Parallel Processing (CM0323)
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The objective of this course is to give you some level of confidence in parallel programming techniques, algorithms and tools. At the end of the course, you would we hope be in a position to apply parallelization to your project areas and beyond, and to explore new avenues of research in the area of parallel programming. The course covers parallel programming tools, constructs, models, algorithms, parallel matrix computations, parallel programming optimizations, scientific applications and parallel system software. Direct linear algebra pdf Sparse LA. Sections In Parallel Computing 24 In Journal of Parallel and Distributed Computing,
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C++ CoreHard Spring 2017: Dori Exterman. Considerations for choosing the parallel computing...
This is not a good ratio! Quinn Block Matrix Multiplication For example there are processes, then matrix multiplication is done by dividing A and B into p blocks of size k x k. Quinn Algorithms for Multicomputers: Row-Column Oriented Algorithm Partition Matrix A into rows and B into columns n is a power of 2 and we are executing algorithm on an n- processor hypercube : One imaginable parallelization: Parallelize the outer loop i All parallel processes access column 0 of b, then column 1 of b, etc. This results in a sequence of broadcast steps each having logn on an n-processor hypercube refer to chapter 6, p. Quinn Row-Column Oriented Algorithm Design Strategy Eliminate contention for shared resources by changing temporal order of data accesses. New Solution for a multicomputer: Change the order in which the algorithm computes the elements of each row of C Processes are organized as a ring.
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