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parallel computing

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Transcript

  • 1. 1
  • 2. K.NARAYANA08Q61A0575 2
  • 3. ExecutionEXAMPLE :-• main(){• for (int i = 0; d.get_meaning(i,s) != 0; ++i)• cout << (i+1) << ": " << s << "n";• return 0;}
  • 4. 4
  • 5. 5
  • 6. For example:
  • 7. Traditionally software has been written for serial computation. 7
  • 8. 8
  • 9. Load Balancing 10
  • 10. 11
  • 11. Parallel Computer Memory Architectures:
  • 12. Parallel Computer Memory Architectures: Distributed Memory
  • 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. SISD 15
  • 15. SIMD 16
  • 16. MISD 17
  • 17. MIMD 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. Parallel computingOverheads  Synchronization  Problem decomposition  Data Dependencies 20
  • 20. 21
  • 21. Problem decomposition
  • 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. 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. 25

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