COMP SCI 3305 - Parallel and Distributed Computing
North Terrace Campus - Semester 1 - 2016
General Course Information
Course Code COMP SCI 3305 Course Parallel and Distributed Computing Coordinating Unit School of Computer Science Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact up to 2.5 hours per week Available for Study Abroad and Exchange Prerequisites One of COMP SCI 1007, COMP SCI 1009, COMP SCI 1103, COMP SCI 1203, COMP SCI 2103, or COMP SCI 2202 Assumed Knowledge COMP SCI 2000 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: A/Prof Claudia Szabo
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesIn this course, you will learn a range of fundamental and applied techniques in distributed systems. The learning objectives for Parallel and Distributed Computing are:
- To develop and apply knowledge of parallel and distributed computing techniques and methodologies.
- To gain experience in the design, development, and performance analysis of parallel and distributed applications.
- To gain experience in the application of fundamental Computer Science methods and algorithms in the development of parallel applications.
- 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)
1,2,3,4 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
2,3,4 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
Required ResourcesYou should be able to perform all the exercise work required for the course in the University computer Labs. The programming language used are Java, C with MPI and OpenMP, and OpenCL.
However, if you want to be able to work at home, you must install these on your system.
Recommended ResourcesThere are no textbooks for this course. However, there are a number of reference books for this course:
- Parallel Programming for Multicore and Cluster Systems - T. Rauber, G. Runger, Springer 2009 - avalable 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 Moodle forum of the school.
Learning & Teaching Activities
Learning & Teaching ModesThe course will be taught with lecture and tutorials. You are expected to attend the lectures and take part in the activities, and attempt tutorial questions before the scheduled tutorial session. All lectures will be attempted to be recorded, however attendance at the lectures is recommended, due to the large number of activities present in the lectures. These activities will be critical for your learning, so attendance is a must!
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, if scheduled, the tutorial sessions. In addition to the schedule contact hours, you are expected to spend an additional 2-4 hours per week after each lecture to consolidate your understanding of it. 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. The list of topics and their schedule is available on the course forum.
Parallel and distributed systems - Overview and challenges
Multi-threading synchronization issues and solutions
Parallel systems - Flynn. Introduction to parallel programming models.
Parallel algorithm design
Shared memory and Message Passing
GPU Architecture and CUDA Programming
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: practical programming assignments worth 70% and a final exam worth 30%.
Component Weighting CBOK Areas Assignments 70% 1,2,4,7,8,9,11 Final Written 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 DetailMore information on the assessment is provided online on the course forum. The course has two forms of assessment: summative assessment, provided by the tutorial sessions and intermediate assignment submissions, and formative assessment provided by the assignments.
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 forum. 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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- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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