Introduction to GPU Accelerated Computing SS 2016 Block Course with Exercises
NEWS:  Due to too many registrations and a problem with the dates Aug. 14 the course will be split into TWO courses: the first one July 2528, 2016, the second one Oct. 47, 2016 . The July course is already in the LSF system with this link here . The October course will be published in the LSF information for the next term (WS 2016/2017) soon. 
Files: 
Complete Slides of Lecture 13, MonWed July 2527 Lecture Slides Jason Sanders complete, Mon/Tue July 25/26 User Handbook kepler cluster NVIDIA Slides on User API's NBODY6++ Technical Manual NBODY6++ Our Experiments Description NBODY6++ Lecture Slides of Thu July 28 
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.uniheidelberg.de/v/638 
Lecturer: 
Prof. Rainer Spurzem;
ZAH/ARI, Mönchhofstr. 1214, 69120 Heidelberg Email: spurzem@ari.uniheidelberg.de 
Time and Place:  Monday  Thursday, July 2528, 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 handson exercises in the afternoon. Topics: Parallel Computing, GPU Hardware, Elements of CUDA Language, Data Transfer, Vector and Matrix Operations, Simple Application for NBody 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:  Mon Fri, 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 )
Contact: D. Möricke