|
Teaching GuideTerm Faculty of Computer Science |
| Grao en Intelixencia Artificial |
 Subjects |
| Concurrent. Parallel and Distributed Computation |
| Contents |
| Identifying Data | 2023/24 | |||||||||||||
| Subject | Concurrent. Parallel and Distributed Computation | Code | 614G03014 | |||||||||||
| Study programme |
|
|||||||||||||
| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 2nd four-month period |
Second | Obligatory | 6 | ||||||||||
|
||||||||||||||
| Topic | Sub-topic |
| Chapter 1 - Introduction and previous concepts | * The process and sequential program * Lifecycle of a process * Threads * Paralell program * Usefulness of parallelism |
| Chapter 2 - Hardware parellelism, hierarchy | * Levels of parallelism * Internal processor parallelism (hidden) * Processor functionalities (low-level parallelism) * Processor accessible resources (high-level parallelism) * Pool of machines (Cluster and Supercomputer) * Distributed computing * Specific devices * State of the art of processors |
| Chapter 3 - Software parallelism, design and implementation | * Flynn taxonomy * Frameworks and languages for parallelism * Key concepts * Paradigms for parallel processing * Parallel programs analysis * Parallel programs design |
| Chapter 4 - Parallelism for Artificial Intelligence | * Parallelism in IA application * Massive and distributed data processing * Data processing in GPU |
|
