Parallel Algorithms

Wishlist Share
Share Course
Page Link
Share On Social Media

About Course

Parallel Algorithms. A conventional algorithm uses a single processing element. A parallel algorithm assumes that there are multiple processors.

These processors may communicate with each other using shared memory or an interconnection network. An algorithm designed for a large number (for example, a polynomial in the problem size) of processors can be simulated on a machine with a small number of a processor for a trade-off on time, and therefore is of practical value, while at the same time allowing us to test the limits of parallelism.

Many algorithmic design techniques in the parallel setting will be explored. Parallel complexity theory will also be briefly studied.

Join now for the Parallel Algorithms course !

Show More

What Will You Learn?

  • Week 1: Theoretical models: PRAM, interconnection networks
  • Week 2: Performance of parallel algorithms, Basic techniques
  • Week 3: Basic techniques
  • Week 4: Comparator Networks.
  • Week 5: Optimal List ranking, applications
  • Week 6: Algorithms for searching, merging, and sorting. Cole’s Merge Sort
  • Week 7: Cole’s Merge Sort(cont’d), Graph algorithms
  • Week 8: Graph algorithms (cont’d)
  • Week 9: Sorting in meshes, Hypercube algorithms, Butterfly network, CCC, Benes network
  • Week 10: Butterfly network, CCC, Benes network etc
  • Week 11: Limits to parallelizability. Lower bounds
  • Week 12: Limits to parallelizability. NC-reductions, P-completeness.

Course Content

Parallel Algorithms

  • Parallel Algorithms [Intro video]
    00:00
  • Lec 1: Shared Memory Models – 1
    00:00
  • Lec 2: Shared Memory Models – 2
    00:00
  • Lec 3: Interconnection Networks
    00:00
  • Lec 4: Cost and Optimality
    00:00
  • Lec 5: Basic Techniques 1
    00:00
  • Lec 6: Basic Techniques 2
    00:00
  • Lec 7: Basic Techniques 3
    00:00
  • Lec 8: Basic Techniques 4
    01:03:58
  • Lec 9: Basic Techniques 5
    53:54
  • Lec 10: Odd Even Merge Sort (OEMS)
    00:00
  • Lec 11: OEMS, Bitonic-Sort-Merge Sort (BSMS)
    00:00
  • Lec 12: BSMS, Optimal List Colouring
    00:00
  • Lec 13: Description
    00:00
  • Lec 14: Analysis
    00:00
  • Lec 15: Applications
    57:08
  • Lec 16: Applications
    00:00
  • Lec 17: Fast optimal merge algorithm
    00:00
  • Lec 18: High level Description
    00:00
  • Lec19: Cole’s Merge Sort: Details
    00:00
  • Lec20: Analysis of Cole’s Merge Sort; Lower bound for sorting
    00:00
  • Lec21: Sorting Lower bound; Connected Components
    00:00
  • Lec 22: Connected Components (CREW)
    00:00
  • Lec 23: Connected Components, Vertex Colouring
    00:00
  • Lec 24: Sorting on a 2D mesh
    00:00
  • Lec 25: Sorting on a 2D mesh
    00:00
  • Lec 26: Sorting, Offline routing on a 2D mesh
    00:00
  • Lec 27: Sorting on a 3D mesh
    00:00
  • Lec 28: Mesh of Trees, Hypercube
    00:00
  • Lec 29: Hypercube cont’d
    00:00
  • Lec 30: Hypercube cont’d, butterfly network
    00:00
  • Lec 31: Butterfly, CCC and Benes Networks
    00:00
  • Lec 32: Butterfly, CCC and Benes Networks
    00:00
  • Lec 33: Shuffle Exchange Graphs, de Bruijn Graphs
    00:00
  • Lec 34: SEG, dBG (cont’d)
    00:00
  • Lec 35: Circuit Value Problem is P-complete for NC-reductions
    00:00
  • Lec 36: Ordered DFS is P-complete for NC-reductions
    00:00
  • Lec 37: Max Flow is P-complete for NC-reductions
    00:00

Student Ratings & Reviews

No Review Yet
No Review Yet
ResearcherStore

Want to receive push notifications for all major on-site activities?