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Introduction to GPU Accelerated Computing WS 2016/17 Block Course with Exercises

NEWS:	This is the webpage for the GPU Block Course Oct. 4-7, 2016 . In the first session Oct. 4 everyone present could be accepted for the course (including waiting list); everyone who has a lecture account by now is a regular participant. Registration/Enrolment is finally CLOSED now. Our block course can be found in the in the LSF system with this link here .
Files:	Complete Slides of Lecture 1-3 Lecture Slides Jason Sanders complete User Handbook kepler cluster NVIDIA Slides on User API's NBODY6++ Technical Manual NBODY6++ Our Experiments Description NBODY6++ Lecture Slides
Summary:	We will learn the basic technique to use GPU (graphical processing units, graphics cards) for numerical accelered computing at the example of CUDA - an extension of C.
Enrolment:	Please use this link: https://uebungen.physik.uni-heidelberg.de/v/638 . "October New Group" are the currently registered participants, "Overflow Group" contains the waiting list (in order of enrolment).
Lecturer:	Prof. Rainer Spurzem; ZAH/ARI, Mönchhofstr. 12-14, 69120 Heidelberg Email: spurzem@ari.uni-heidelberg.de
Time and Place:	Tuesday - Friday, Oct. 4-7, 2016; Lecture: 10:15 - 13:00 Uhr, Exercises: 14:15 - 17:00, BOTH at Philosophenweg 12 (Physics/Astronomy), CIP Pool.
Requirements:	Basic knowledge of a higher programming language such as C, C++, Fortran or similar
Topic:	We will learn the basic technique to use GPU (graphical processing units, graphics cards) for numerical accelered computing at the example of CUDA - an extension of the C programming language. Also some general ideas of parallel programming will be discussed. GPU accelerated parallel computing is a technique used in many areas of computational physics and astrophysics. See for example this conference with current research been done: GPU 2016 Rome . After the basic introduction one or two application examples will be presented. This is a four day block course with lectures in the morning and practical hands-on exercises in the afternoon. Topics: Parallel Computing, GPU Hardware, Elements of CUDA Language, Data Transfer, Vector and Matrix Operations, Simple Application for N-Body Problem.
Literature:	CUDA by Example, of Jason Sanders and Edward Kandrot . Our course is inspired by this book and lectures of the authors, but will cover much less details; on the other hand we will learn about some astrophysical application, which is not in the book.
Exercises:	Tue - Fri, Oct. 4-7, 14:15 - 17:00 Uhr, INF 227, CIP Pool KIP 1.401 Solution of Problems with help provided by the lecturer

(Responsible for contents: Rainer Spurzem )