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Main
UQU Programs
Computer Science

Parallel and Distributed Computing

CS4612
3 hours English

Parallel and Distributed Computing

Parallel and Distributed Computing
CS3608 Operating System
3 hours One requisite
1 Ability to measure the performance, and knowledge of the major sources of performance loss in parallel programs
2 Ability to measure the performance, and knowledge of the major sources of performance loss in parallel programs
3 Ability to measure the performance, and knowledge of the major sources of performance loss in parallel programs
4 Parallel Performance (Speedup, Scalability)
5 Accessing shared data safely (Synchronization)
6 Shared memory programming OpenMP (Covered during Lab)
7 Shared memory programming OpenMP (Covered during Lab)
1.1 Mapped to: K1

Awareness of parallel computing concepts and parallel processor architecture.

Teaching Strategy Lectures, discussions, lab exercises.
Assessment Methods Quizzes, lab exercises, and exams
1.2

Ability to measure the performance, and knowledge of the major sources of performance loss in parallel programs.

Teaching Strategy Lectures, discussions, lab exercises.
Assessment Methods Quizzes, lab exercises, and exams
1.3 Mapped to: K2

Develop and implement parallel and distributed algorithms to solve computational problems efficiently using frameworks such as MPI, OpenMP, or Hadoop.

Teaching Strategy Lectures, discussions, lab exercises.
Assessment Methods Quizzes, lab exercises, and exams
2.1 Mapped to: S1

Evaluate the performance of parallel and distributed systems by analyzing factors such as speedup, scalability, and resource utilization.

Teaching Strategy Lectures, discussions, lab exercises.
Assessment Methods Quizzes, lab exercises, project and exams
2.2 Mapped to: S2

Ability to measure the performance, and knowledge of the major sources of performance loss in parallel programs.

Teaching Strategy Lectures, discussions, lab exercises.
Assessment Methods Quizzes, lab exercises, project and exams
2.3 Mapped to: S2

Ability to measure the performance, and knowledge of the major sources of performance loss in parallel programs.

Teaching Strategy Lectures, discussions, lab exercises.
Assessment Methods Quizzes, lab exercises, project and exams
3.1 Mapped to: V1

Recognize the importance of self-directed learning to stay updated with evolving frameworks, tools, and technologies in parallel and distributed computing.

Teaching Strategy Lectures, discussions, lab exercises.
Assessment Methods Quizzes, lab exercises, project and exams
3.2 Mapped to: V2

Evaluate the performance of parallel and distributed systems by analyzing factors such as speedup, scalability, and resource utilization.

Teaching Strategy Lectures, discussions, lab exercises.
Assessment Methods Quizzes, lab exercises, project and exams
Assessment
Weight
Assignments and Quizzes
15%
Lab exercise and Projects
20%
Mid-term Exam
25%
Theoretical and Practical Final Exam
40%
Total
100%