Parallel Computing

Massively Parallel Computing with CUDA – Winter Semester 2008/2009

People:

Lecturers: Hendrik Lensch and Robert Strzodka

Teaching Assistents:
Hendrik Becker (Saarbrücken), Holger Dammertz (Ulm)

Registration and Grading:

Register here

The course is graded with 6 ECTS points.

In the first 6 weeks there are regular assignments. Successful completion of at least 30% of each assignment is a prerequisite for the admission to the student projects.

Student projects are long term assignments on a self chosen lecture related topic. The aim is to gain experience in designing a more demanding parallel application. Student project proposal will be presented in the 8th lecture on the 15 Dec 2008. From then until the last lecture the students have time to work on the solution. The solutions must be presented in the final lecture on the 9 Feb 2009. Until 9 Mar 2009 the students will have time to polish their solution and prepare a written report of approx. 8 pages.

The actual grading depends on the quality of the implementation, presentation and the report of the student project.

Time and Place:

Lecture:

Mon, 10-12 c.t., MPII E1 4, Room 024; starting 27 Oct

Exercise Course:

Wed, 10-12 c.t., MPII E1 4, Room 019; starting 29 Oct

Topics of the lecture:

Background:

Due to thermal restrictions further performance gains of microprocessors do no longer mainly depend on clock frequency increases but parallelization of the processor into multiple cores. Soon there will be tens of cores in each CPU with hundreds to follow. Graphics processors already contain hundreds of parallel processing elements and thus enable us to explore this realm of massively parallel computing today.

The high number of parallel cores poses a great challenge for software design that must expose massive parallelism to benefit from the new hardware. The main purpose of the lecture is to teach practical algorithm design for such parallel hardware.

For the first half of the lecture there will be supervised exercises to familiarize oneself with the CUDA parallel programming model and environment. In the second half the students will work on chosen projects.

List of topics:

Introduction to Parallel Computing

Basic Algorithms:

Map, reduce, parallel branching, sorting
Parallel data storage and retrieval

Parallel Computation:

FFT, linear equation solvers, particle couplings
Parallel complexity analysis and profiling
System integration and graphics processor clusters

Student Project Presentation

Slides:

Assignments:

Questions & Feedback: