|Responsible unit:||Department of Informatics Engineering|
|Course/CS Responsible:||Master in Informatics and Computing Engineering|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|MIEIC||18||Syllabus since 2009/2010||4||-||6||56||162|
Parallel and distributed computing is becoming the computing paradigm as hardware tends to multi-processing units. The common desktop is today built with multicore processors that collectively have more processing power, than the single core processor, but cores are individually less powerful. Programmers will have to deal with multiprocessor architectures in order to use effectively the machines of today and of the future.
Acquisition of useful knowledge to develop parallel programs. Construction of solid basis in parallel architectures, algorithms parallelization, programming models, synchronization of processes and performance measures by the development of programs.
Students should be able to: a) Analyze a problem and identify the adequate parallelization model (Knowledge and Understanding) b) Write message-passing and shared memory programs (Engineering Analysis, Engineering Practice) c) Design parallel solutions for new problems (Engineering Design) d) Use computational models to estimate applications computation time (Investigations) e) knowledge of process concurrency and best practices to implement resource sharing (Transferable skills).
Students should had completed successfully the programming subjects of the 1st and 2nd year.
INTRODUCTION: - Introduction to Parallel Computing. Performance metrics: MIPS, FLOPS. Peak, Max and Sustained performance. Parallel machines: superscalar and vector processors, memory and network organization. Cache memory effect on processor performance - Data Locality.
PARALLEL PROGRAMMING FUNDAMENTALS: Amdahl Law. Ways of extracting parallelism: Functional Parallelism, Data Parallelism, Streaming. Steps to obtain a parallel version of an algorithm: Problem Division, Communication Patterns, Synchronization, Granularity of Parallelization, Staggering (distribution of work by the processors).
PARALLEL PROGRAMMING MODELS: Shared Memory model and Distributed Memory model. Race Condition, Critical sections, False sharing, Reduction operation.
MULTI-PROCESSOR AND MULTI-COMPUTER PROGRAMMING: - Message passing programming with MPI - Shared memory programming with OpenMP - Data Parallel programming with CUDA (GPUs).
CHARACTERIZATION OF PARALLEL COMPUTING: Execution Models, Computing Models, Efficiency and performance Measures, scalability (Isoefficiency Function).
INTRODUCTION TO DISTRIBUTED COMPUTING IN INTERNET ENVIRONMENT: P2P, GRID, CLOUD COMPUTING. Application characteristics.
Theoretical classes: exposition of the subject matter with presentation and discussion of examples. Theoretical-practical classes: problem solving and discussion, including the development of some programs.
|Frequência das aulas||42,00|
Not exceed the maximum number of absences to classes (25%) and deliver the course work.
Final Grade= 0.5*Cont + 0.5*Ex
Cont – Programming assignments and class participation
Ex – Exam grade
The programming assignments are developed during the classes but are optinal for special registration students.
The programming assignments can only be improved in the course next instance.
Experience in C/C++ programming is required.