Module/Course Title: Parallel Computing
Module course code KOMS120611	Student Workload 119 hours	Credits 3 / 4.5 ETCS	Semester 6	Frequency Even Semester	Duration 16
1	Type of course Field of Study Courses	Contact hours 40 hours of face-to-face (theoretical) class activity	Independent Study 48 hours of independent activity 48 hours of structured activities	Class Size 30
2	Prerequisites for participation (if applicable) -
3	Learning Outcomes Students can demonstrate systematic thinking in selecting information technology architecture Students can analyze information technology infrastructure requirements Students can design information technology infrastructure solutions Students can compare between shared-memory model and distributed-memory model in parallel computing Students can design parallel computing solutions for several problems Students can implements parallel computing solutions for several problems
4	Subject aims/Content Parallel Computing is an advanced computer science course that focuses on the principles, techniques, and methodologies for developing efficient and scalable parallel programs. The course is designed to equip students with the knowledge and skills required to harness the power of parallel computing in order to solve complex computational problems. Throughout the course, students will explore various parallel programming models, architectures, and algorithms associated with parallel programming, including concurrency, load balancing, synchronization, and communication among parallel processes and gain a knowledge of the challenges, opportunities, and emerging trends on the topics. Students will also have hands-on experiences of implementing several parallel programming technique using parallel languages and tools (such as OpenMP and MPI) in real-world problems. Study Material Parallel Computing Overview Parallel Computing: Concepts and Terminology Parallel Computer Memory Architectures Parallel Programming Models Parallel Programming Models (part 2) Designing Parallel Programs Designing Parallel Programs (part 2) - Designing Parallel Programs (part 3) Designing Parallel Programs (part 4) Parallel Examples: Array Processing  Parallel Examples: PI Calculation Parallel Examples: Simple Heat Equation Parallel Examples: 1-D Wave Equation Paper review -
5	Teaching methods Synchronous: Face-to-face lectures, discussions Asynchronous: The material is delivered through e-learning
6	Assesment Methods Participation, active observation, structured assignments
7	This module/course is used in the following study programme/s as well Computer Science Study Programme
8	Responsibility for module/course A.A. Gede Yudhi Paramartha, S.Kom., M.Kom. NIDN : 0022068803
9	Other Information Books: Thomas Rauber; Gudula Rünger (2013). Parallel Programming: for Multicore and Cluster Systems. Springer Science & Business Media. p. 1. ISBN 9783642378010.  Barney, Blaise. "Introduction to Parallel Computing". Lawrence Livermore National Laboratory. Retrieved 2007-11-09. Publications: You Zou, et al. Parallel computing for genome sequence processing, Briefings in Bioinformatics, Volume 22, Issue 5, September 2021 Websites: OpenMP tutorials and Articles (https://www.openmp.org/resources/tutorials-articles/) MPI Tutorials (https://mpitutorial.com/tutorials/)