 1990’s “Cray”-like CPU is 2-4 times as fast as a microprocessor. (The price of supercomputer is 10-20 times a microcomputer – make no sense) The solution to the need for computational power is a massively parallel computers, where tens to hundreds of commercial off-the-shelf processors are used to build a machine whose performance is much greater than that of a single processor.

16. 16 Scale of Today’s HPC Systems

17. 17 CSI’s High Performance Center Athena – 97 node Dell PowerEdge Cluster 1 Gbit ethernet internal network – 96 Compute nodes (PowerEdge 1850) Two Intel Xeon dual processor chips operating at 2.8 GHz 8 Gbytes of memory – 1 Head node (PowerEdge 2850) Two Intel Xeon dual processor chips operating at 2.8 GHz 4 Gbytes of memory Zeus – 9 node Dell PowerEdge Cluster 1 Gbit ethernet internal network – 8 Compute nodes (PowerEdge 1850) Two Intel Xeon dual processor chips operating at 2.8 GHz 8 Gbytes of memory – 1 Head node (PowerEdge 1850) Two Intel Xeon dual processor chips operating at 2.8 GHz 8 Gbytes of memory File system – 4.5 Terabyte file system (to be upgraded in October to 18 terabytes) Each user account will be assigned at 30 gigabyte home directory)

18. 18 CSI’s cluster – Typhon 32 Dell Power Edge 2650 machines and each machine has two Intel(R) Xeon(TM) CPU of 2.80GHz and 2GB RAM. MPI library (MPICH 1.27, Intel MPI 2.0, LAM MPI 7.14)

19. 19 Applications of Parallel Computing Astrophysics(explore the evoluation of galaxies, analysis of extremely large datasets from telescope). Material sciences (eg superconductivity). Biology, biochemistry, gene sequencing. Medicine and human organ modeling (eg. to study the effects and dynamics of a heart attack, developing new drugs and cures for diseases). Global weather prediction. Visualization (eg movie industry, 3D animation). Data Mining (optimizing business and marketing decisions). Computational-Fluid Dynamics (CFD) for aircraft and automotive vehicle design. Computer security, cryptography

20. 20 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+δt 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

21. 21 Global Climate Modeling Computation One piece is modeling the fluid flow in the atmosphere — Solve Navier-Stokes equations – Roughly 100 Flops per grid point with 1 minute timestep Computational requirements: —To match real-time, need 5 x 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 8x to 16x 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

22. 22 What is a parallel algorithm? A parallel algorithm is an algorithm designed for a parallel computer.

23. 23 Questions when combining processor power How does one combine processors efficiently? Do processors work independently? Do they cooperate? If they cooperate how do they interact with each other? How are the processors interconnected? How can we make programs portable? How does one program such machines so that programs run efficiently and do not waster resourses?

24. 24 End of lecture 1 Thank you!