Second Year Non-Departmental Course Feedback

Computer Architecture

Objective:

1 To learn the basics of stored program concepts.

2 To learn the principles of pipelining.

3 To learn mechanism of data storage.

4 To distinguish between the concepts of serial, parallel, pipeline architecture.

 Course Outcome

PCC-CS402Computer ArchitectureBT Level
Upon successful completion of this course, the students will be able to
PCC-CS402.1Understand the evolution of computer hardware to meet the needs of multiprocessing systems.1
PCC-CS402.2Discuss CPU performance measurements.2
PCC-CS402.3Explain and employ the concept of pipeline for consistent execution of instructions with minimum hazards with a prior knowledge of stored program method.3
PCC-CS402.4Illustrate the components and operation of a memory hierarchy with mapping techniques and the range of performance issues.4
PCC-CS402.5Compare different contemporary parallel architectures based on their performances. 4
PCC-CS402.6


Design & Analysis of Algorithm

Course Objective:

To explain the concept of asymptotic complexity with different classes of problems and solution strategies available, and finally evaluate by comparing them.

Course Outcomes:

After completion of this course the students are expected to be able to demonstrate following knowledge, skills and attitudes :-

1.For a given algorithm analyze the worst-case running times, of algorithms based on asymptotic analysis and justify the correctness of the algorithm.

 2. Describe the greedy paradigm and explain when an algorithmic design situation calls for it. For a given problem develop the greedy algorithms.

 3. Describe the divide-and-conquer paradigm and explain when an algorithmic design situation calls for it. Synthesize divide-and-conquer algorithms. Derive and solve recurrence relation.

4. Describe the dynamic-programming paradigm and explain when an algorithmic design situation calls for it. For a given problems of dynamic-programming .

 5. Develop the dynamic programming algorithms, and analyze it to determine its computational complexity.

6. For a given model engineering problem model it using graph and write the corresponding algorithm to solve the problems.

7. Explain the ways to analyze randomized algorithms (expected running time, probability of error).

8. Explain what an approximation algorithm is. Compute the approximation factor of an approximation algorithm (PTAS and FPTAS).