COMP SCI 7305 - Parallel and Distributed Computing
North Terrace Campus - Semester 1 - 2022
General Course Information
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, COMP SCI 7208 or COMP SCI 7211 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 Coordinator: Associate Professor Hung NguyenCourse Coordinator: Clint Gamlin
Tutor: Tinson "Vincent" Lai
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn 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)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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 ResourcesSee 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 LearningMore information about the course can be found online on the course page
Learning & Teaching Activities
Learning & Teaching ModesThe 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.
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 SummaryThe 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.
Using high performance computing facilities.
Other issues in parallel and distributed computing.
Specific Course RequirementsPrerequisites 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.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment SummaryThe assessment will comprise of two parts: coursework worth 70% and a final exam worth 30%.
Component Weighting CBOK Areas Assignments 70% 1,2,4,7,8,9,11 Final Exam 30% 1,2,8
4. Interpersonal Communication
5. Societal Issues
6. History & Status of the Discipline
7. Hardware & Software
8. Data & Information
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 DetailThe 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.
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.
SubmissionAll 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.
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.
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.
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