Computational systems, data bases, networks 4010-WSOa

A detailed description of the subject is provided at the level of a given teaching cycle in the Description section.

Term 2025Z:

Subjects:
1. Programming languages - lecture 4 hours
2. File transfer - lecture 2 hours, laboratory 2 hours
3. File systems - lecture 2 hours
4. NAS - lecture 2 hours
5. CPU Architecture: x86_64 - lecture 4 hours
6. Virtualisation - lecture 2 hours, laboratory 2 hours
7. GPU - lecture 4 hours, laboratory 4 hours
8. Slurm Scheduling System - lecture 2 hours, laboratory 2 hours
9. Building and installing software - lecture 2 hours, laboratory 4 hours
10. Vector computers - lecture 2 hours
11. LINPACK - laboratory 2 hours
12. HPCG - laboratory 2 hours
13. Numerical libraries - laboratory 2 hours
14. Scalar optimization - lecture 2 hours, laboratory 2 hours
15. Quantum computers - lecture 2 hours
16. Scaling HPC Application - laboratory 8 hours

The order of topics and the level of detail may be subject to minor adjustments.

Term 2025L:

The order of topics and the level of detail may be subject to minor adjustments.

Term 2026Z:

The order of topics and the level of detail may be subject to minor adjustments.

Course coordinators

Michał Hermanowicz

Type of course

obligatory courses

Mode

Blended learning

Prerequisites (description)

Ability to operate a personal computer.

Learning outcomes

Field-specific learning outcomes with reference to Level 2 PRK characteristics:
the student knows and understands:
K_W07 – in-depth knowledge of the construction, operation, and design of computer networks and modern computer systems,
K_W08 – in-depth knowledge of current trends in the development of network technologies and computer architectures,

the student is able to:
K_U07 – evaluate the suitability and application potential of new hardware and software solutions
in the context of solving computational problems in a selected field of science,
K_U13 – manage large-scale computer systems, e.g., by ensuring access control and a security level appropriate to requirements, as well as monitoring resource utilization,

the student is ready to:
K_K01 – establish and maintain cooperation and strive to achieve team goals through appropriate work planning and organization.

and the subject-specific learning outcomest
the student knows and understands:
W1 - Demonstrates in-depth understanding of the structure of HPC computer systems, their key components, and their operating mechanisms [K_W07],
W2 - Demonstrates in-depth understanding of the current development trends in modern HPC computer systems (clusters and supercomputers), including their architectures and the networking technologies they utilize [K_W08],

the student is able to:
U1 - Assess the usefulness and feasibility of using new hardware solutions, including HPC architectures, and software solutions (domain-specific implementations) for solving computational problems [K_U07],
U2 - Operate HPC computer systems using secure access protocols, and monitor resource utilization for computational tasks [K_U13],

the student is ready to:
K1 - Establish and maintain collaboration with others and strive to achieve team goals by planning and organizing work – utilizes tools for collaborating on IT projects [K_K01]

Assessment criteria

1. Completion of the course ends with one common grade for all classes (lecture + laboratory) carried out as part of the course.
2. A sick leave does not exempt students from knowledge of the material. It only entitles them to an individualized form of assessment.
3. Students who have received approval for an individualized course of study are required to contact the course coordinator to determine how to achieve all the learning outcomes assigned to the course. If the above-mentioned outcomes cannot be achieved, the coordinator may refuse to grant credit for the course.
4. Attendance is mandatory. In cases of justified absences, the student is required to contact the course coordinator immediately.
5. Regarding the use of artificial intelligence (AI) tools in the preparation of coursework, the rules set out in the "Regulations on the Use of Artificial Intelligence Tools in the Educational Process for the Computational Engineering Degree Programme" apply.
6. Detailed grading criteria are provided at the level of the given academic cycle in the "Notes" section.

Practical placement

Not applicable.

Bibliography

The subject literature has been posted at the level of a given teaching cycle in the Bibliography section.

Term 2025Z:

Lecture and exercise materials provided by instructors, as well as technical documentation for the software and hardware platforms used, including KDM ICM instructions: https://kdm.icm.edu.pl/.

Term 2025L:

Lecture and exercise materials provided by instructors, as well as technical documentation for the software and hardware platforms used, including KDM ICM instructions: https://kdm.icm.edu.pl/.

Term 2026Z:

Lecture and exercise materials provided by instructors, as well as technical documentation for the software and hardware platforms used, including KDM ICM instructions: https://kdm.icm.edu.pl/.

Notes

Term 2025Z:

Learning outcomes W1-W2 and U1-U2 are assessed by a written exam and ongoing monitoring of student activity during lectures and laboratory exercises; Learning outcome K1 is assessed by ongoing monitoring of student activity during lectures and laboratory exercises.

Term 2025L:

Term 2026Z: