Code: | PRODEI021 | Acronym: | REDAI |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Comp. Architectures, Operating and Networks Sys. |
Active? | Yes |
Responsible unit: | Department of Informatics Engineering |
Course/CS Responsible: | Doctoral Program in Informatics Engineering |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
PRODEI | 2 | Syllabus | 1 | - | 6 | 28 | 162 |
Teacher | Responsibility |
---|---|
António Miguel Pontes Pimenta Monteiro | |
António Miguel Pontes Pimenta Monteiro |
Recitations: | 2,00 |
Type | Teacher | Classes | Hour |
---|---|---|---|
Recitations | Totals | 1 | 2,00 |
António Miguel Pontes Pimenta Monteiro | 2,00 |
Objectives:
The main objectives are to provide the students with a solid knowledge of parallel computing (in clusters) and distributed computing in the Internet infrastructure, mainly Grid Computing and Cloud Computing. Also we can enlist as objectives the obtention of a solid knowledge on parallel architectures and on developing parallel programs for distributed memory and shared memory architectures. Through assignments and lab projects the students will also obtain experience in the core technologies in the field, including performance analysis and tuning.
Aptitudes and competences:
Algorithm conception and software implementation aiming efficient parallel execution using Foster's methodology.
Use of distribution techniques for grid and cloud environments.
Deployment and exploitation of the developed software in clusters, grids and clouds.
General computing.
Proficiency in C/C++ programming for the assignments.
1. Parallel Programming
Introduction to parallel programming, computer architectures, processors, memory organization and interconnection networks.
Parallel Programming Fundamentals: task/channel paradigm, communication patters, synchronization, task granularity and scheduling.
Cluster programming with MPI and OpenMP.
Parallel computing characterization: execution models, programming models, computation models, performance and efficiency measures, scalability analysis.
2. Distributed Computing
Grid computing:
Grid computing models: generic grid, utility grid and desktop grid.
Evolution of grid middleware: metacomputing (e.g. Condor), resource-oriented (e.g. Globus 3) and service-oriented (e.g. Globus 4).
Grid security: authentication, data integrity and encryption, authorization.
Hands-on Grid technology.
Other distributed environments:
Cloud Computing
Peer-to-Peer Computing
In the Classes: Theorethical presentation, complemented by examples, small demonstrations and clues for lab assignements and project.
In the Labs: discussions, demonstrantions and problem solving related to assignements and project.
Designation | Weight (%) |
---|---|
Trabalho escrito | 60,00 |
Trabalho laboratorial | 40,00 |
Total: | 100,00 |
Designation | Time (hours) |
---|---|
Estudo autónomo | 30,00 |
Frequência das aulas | 42,00 |
Trabalho de investigação | 38,00 |
Trabalho laboratorial | 52,00 |
Total: | 162,00 |
Implement, deliver and present the assignments.
Score = 60% Assignment + 40% Monography
These students will be subject to all evaluation procedures of regular students, i.e., they must deliver their assignments specified during the course plus any special works also specified. The only difference towards regular students being that they are not required to attend classes, in the cases the law specifically states it.
The classification improvement can only be done in new instances of the curricular unit.