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Some of the homework help topics include:

  • Parallel computers
  • Types of parallel computers
  • Architectural features

Parallel execution time, Time complexity, Debugging and evaluating parallel programs , Embarrassingly parallel computations.

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Embarrassingly parallel examples, Geometrical transformations of images

It is also termed as concurrent algorithm. It is an algorithm where different pieces of program can be divided and each piece is executed at a time to different processing device & then they are combined together to get the correct result.
Communication of Parallel processes:
a) Shared memory
b) Message passing
Parallel Algorithm:
  • Finite differences
  • Pair wise interactions
  • Search – it creates tasks dynamically during execution of the program.
  • Parameter study – it has fixed number of tasks where different task performs different functions.
Parallel algorithms on sequences and strings: 
  • Scan (prefix sums) : It takes a binary associative operator & an array and on processing it returns a new array in which each element of new array has sum of all previous elements.
  • List ranking : It takes a linked list & defines the position of each element in the list.
  • Sorting: there are various sorting algorithm like quick sort, insertion sort, selection sort.
  • Merging
  • Searching
  • String matching
  • Parallel algorithms on trees and graphs
  • Connected components problem: it takes an undirected graph as an input. It returns components that are connected by an edge. This can be achieved using breadth-first-search or depth-first-search.
  • Shortest path algorithm: they are mainly used to calculate the shortest distance between two different nodes.
Parallel algorithms for Computational Geometry: 
  • Convex hull
  • Closest pairs
  • Delaunay triangulation
Parallel algorithms for Numerical Computing
  • Fourier transform, Dense matrix operations, Sparse matrix operations, N-body code, Critical paths, work and span, and the relation to Amdahl’s law (cross-reference SF/Performance) Speed-up and scalability Naturally (embarassingly) parallel algorithms Parallel algorithmic patterns (divide-and-conquer, map and reduce, others) Specific algorithms (e.g., parallel MergeSort).
  • Parallel graph algorithms (e.g., parallel shortest path, parallel spanning tree) (cross-reference  AL/Algorithmic Strategies/Divide-and-conquer) Producer-consumer and pipelined algorithms  Define “critical path”, “work”, and “span”  work and span,  critical path , parallel execution diagram speed-up ,algorithm’s scalability ,independent tasks , naturally parallelized.
  • Parallel divide-and-conquer and/or graph algorithm and empirically sequential analog ,via map and  reduce operations , producer-consumer paradigm , pipelining ,parallelization ,producer-consumer algorithms mechanisms , addressing.
Parallel programming: 
  • Mandelbrot set, Monte carlo methods, Partitioning and divide-and-conquer strategies, Divide-and-conquer examples, Sorting using bucket sort, Numerical integration, N-body problem, Pipelined computations, Computing platform for pipelined applications, Adding numbers, Sorting numbers, Prime number generation, Solving a system of linear equations
  • Synchronous computations, Barrier and implementations, Local synchronization, Deadlock, Synchronized computations, Data parallel computations, Synchronous iteration, Solving a system of linear equations by iteration
  • Heat distribution problem, Cellular automata, Load balancing and termination detection, Dynamic load balancing, Distributed termination detection algorithms, Programming with shared memory, Specifying parallelism, Sharing data
  • Language constructs for parallelism, Dependency analysis, Shared data in systems with caches, Algorithms and applications, Sorting algorithms, Rank sort, Bubble sort and odd-even transposition sort, Two-dimensional sorting
  • Mergesort, Quicksort, Odd-even mergesort, Bitonic mergesort, Numerical algorithms, Matrix addition, Matrix and matrix-vector multiplication, Relationship of matrices to linear equations, Implementing matrix multiplication, Direct implementation, Recursive implementation, Mesh implementation, Solving a system of linear equations linear equations
  • Gaussian elimination – parallel implementation, Iterative methods, Jacobi iteration, Faster convergence methods, Image processing, Low-level image processing, Smoothing, Sharpening,, Noise reduction, Mean, Median, Weighted masks
  • Edge detection, Edge detection masks, The hough transform, Transformation into the frequency domain, Discrete fourier transform, Fast fourier transform, Searching and optimization, Branch-and-bound search, Genetic algorithms, Hill climbing,
  • Performance metrics, scalability and overheads, Classification of algorithms, architectures and applications: searching, divide and conquer, data parallel. Static and dynamic, message passing and shared memory, systolic, Sorting and searching algorithms: mergesort, quicksort and bitonic sort, implementation on different architectures. Parallel depth-first and breadth-first search techniques.
  • Matrix algorithms: striping and partitioning, matrix multiplication, linear equations, eigenvalues, dense and sparse techniques, finite element and conjugate gradient methods, Optimisation: graph problems, shortest path and spanning trees. Dynamic programming, knapsack problems, scheduling. element methods, Synthesis of parallel algorithms: algebraic methods, pipelines, homomorphisms.


COM S 526 Introduction to Parallel Algorithms and Programming: 

  • Models of parallel computation
  • performance measures
  • basic parallel constructs 
  • communication primitives
  • parallel programming using MPI
  • parallel algorithms  
  • matrix tree 
  • graph problems
  • fast Fourier transforms


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