COMP SCI 7305 - Parallel and Distributed Computing

North Terrace Campus - Semester 1 - 2020

A selection of topics from the following: the challenges faced in constructing parallel and distributed applications, including testing, debugging and performance evaluation. Various implementation techniques, paradigms, architectures and programming languages including: Flynn's taxonomy, MPI, MapReduce, OpenMP, GPGPU, concurrency and multi-threading.

  • General Course Information
    Course Details
    Course Code COMP SCI 7305
    Course Parallel and Distributed Computing
    Coordinating Unit School of Computer Science
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 2.5 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites COMP SCI 7103, COMP SCI 7202, COMP SCI 7202B or COMP SCI 7208
    Assumed Knowledge COMP SCI 7081
    Restrictions Master of Computing and Innovation, Master of Data Science, Graduate Diploma in Computer Science and Graduate Certificate in Computer Science students only.
    Course Description A selection of topics from the following: the challenges faced in constructing parallel and distributed applications, including testing, debugging and performance evaluation. Various implementation techniques, paradigms, architectures and programming languages including: Flynn's taxonomy, MPI, MapReduce, OpenMP, GPGPU, concurrency and multi-threading.
    Course Staff

    Course Coordinator: Dr Andrew Wendelborn

    Course Coordinator: Dr Andrew Wendelborn

    Tutors:

    Clint Gamlin
    Joshua Groot
    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

     
    1 To develop and apply knowledge of parallel and distributed computing techniques and methodologies.
    2 To gain experience in the design, development, and performance analysis of parallel and distributed applications.
    3 To gain experience in the application of fundamental Computer Science methods and algorithms in the development of parallel applications.
    4 To gain experience in the design, testing, and performance analysis of a software system, and to be able to communicate that design to others.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1-4
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    2-4
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    1,4
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    4
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • Able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
    1-4
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
    2,4
  • Learning Resources
    Required Resources

    This book is the textbook for much of the course, and you should ensure that you have continuing access to a copy:

    An Introduction to Parallel Programming by Peter Pacheco (Elsevier 2011; ISBN 9786612954047).

    Copies are available in the library, and ordered for the bookshop (now Booktopia).

    The library has an electronic version, accessible through a very good eReader.
    To see what the library has, do a catalogue search using the ISBN above.
    Programming assignments are an essential part of the course.
    The programming language used will be C with MPI, Pthreads and OpenMP, and also OpenCL.
    You should be able to perform all the programming required for the course in the University computer laboratories.
    If you want to be able to work at home, you must install these on your system.
    Recommended Resources
    See above for details of the course textbook.

    A very good reference book is:

    Designing and Building Parallel Programs, by Ian Foster (Addison-Wesley, 1995)
             full online edition at: https://www.mcs.anl.gov/~itf/dbpp

    These are useful additional references:

    Parallel Programming for Multicore and Cluster Systems - T. Rauber, G. Runger, Springer 2009 - available online through the University library
    Principles of Parallel Programming - C. Lin, L. Snyder, Addison-Wesley, 2009 - available in the University library
    The Art of Computer Systems Performance Analysis - R. Jain, 1997
    Online Learning
    More information about the course can be found online on the course page
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will be taught with lectures and tutorials (workshops).

    The lectures are important. We use them to convey information about the course, and to explain the concepts. The best way to use them is to listen carefully, take notes, and to follow up soon after the lecture by reading about the concepts in the textbook and other materials.

    Many lectures will include activities, such as demonstrations, problem discussion, and quizzes. You are expected to take part in these activities, and attempt tutorial questions before the scheduled workshop session.

    We will attempt to record lectures as much as possible. However, is possible that some parts may not be recorded. Hence, lecture attendance is strongly recommended!

    If you can't attend, make sure to look at the recorded video as soon as possible after the lecture. Work through it carefully, taking notes as recommended above. If possible, talk to other students about the lecture that you missed.

    See the "Specific Course Requirements" section below for an important note about prerequisites and assumed knowledge.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    You are expected to attend all scheduled lecture classes (2hrs per week), and all scheduled workshop (tutorial) sessions. In addition to the scheduled contact hours, you are expected to spend an additional 2-4 hours per week after each lecture to consolidate your understanding of it. Note that, as much as possible, the lecture material will be based on content from the textbook. You will need to allocate up to 7 hours per week on average to work on the assignments and tutorials.
    Learning Activities Summary
    The topics taught in this course can be broadly classified as shown below.
    See the course page in MyUni for more details.

    Parallel and distributed systems. Overview and challenges. Why do it?
    Parallel hardware and software.
    Distributed memory programming with message passing and MPI.
    Shared memory programming: multi-threading, in particular Pthreads and OpenMP.
    Introduction to parallel programming models.
    Parallel algorithm design and program development.
    GPU Architecture and CUDA Programming.
    Performance Analysis.
    Using high performance computing facilities.
    Other issues in parallel and distributed computing.
    Specific Course Requirements
    Prerequisites and assumed knowledge

    An official prerequisite for this course is an advanced course in data structures.
    See the section "General Course Information" for details.

    The course Computer Systems is assumed knowledge.

    Each of these is important. In order to do parallel programming, we need to look at data structures in different ways: to do this, we need to clearly understand their fundamental properties.

    High performance computing pushes computer design to its limits. To understand this, we need a good working knowledge of computer systems aspects, in hardware, software and networks.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Due to the current COVID-19 situation modified arrangements have been made to assessments to facilitate remote learning and teaching. Assessment details provided here reflect recent updates.

    Component Weighting CBOK Areas
    Assignments 70% 1,2,4,7,8,9,11
    Final Written Exam 30% 1,2,8

    The examination will be online and open book, scheduled by the Examinations Office, and marked in the usual way.
    The first two assignments, Assignment 1 and Assignment 2, will increase in value from 10% to 12.5%.

    CBOK Legend
    1. Abstraction
    2. Design
    3. Ethics
    4. Interpersonal Communication
    5. Societal Issues
    6. History & Status of the Discipline
    7. Hardware & Software
    8. Data & Information
    9. Programming
    10. Human Computer Interfaces
    11. Systems Development

    Details of the Australian Computer Society's Core Bode of Knowledge (CBOK) can be found in this document.
    Assessment Detail
    The course has on-going assessment through the semester in the form of assignments and other coursework. There is also a final examination at the end of the semester.

    There will be programming assignments, and a Report on an extension topic. Each programming assignment will be assessed primarily via a written description and analysis of the work done. There will also be assessment based on online quizzes.

    Detailed information about assessment will be provided online on the course page.
    Submission
    All practical assignments must be submitted using the School of Computer Science online Submission System.
    Details are included in each assignment description on the course MyUni page. The University policy on plagiarism applies on all submissions.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.