FI:PV197 GPU Programming - Course Information
PV197 GPU ProgrammingFaculty of Informatics
- Extent and Intensity
- 1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- RNDr. Jiří Filipovič, Ph.D. (lecturer)
prof. RNDr. Jiří Barnat, Ph.D. (assistant)
doc. RNDr. Petr Holub, Ph.D. (assistant)
RNDr. Jiří Matela, Ph.D. (assistant)
- Guaranteed by
- doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications - Faculty of Informatics
Supplier department: Department of Computer Systems and Communications - Faculty of Informatics
- Thu 16:00–17:50 D2
- IB109 Design of Parallel Systems
C programming basics, familiarity with CPU architecture and parallelization of algorithms.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- Fields of study the course is directly associated with
- there are 40 fields of study the course is directly associated with, display
- Course objectives
- After the end of the course students should: describe architecture, programming model and optimization for GPUs; explain GPU implementation of several broadly used algorithms; create GPUs implementation of given computational tasks; judge the suitability of given computational problem for GPU acceleration.
- Introduction: motivation for GPU programming, GPU architecture, overview of parallelism model, basics of CUDA, first demonstration code
- GPU hardware and parallelism: detailed hardware description, synchronization, calculation on GPU -- rate of instruction processing, arithmetic precision, example of different approaches to matrix multiplication -- naive versus block-based
- Performance of GPUs: memory access optimization, instructions perormance, example of matrix transposition
- CUDA, tools and libraries: detailed description of CUDA API, compilation using nvcc, debugging, profiling, basic libraries, project assignment
- Optimization: general rules for algorithm design for GPU, revision of matrix multiplication, parallel reduction
- Parallelism in general: problem decomposition, dependence analysis, design analysis, parallel patterns
- Metrics of efficiency for GPU: parallel GPU and CPU usage, metrics for performance prediction of GPU code, demonstration using graphics algorithms, principles of performance measurement
- OpenCL: introduction to OpenCL, differences comparing to CUDA, exploiting OpenCL for hardware not accessible from CUDA
- Case studies 1: Calculation of force field of molecule, automatic optimization of memory-bound functions
- Case studies 2: Acceleration of image and video compression
- Case studies 3: LTL model checking acceleration
- Discussion of project, presentation of best achieved results, presentation of 3 best solutions by authors, final discussion
- MATTSON, Timothy G, Beverly A. SANDERS and Berna MASSINGILL. Patterns for Parallel Programming. Boston: Addison-Wesley, 2005. xiii, 355. ISBN 0321228111. info
- The data parallel programming model : foundations, HPF realization, and scientific applications. Edited by Guy-René Perrin - Alain Darte. Berlin: Springer, 1996. xv, 284. ISBN 3540617361. info
- GPU gems 3. Edited by Hubert Nguyen. Upper Saddle River, NJ: Addison-Wesley, 2007. l, 942. ISBN 9780321515261. info
- Teaching methods
- Lectures, reading of recommended literature, solving and programming assignments.
- Assessment methods
- Scores for assignment solutions: 50% for the project, up to 30% bonus for performance of the solution. Oral exam after all the lectures: 50%. In order to pass successfully, score for the oral exam must be at least half of maximum.
- Language of instruction
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.