Graphics processing units (GPUs)  were originally developed  as specialized electronic circuits for fast image processing and graphics rendering. GPUs are nowadays heavily employed for all the general purpose applications that require high-performance computational power, because their highly parallel structure makes them more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel. GPUs are becoming a valid alternative to the classic CPU-based supercomputer clusters also for the improved energy-consumption/performance ratio and their lower cost.  GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles.

The aim of the course is to provide students with a general overview about this emerging multi-core parallel computing architecture. The objectives of this course are:

• gaining understanding of GPU computer architecture,
• getting familiar with GPU programming environments,
• implementing programs solving problems that would classically have been run on supercomputers

Basic notions of Computer Architectures and a good knowledge of C programming are expected, as all the programming will use environments building on C.

The course will start with an introduction on the modern GPU architectures, by tracing the evolution from the SIMD (Single Instruction, Multiple Data) architecture to the current architectural features and by discussing the trends for the future. We then will explore the two main Programming Models: CUDA and OpenCL. Real case studies will expose students to the potential applications of this technology. A final project will give them the possibility to make a concrete experience of the concepts taught, to solve a modest GPU programming problem that will be assigned by the teacher or proposed by the student, to present it at the end to the class. This is the
list of the main topics of the course:

• GPU Architectures (NVIDIA Fermi, NVIDIA Kepler, ATI/AMD)
• CUDA Programming Model
• OpenCL Programming Model
• Case Studies (Graph exploration, Path Planning, Curvature Analysis, Signal Processing, PDE Solvers)
• Optimizing GPU performance

Registration

The enrollment can be performed using TISS. The enrollment will be close on February 28th, 2019. The max number of students for this course is 15. Please register soon !!! The first lecture will start on 6th of March in the Seminarraum Techn. Informatik.

ECTS-Breakdown: 6 ECTS = 150 Hours 

• 1 h - Introduction to the Course
• 1 h - Group formation
• x h - Development of project idea and brainstorming
• 20 h - Lessons on GPU Computing and Architecture
• 1 h - Workshop 1 - Project proposal presentation
• 1 h - Workshop 2 - Project results presentation
• 100 h - Project Work
•   20 h - Project discussion 

Some Resources:

• Wen-Mei W. Hwu, GPU Computing GEMS, Emerald Edition, NVIDIA
• Jason Sanders and Edward Kandrot, CUDA by Example: An Introduction to General-Purpose GPUProgramming
• Wen-mei W. Hwu, Programming Massively Parallel Processors: A Hands-on Approach
• E. Bartocci, E. Cherry, J. Glimm, R. Grosu, S.A. Smolka, and F. Fenton. Toward Real-time Simulation of Cardiac Dynamics. In CMSB 2011: Proceedings of the 9th ACM International Conference on Computational Methods in Systems Biology, Paris, France, September 21-23, pages 103-110. ACM, 2011.
• A. Murthy, E. Bartocci, F. Fenton, J. Glimm, R. Gray, S.A. Smolka, and R. Grosu. Curvature Analysis of Cardiac Excitation Wavefronts. In CMSB 2011: Proceedings of the 9th ACM International Conference on Computational Methods in Systems Biology, Paris, France, September 21-23, pages 151-160. ACM, 2011.

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