Subjects

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Parallel programming in OpenMP, MPI, and CUDA - M403065

Title:	Paralelní programování v OpenMP, MPI a CUDA
Guaranteed by:	Centrum počítačového vzdělávání VŠCHT Praha (599)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2019 to 2020
Semester:	summer
Points:	summer s.:2
E-Credits:	summer s.:2
Examination process:	summer s.:
Hours per week, examination:	summer s.:0/10, C [HS]
Capacity:	unknown / 20 (unknown)
Min. number of students:	unlimited
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Level:
Note:	course can be enrolled in outside the study plan enabled for web enrollment

Guarantor:	Kolafa Jiří prof. RNDr. CSc.
Class:	Kurz CŽV
Classification:	Informatics > Programming

Examination dates Schedule

This subject contains the following additional online materials

E-learning course

Annotation -

After general introduction to models of parallel programming, we will cover main rules and tricks leading to efficient parallelization of various tasks. In the second part of the course, we will focus on a particlar architecture, namely graphics processing units NVIDIA. We will cover CUDA software (incl. the needed basics of laguage C++) and solve exercises focussing on the field of computational chemistry.

Last update: Kolafa Jiří (04.09.2019)

Literature -

J. Sanders, E. Kandrot: CUDA by example - An Introdiction to General-Purpose GPU Programming (Adisson-Wesley 2011, 978-0-13-138768-3)

D. B. Kirk, W-m. W. Hwu: Programming Massively Parallel Processors - A Hands-on Approach (Elsevier 2010, 978-0-12-381472-2)

Last update: Kolafa Jiří (04.09.2019)

Teaching methods -

talks(30%), exercise(30%), project (40%)

Last update: Kolafa Jiří (04.09.2019)

Requirements to the exam -

Interactive evaluation without a formal test or exam

Last update: Kolafa Jiří (04.09.2019)

Syllabus -

1. History: D825, transputers. Unix world: MIMD threads. Platforms (MPI etc.). SIMD and GPUs. Processors, cores, threads.

2. Algorithmization: Linear algebra (dense, sparse). Grid methods (PDE). MC and MD: domain decomposition, linked-cell list. Graphics: rendering.

3. C and C++ review.

4. Message passing and threads: OpenMP, MPI.

5. GPU - architecture.

6. CUDA basics (NVIDIA Teaching Kit modules 1,2).

7. CUDA Parallelism Model, memory (NTK modules 3-6).

8. Parallel Computation Patterns (NTK modules 7-9).

9. Floating point (NTK module 12).

10. Case study: Electrostatic potential (NTK Module 16).

Last update: Kolafa Jiří (04.09.2019)

Learning outcomes -

Students will understand parallelization methods and will be able to write a simple CUDA-based code.

Last update: Kolafa Jiří (04.09.2019)

Entry requirements -

C-language basics and experience with linux.

Last update: Kolafa Jiří (04.09.2019)

Teaching methods
	Activity	Credits	Hours
	Práce na individuálním projektu	0.2	5
	Účast na seminářích	0.2	5
		0 / 2	10 / 56

Coursework assessment
Form	Significance
Regular attendance	50
Defense of an individual project	50