Big Data Analytics Big Data Analytics C. Distributed Computing Emvironments / C.1 Map Reduce Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Institute for Computer Science University of Hildesheim, Germany Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 32 Big Data Analytics Syllabus Tue. 9.4. (1) 0. Introduction Tue. 16.4. Tue. 23.4. Tue. 30.4. (2) (3) (4) A. Parallel Computing A.1 Threads A.2 Message Passing Interface (MPI) A.3 Graphical Processing Units (GPUs) Tue. 7.5. Tue. 14.5. Tue. 21.5. (5) (6) (7) B. Distributed Storage B.1 Distributed File Systems B.2 Partioning of Relational Databases B.3 NoSQL Databases Tue. 28.5. Tue. 4.6. Tue. 11.6. Tue. 18.6. (8) (9) (10) C. Distributed Computing Environments C.1 Map-Reduce Pentecoste Break — C.2 Resilient Distributed Datasets (Spark) C.3 Computational Graphs (TensorFlow) Tue. 25.6. Tue. 2.7. (11) (12) D. Distributed Machine Learning Algorithms D.1 Distributed Stochastic Gradient Descent D.2 Distributed Matrix Factorization Tue. 9.7. (13) Questions and Answers Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 32 Big Data Analytics Outline 1. Introduction 2. Parallel Computing Speedup 3. Example: Counting Words 4. Map-Reduce Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 32 Big Data Analytics 1. Introduction Outline 1. Introduction 2. Parallel Computing Speedup 3. Example: Counting Words 4. Map-Reduce Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 32 Big Data Analytics 1. Introduction Technology Stack Part D Distributed Machine Learning Algorithms Part C Distributed Execution Environments Part B Distributed Storage Part A Parallel/Distributed Computing Lars Schmidt-Thieme, Information Syst