PARALLEL AND DISTRIBUTED COMPUTING SYSTEMS

Course Information
TitleΠΑΡΑΛΛΗΛΑ ΚΑΙ ΚΑΤΑΝΕΜΗΜΕΝΑ ΥΠΟΛΟΓΙΣΤΙΚΑ ΣΥΣΤΗΜΑΤΑ / PARALLEL AND DISTRIBUTED COMPUTING SYSTEMS
CodeΔΜ1012
Interdepartmental ProgrammePPS Advanced Computer and Communication Systems
Collaborating SchoolsElectrical and Computer Engineering
Medicine
Music Studies
Journalism and Mass Communications
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorNikolaos Pitsianis
CommonNo
StatusActive
Course ID600004423
Class Information
Academic Year2017 – 2018
Class PeriodWinter
Faculty Instructors
Weekly Hours24
Class ID
600112829
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Prerequisites
General Prerequisites
Introductory courses in computer architectures, operating systems, algorithms and programming
Learning Outcomes
Understanding of parallel computer architectures, Identification of basic parallel algorithms and parallelization techniques. Knowledge of main programming languages and methodologies for parallel program implementation.
General Competences
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Work autonomously
  • Work in teams
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Overview of parallel computer architectures, from bit and word parallelism to pipelines, instruction-level parallelism, VLIWs, superscalar architectures, multicores and distributed memory multiprocessors, and graphics processing units. Parallel algorithms in dense and sparse linear algebra, signal processing (FFT, convolution), searching and sorting, reductions and parallel prefix. Programming with PTHREADS, Message Passing Interface, OpenMP, Cilk, and CUDA. Examples with race conditions, mutual exclusion, deadlock avoidance and streaming to overlap communications and computations.
Educational Material Types
  • Slide presentations
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures30.1
Written assigments
Total30.1
Student Assessment
Description
Class participation, 3 homework grades and final project grade.
Bibliography
Additional bibliography for study
Culler, Singh and Gupta, Parallel Computer Architecture: A Hardware/Software Approach 1999 Bertsekas and Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, 2003 POSIX Threads Programming tutorial from Lawrence Livermore National Laboratory USA MPI Programming tutorial from Lawrence Livermore National Laboratory USA NVIDIA CUDA User Guide Cilk User’s Guide
Last Update
02-07-2018