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Big Data Analytics Big Data Analytics Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany A. Parallel Computing / A.2 Message Passing Interface (MPI) Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany A. Parallel Computing / A.2 Message Passing Interface (MPI) 1 / 28 Big Data Analytics Syllabus Tue. 10.4. (1) 0. Introduction Tue. 17.4. Tue. 24.4. Tue. 1.5. (2) (3) (4) A. Parallel Computing A.1 Threads A.2 Message Passing Interface (MPI) A.3 Graphical Processing Units (GPUs) Tue. 8.5. Tue. 15.5. Tue. 22.5. Tue. 29.5. (5) (6) (7) B. Distributed Storage B.1 Distributed File Systems B.2 Partioning of Relational Databases Pentecoste Break — B.3 NoSQL Databases Tue. 5.6. Tue. 12.6. Tue. 19.6. (8) (9) (10) C. Distributed Computing Environments C.1 Map-Reduce C.2 Resilient Distributed Datasets (Spark) C.3 Computational Graphs (TensorFlow) Tue. 26.6. (11) D. Distributed Machine Learning Algorithms D.1 Distributed Stochastic Gradient Descent Tue. 3.7. Tue. 10.7. (12) (13) D.2 Distributed Matrix Factorization Questions and Answers Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany A. Parallel Computing / A.2 Message Passing Interface (MPI) 1 / 28 Big Data Analytics Outline 1. MPI Basics 2. Point to Point Communication 3. Collective Communication 4. One-sided Communication Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany A. Parallel Computing / A.2 Message Passing Interface (MPI) 1 / 28 Big Data Analytics 1. MPI Basics Outline 1. MPI Basics 2. Point to Point Communication 3. Collective Communication 4. One-sided Communication Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany A. Parallel Computing / A.2 Message Passing Interfa

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