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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

BIK-EIA Efficient Implementation of Algorithms Extent of teaching: 13KP+4KC

Instructor: Completion: Z,ZK

Department: 18101 Credits: 5 Semester: Z

Annotation:
Student learn to combine their SW skills (efficient algorithms) and HW knowledge (utilization of all available features of the particular processor and memory architecture). Students learn the basics of code tuning.

Lecture syllabus:

1. Recap of basic computer architecture concepts (pipeline, vector instructions, cache memory, TLB).

2. New architecture features of modern CPUs.

3. Multicore architectures.

4. Multicore programming in OpenMP API.

5. Efficient algorithms, asymptotic complexity.

6. Efficient data structures and their combinations.

7. Basic routines for numerical linear algebra (dense matrices).

8. Basic routines for numerical linear algebra (sparse matrices).

9. Source code transformations I.

10. Source code transformations II.

11. Compilers and optimizations.

12. Models of cache behavior.

13. Models of cache behavior in multicore environments.

Seminar syllabus:

1. Introduction, laboratory equipment

2. Performance metrics.

3. Algorithms for searching elements.

4. Algorithms for searching patterns in text, evaluation of logical expressions

5. Event counters based profiling, assignment No. 1.

6. Experiments with compiler's settings

7. Sorting of arrays and linked lists.

8. Sorting on external memories, checking the assignment No. 1

9. FFT algorithm, modelling of multi-element interaction, assignment No. 2

10. Graph algorithms, assignment No. 3.

11. Encryption, data compression, checking the assignment No. 2

12. Available math libraries.

13. Checking the assignment No. 3.

Literature:

1. Wolfe, M. ''High-Performance Compilers for Parallel Computing''. Addison Wesley, 1995. ISBN 0805327304.

2. Wadleigh, K. R., Crawford, I. L. ''Software Optimization for High Performance Computing: Creating Faster Applications''. Prentice Hall PTR, 2000. ISBN 0130170089.

Requirements:
Subject BI-EFA. Knowledge of C or C++ programming language, active knowledge of assembly language. Good knowledge of processor and memory architecture concepts.

Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/BI-EIA/

The course is also part of the following Study plans:

Study Plan Study Branch/Specialization Role Recommended semester

BIK-WSI-SI.2015 Web and Software Engineering V 5

BIK-SPOL.2015 Unspecified Branch/Specialisation of Study VO 5

BIK-BIT.2015 Computer Security and Information technology V 5

BIK-BIT.2020 Computer Security and Information technology V 5

Page updated 26. 4. 2024, semester: Z/2020-1, L/2021-2, L/2019-20, L/2022-3, Z/2019-20, L/2020-1, L/2023-4, Z/2022-3, Z/2021-2, Z/2023-4, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška

BIK-EIA	Efficient Implementation of Algorithms			Extent of teaching:	13KP+4KC
Instructor:				Completion:	Z,ZK
Department:	18101	Credits:	5	Semester:	Z

1.		Recap of basic computer architecture concepts (pipeline, vector instructions, cache memory, TLB).
2.		New architecture features of modern CPUs.
3.		Multicore architectures.
4.		Multicore programming in OpenMP API.
5.		Efficient algorithms, asymptotic complexity.
6.		Efficient data structures and their combinations.
7.		Basic routines for numerical linear algebra (dense matrices).
8.		Basic routines for numerical linear algebra (sparse matrices).
9.		Source code transformations I.
10.		Source code transformations II.
11.		Compilers and optimizations.
12.		Models of cache behavior.
13.		Models of cache behavior in multicore environments.

1.		Introduction, laboratory equipment
2.		Performance metrics.
3.		Algorithms for searching elements.
4.		Algorithms for searching patterns in text, evaluation of logical expressions
5.		Event counters based profiling, assignment No. 1.
6.		Experiments with compiler's settings
7.		Sorting of arrays and linked lists.
8.		Sorting on external memories, checking the assignment No. 1
9.		FFT algorithm, modelling of multi-element interaction, assignment No. 2
10.		Graph algorithms, assignment No. 3.
11.		Encryption, data compression, checking the assignment No. 2
12.		Available math libraries.
13.		Checking the assignment No. 3.

1.		Wolfe, M. ''High-Performance Compilers for Parallel Computing''. Addison Wesley, 1995. ISBN 0805327304.
2.		Wadleigh, K. R., Crawford, I. L. ''Software Optimization for High Performance Computing: Creating Faster Applications''. Prentice Hall PTR, 2000. ISBN 0130170089.