Parallel and Distributed Programming

Code:

CC430

Acronym:

CC430

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2010/2011 - 1S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~ricroc/aulas/1011/ppd
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master's Degree in Network and Information Systems Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:CC	11	PE do Mestrado em Ciência de Computadores	1	-	7,5	67	202,5
			2
MI:ERS	8	Plano de Estudos a partir de 2007	4	-	7,5	67	202,5

Teaching language

Portuguese

Objectives

Provide students with advanced concepts of theory and practice of computational model for parallel and distributed memory architectures. Hands-on experience on programming distributed memory architectures with MPI, programming shared memory architectures using processes, threads and OpenMP and programming many-core architectures with CUDA.

Program

Introduction and foundations:
Parallel programming, concurrency and parallelism, Flynn taxonomy. Foster's programming methodology. major parallel programming models and paradigms.

Programming for distributed memory architectures using MPI:
MPI specification, explicit message passing, communication protocols, Derived types and data packing, collective communication, communicators, topologies.

Programming for shared memory architectures with processes:
Processes, shared memory segments, shared memory through file mapping, spinlocks, semaphores.

Programming for shared memory architectures with threads:
Multithreading processes with Pthreads, mutexs, conditional variables, keys, implementation of Pthreads.

Programming for shared memory architectures with OpenMP:
OpenMP specification, compilation directives, work-sharing constructors, basic constructors, sincronisation constructors, basic functions, locking functions, environment variables, removing data dependencies, performance, combining OpenMP with MPI.

Programming for many-core architectures with CUDA:
GPU architectures. CUDA specification, programming interface and main constructs. Examples of CUDA programs.

Performance metrics:
Speedup measures, efficiency, redundancy, usability and quality of a parallel application. Amdahl law. Gustafson-Barsis law. Karp-Flatt metrics.

Parallel algorithms:
Scheduling and load balancing strategies. Parallel algorithms for sorting, search, monte-carlo simulation, matrix and multiplication.

Mandatory literature

Michael J. Quinn; Parallel Programming in C with MPI and OPenMP, McGraw-Hill.
B. Nichols, D. Buttlar and J.P. Farrell; Pthreads Programming, O'Reilly
R. Chandra, L. Dagum, D. Kohr, D. Maydan, J. McDonald and R. Menon; Parallel Programming in OpenMP, Morgan Kaufmann
P. Pacheco.; Parallel Programming with MPI, Morgan Kaufmann
M. Mitchell, J. Oldham and A. Samuel; Advanced Linux Programming, New Riders
B. Wilkinson, M. Allen.; Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers , Prentice Hall.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	63,00
	Total:	-	0,00

Calculation formula of final grade

The assessment is made based on the students performance on two practical assignments and two written tests.

- Each practical assignments weight 4 out of 20 in the final evaluation mark for this course. The minimal classification in the sum of both practical assignments is 4 out of 8.

- Each written test weight 6 out of 20 in the final evaluation mark for this course. The minimal classification in the sum of both written tests is 4 out of 12.

Examinations or Special Assignments

The assessment is made based on the students performance on:

- two practical assignments, one concerning parallel programming for distributed memory architectures and the other concerning parallel programming for shared memory architectures.

- two written tests, one at the middle of the semester and the other at the end.