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Parallel Computing Glib Dmytriiev

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What is Serial Computing? Traditionally, software has been written for serial computation: To be run on a single computer having a single Central Processing Unit (CPU); A problem is broken into a discrete series of instructions. Instructions are executed one after another. Only one instruction may execute at any moment in time.

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Serial Computing

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What is Parallel Computing? In the simplest sense, parallel computing is the simultaneous use of multiple compute resources to solve a computational problem: 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

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Parallel Computing

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The compute resources might be: A single computer with multiple processors or cores; An arbitrary number of computers connected by a network; A combination of both; A special video card.

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Hardware. Cluster A computer cluster is a group of linked computers, working together closely thus in many respects forming a single computer. The components of a cluster are commonly, but not always, connected to each other through fast local area networks.

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K Computer

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is a supercomputer being produced by Fujitsu at the RIKEN Advanced Institute for Computational Science campus in Kobe, Japan K became the world's fastest supercomputer in June 2011, as recorded by the TOP500 it is expected to become fully operational in November 2012 K topped the LINPACK benchmark with the performance of petaflops, or quadrillion calculations per second 68, GHz 8-core SPARC64 VIIIfx processors packed in 672 cabinets, for a total of 548,352 cores

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Multi-core processor A multi-core processor is a single computing component with two or more independent actual processors (called "cores"), which are the units that read and execute program instructions.

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Nvidia CUDA CUDA or Compute Unified Device Architecture is a parallel computing architecture developed by Nvidia Using CUDA, the latest Nvidia GPUs become accessible for computation like CPUs

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Nvidia CUDA

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Parallel Computing The computational problem should be able to: Be broken apart into discrete pieces of work that can be solved simultaneously; Execute multiple program instructions at any moment in time; Be solved in less time with multiple compute resources than with a single compute resource.

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Parallel Computing Example PI Calculation The value of PI can be calculated in a number of ways. Consider the following method of approximating PI 1.Inscribe a circle in a square 2.Randomly generate points in the square 3.Determine the number of points in the square that are also in the circle 4.Let r be the number of points in the circle divided by the number of points in the square 5.PI ~ 4 r 6.Note that the more points generated, the better the approximation

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Serial pseudo code for this procedure: Note that most of the time in running this program would be spent executing the loop.

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Parallel solution Computationally intensive Minimal communication Minimal I/O

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Parallel solution Parallel strategy: break the loop into portions that can be executed by the tasks. For the task of approximating PI: – Each task executes its portion of the loop a number of times. – Each task can do its work without requiring any information from the other tasks (there are no data dependencies). – Uses the SPMD model. One task acts as master and collects the results.

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Parallel solution

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References https://computing.llnl.gov/tutorials/parallel_comp/

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