Architectures For Parallel Computing
Public syllabus for 2025-2026
Academic overview
Teaching team
Learning time distribution
| Total | ||||||
|---|---|---|---|---|---|---|
| Curriculum | Lecture | Practice | Total Weekly | Lecture | Practice | |
| 42 | 28 | 14 | 3 | 2 | 1 | |
| Exam hours | ||||||
| 10 | ||||||
| Individual Study | Bibliography study | Field study | Homework | Tutoring | Others | |
| 108 | 20 | 15 | 58 | 5 | 0 | |
| Overall | ||||||
| 150 |
Learning outcomes
Knowledge
- Knowledge of basic architectures and VLSI concept
- Knowledge and understanding of convolutions and their applications in signal filtering
- Knowledge and understanding of the theoretical model and applications of long integer arithmetic
- Understanding challenges and implementation solutions in GPU
Skills
- Design and implementation/simulation of systolic algorithms in computer arithmetic, matrix computation, numerical computing and graphics
- Implementation of parallel algorithms that use GPU
Responsibility
- Ability to use specialized applications in different domains
Online platform
Course content
| Content | Methods | Obs |
|---|---|---|
| L1-2. Introduction, evolution of the model, theoretical models | Discourse, conversation,illustration by examples | |
| L3. Parallel algorithms analysis, comparison with sequential algorithms analysis | Discourse, conversation,illustration by examples | |
| L4-5. Systolic algorithms for matrices operations | Discourse, conversation,illustration by examples | |
| L6-8. Semi-systolic and systolic algorithms for signal processing (FIR, IIR) | Discourse, conversation,illustration by examples | |
| L9. Systolic algorithms in multi-precision integer arithmetic | Discourse, conversation,illustration by examples | |
| L10-12. Challenges in SIMD architectures and their solution in CUDA, comparison of SIMT with SIMD and SMT | Discourse, conversation,illustration by examples | |
| L13-14. Architecture details of GPU's supporting CUDA, optimizations and limitations | Discourse, conversation,illustration by examples |
Course bibliography
Brudaru, Octav şi Gâlea, Dan – Introducere în CALCULUL SISTOLIC, Ed. Academiei Române, 1994 Jebelean, Tudor – Systolic Multiprecision Arithmetic, Phd. Thesis, RISC Linz, 1994 Knuth, D. – Tratat de programarea calculatoarelor. Vol. 2. Algoritmi seminumerici, Editura Tehnică. Petkov, N. – Systolic Parallel Processing, in Advances in Parallel Computing, Volume 5, North-Holland, 1993 Classroom materials (Classroom code: rdvjsai)
Seminar content
| Content | Methods | Obs |
|---|---|---|
| S1. Cellular Automata, Elementary cellular automaton, Game of life | Dialogue with students,cooperative learning | |
| S2. Evaluation of polynomials | Dialogue with students,cooperative learning | |
| S3. Matrices multiplications | Dialogue with students,cooperative learning | |
| S4. Convolutions and linear filters | Dialogue with students,cooperative learning | |
| S5. Long integers multiplication | Dialogue with students,cooperative learning | |
| S6. Basic operations on images using CUDA | Dialogue with students,cooperative learning | |
| S7. Julia fractal computation using CUDA | Dialogue with students,cooperative learning | |
| Bibliography: Petkov, N. – Systolic Parallel Processing, in Advances in Parallel Computing, Volume 5, North-Holland, 1993 https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html |
Seminar bibliography
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Corroboration
(none)
AI tools guidance
Evaluation and delivery
| Activity | Criteria | Methods | Percentage |
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Performance standards
Student should have basic understanding of special parallel systems and should be able to give at least two examples of existing systems At least 4 lab assignments are required
Additional info
(none)