14-ParallelProcessing.pptx - Parallel Processing Introduction to Computational Thinking and Data Science Lecture 14 When Datasets Are Large\"MEGWARE.CLIC

14-ParallelProcessing.pptx - Parallel Processing...

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Parallel Processing Introduction to Computational Thinking and Data Science Lecture 14
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When Datasets Are Large "MEGWARE.CLIC" by MEGWARE Computer GmbH - . Licensed under CC BY-SA 3.0 via Wikimedia Commons – 2
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Today’s Topics 1. Estimating execution time 2. Algorithmic complexity 3. Processing several datasets with a workflow 4. Processing data with a divide-and- conquer strategy 5. Analysis of parallel processing 3
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Estimating Execution Time 4
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Processing a Document: Encryption Workflow It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair… shiftkey=1 Ju xbt uif cftu pg ujnft, … 5
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Encryption Workflow: How Does It Work a b c d e f g h i j It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair… shiftkey=1 6
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Execution Time with Shiftkey=1 size time shiftkey=1 7
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Encryption Workflow: How Does It Work a b c d e f g h i j It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair… shiftkey=5 8
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Execution Time with Different Shiftkey Values size time shiftkey=1 shiftkey=5 time = numletters x shiftkey shiftkey=8 9
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Image Processing Workflow 10
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Execution Time size time 11
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Algorithmic Complexity 12
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Algorithmic Complexity An algorithm has linear complexity when its execution time grows linearly with the size of the input data An algorithm has polynomial complexity when its execution time is bound by a polynomial expression in the size of the input data (e.g., n 3 ) Similarly, an algorithm has exponential complexity when bound by an exponential expression (e.g., 2 n ) size time size time 13
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Algorithmic Complexity: Big “O” Notation Linear complexity Polynomial complexity Exponential complexity O(n) O(n k ) O(k n ) n: size of the input data 14
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