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CSE 260 Parallel Computation. Allan Snavely, Henri Casanova asnavely@cs.ucsd.edu casanova@cs.ucsd.edu http://www.sdsc.edu/~allans/cs260/cs260.htm. Outline. Introductions Why we need powerful computers Why powerful computers are parallel Issues in parallel performance

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cse 260 parallel computation

CSE 260Parallel Computation

Allan Snavely, Henri Casanova

asnavely@cs.ucsd.edu

casanova@cs.ucsd.edu

http://www.sdsc.edu/~allans/cs260/cs260.htm

outline
Outline
  • Introductions
  • Why we need powerful computers
  • Why powerful computers are parallel
  • Issues in parallel performance
  • Parallel computers, yesterday and today
  • Class organization
introductions
Introductions
  • Instructors: Allan Snavely, asnavely@cs.ucsd.edu, www.sdsc.edu/~allans Henri Casanova, casanova@cs.ucsd.edu, www.cs.ucsd.edu/~casanova/
  • T.A.: Michael McCracken, mike@cs.ucsd.edu
  • Course web page: 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.

simulation the third pillar of science
Simulation: The Third Pillar of Science
  • Traditional scientific and engineering paradigm:
    • Do theory or paper design.
    • 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:
    • Use high performance computer systems to simulate the phenomenon.
      • Base on known physical laws and efficient numerical methods.
some challenging computations
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
units of measure in hpc
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…

Mega Mflop/s = 106 flop/sec Mbyte = 106 byte

(also 220 = 1048576)

Giga Gflop/s = 109 flop/sec Gbyte = 109 byte

(also 230 = 1073741824)

Tera Tflop/s = 1012 flop/sec Tbyte = 1012 byte

(also 240 = 10995211627776)

Peta Pflop/s = 1015 flop/sec Pbyte = 1015 byte

(also 250 = 1125899906842624)

Exa Eflop/s = 1018 flop/sec Ebyte = 1018 byte

global climate modeling problem
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

global climate modeling computation
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 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
a 1000 year climate simulation
A 1000 Year Climate Simulation
  • 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
  • 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
slide12

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.
  • Construction was completed February, 2002 and practical operation started March 1, 2002
  • 35.86 Tflops (87.5% of peak performance) on Linpack benchmark.
  • 26.58 Tflops on a global atmospheric circulation code.
tunnel vision by experts
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.

technology trends microprocessor capacity
Technology Trends: Microprocessor Capacity

Moore’s Law

Moore’s Law: #transistors/chip doubles every 1.5 years

Gordon Moore (co-founder of Intel) predicted in 1965 that the transistor density of semiconductor chips would double roughly every 18 months.

Microprocessors have become smaller, denser, and more powerful.

Slide source: Jack Dongarra

how fast can a serial computer be
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

1 Tflop 1 TB sequential machine

r = .3 mm

scaling microprocessors
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 x2
  • Die size also tends to increase
    • typically another factor of ~x
  • Raw computing power of the chip goes up by ~ x4!
    • of which x3is devoted either to parallelism or locality
automatic parallelism in modern machines
“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

number of transistors per processor chip20
Number of transistors per processor chip

Instruction-Level

Parallelism

Thread-Level

Parallelism?

Bit-Level

Parallelism

locality and parallelism
Locality and Parallelism

Conventional

Storage

Hierarchy

Proc

Proc

Proc

  • 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

Cache

Cache

Cache

L2 Cache

L2 Cache

L2 Cache

L3 Cache

L3 Cache

L3 Cache

potential

interconnects

Memory

Memory

Memory

finding enough parallelism amdahl s law
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.
load imbalance
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
slide26
Dead supercomputers
  • Top 500 list
  • Flashmob computing (!?)
slide27

Japanese Earth Simulator machine

Parallel Computing Today

Small class Beowulf cluster

course overview
Course overview
  • Key ideas:
    • Algorithms
    • Programming models
    • Performance
  • Course outline – see home page
resources
Resources
  • Course home page: http://www.sdsc.edu/~allans/cs260/cs260.htm
  • 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
requirements
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