Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-14-54-M-6

High Performance Computing with GPUs (M, 6.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-14-54-M-6 High Performance Computing with GPUs 6.0 CP (180 h)

Basedata

CP, Effort 6.0 CP = 180 h
Position of the semester 1 Sem. in WiSe
Level [6] Master (General)
Language [DE/EN] German or English as required
Module Manager
Lecturers
Area of study [INF-VIS] Visualisation and Scientific Computing
Reference course of study [INF-88.79-SG] M.Sc. Computer Science
Livecycle-State [NORM] Active

Notice

Lecuture plus programming exercises.

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
3V+1U INF-14-54-K-6
High Performance Computing with GPUs
P 56 h 124 h
U-Schein
ja PL1 6.0 WiSe
  • About [INF-14-54-K-6]: Title: "High Performance Computing with GPUs"; Presence-Time: 56 h; Self-Study: 124 h
  • About [INF-14-54-K-6]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.
    • It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: oral examination (20-60 Min.)
  • Examination Frequency: each semester
  • Examination number: 61454 ("High Performance Computing with GPGPUs")

Evaluation of grades

The grade of the module examination is also the module grade.


Contents

Up-to-date graphical devices allow not only video games but may be used for scientific computations. They are heavily used in deep learning and artificial intelligence. With their superior performance it is not surprising that many of the fastest computers in the world contain these cards. This course offers basic knowledges on high performance computing on graphical devices. The course focuses on programming graphic cards with Compute Unified Device Architecture (CUDA) which is trained and deepened in examples and exercises.
  • Comprehension in SIMD programming model (single instruction multiple data) and relation between hardware architecture and performance
  • evaluation of parallel algorithms with regard to their performance
  • theoretical and practical application of CUDA

Competencies / intended learning achievements

After successfully completing the module, students will be able to
  • analyze simple algorithms with regard to parallelization and transform them into parallel algorithms,
  • implement qualified and unaided given algorithms in CUDA on graphical processing units (GPUs).

Literature

  • Paralleles Rechnen: Performancebetrachtungen zu Gleichungslösern; Josef Schüle, Oldenbourg 2010
  • CUDA by Example: An Introduction to General-Purpose GPU Programming; Jason Sanders, Edward Kandrot; Addison Wesley 2010
  • Programming Massively Parallel Processors: A Hands-On Approach; David Kirk, Wen-Mei W. Hwu; Morgan Kaufman Publ Inc. 2010

Requirements for attendance of the module (informal)

None

Requirements for attendance of the module (formal)

None

References to Module / Module Number [INF-14-54-M-6]

Course of Study Section Choice/Obligation
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective
[MAT-88.105-SG] M.Sc. Mathematics [Subsidiary Topic] Subsidiary Topic (Minor) [WP] Compulsory Elective
[MAT-88.118-SG] M.Sc. Industrial Mathematics [Core Modules (non specialised)] Computer Science and Computational Methods [WP] Compulsory Elective
[MAT-88.276-SG] M.Sc. Business Mathematics [Core Modules (non specialised)] Computer Science and Computational Methods [WP] Compulsory Elective