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K.NARAYANA08Q61A0575        2
ExecutionEXAMPLE :-•   main(){•    for (int i = 0; d.get_meaning(i,s) != 0; ++i)•      cout << (i+1) << ": " << s << "n";•...
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For example:
Traditionally software has been written for            serial computation.                                              7
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Load Balancing                 10
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Parallel Computer Memory       Architectures:
Parallel Computer Memory       Architectures:   Distributed Memory
There are different ways to     classify parallel computers•    classified along the two independent dimensions of    Inst...
SISD       15
SIMD       16
MISD       17
MIMD       18
ADVANTAGES•In the simplest sense, parallel computing is    To be run using multiple CPUs    A problem is broken into discr...
Parallel computingOverheads      Synchronization      Problem decomposition      Data Dependencies                     ...
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Problem decomposition
Data Dependencies•   1: function Dep(a, b)   •   1: function NoDep(a, b)•   2: c := a·b             •   2:   c := a·b•   3...
Conclusion•   Parallel computing is fast.•   There are many different approaches and models of parallel    computing.•   P...
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parallel computing
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parallel computing

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parallel computing

  1. 1. 1
  2. 2. K.NARAYANA08Q61A0575 2
  3. 3. ExecutionEXAMPLE :-• main(){• for (int i = 0; d.get_meaning(i,s) != 0; ++i)• cout << (i+1) << ": " << s << "n";• return 0;}
  4. 4. 4
  5. 5. 5
  6. 6. For example:
  7. 7. Traditionally software has been written for serial computation. 7
  8. 8. 8
  9. 9. Load Balancing 10
  10. 10. 11
  11. 11. Parallel Computer Memory Architectures:
  12. 12. Parallel Computer Memory Architectures: Distributed Memory
  13. 13. There are different ways to classify parallel computers• classified along the two independent dimensions of Instruction and Data• SISD – Single Instruction, Single Data• SIMD – Single Instruction, Multiple Data• MISD – Multiple Instruction, Single Data• MIMD – Multiple Instruction, Multiple Data
  14. 14. SISD 15
  15. 15. SIMD 16
  16. 16. MISD 17
  17. 17. MIMD 18
  18. 18. ADVANTAGES•In the simplest sense, parallel computing is To be run using multiple CPUs A problem is broken into discrete parts that can be solved concurrently Each part is further broken down to a series of instructions Instructions from each part execute simultaneously on different CPUs
  19. 19. Parallel computingOverheads  Synchronization  Problem decomposition  Data Dependencies 20
  20. 20. 21
  21. 21. Problem decomposition
  22. 22. Data Dependencies• 1: function Dep(a, b) • 1: function NoDep(a, b)• 2: c := a·b • 2: c := a·b• 3: d := 3·c • 3: d := 3·b• 4: end function • 4: e := a+b • 5: end function
  23. 23. Conclusion• Parallel computing is fast.• There are many different approaches and models of parallel computing.• Parallel computing is the future of computing.• Solve larger problems
  24. 24. 25

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