Page 83 - B.E CSE Curriculum and Syllabus R2017 - REC

Department of CSE, REC

synchronization, Contention, Reducing communication overhead, Optimal domain decomposition,
Aggregating messages, Nonblocking vs. asynchronous communication, Collective communication,
Understanding intranode point-to-point communication
TOTAL: 45 PERIODS

OUTCOME:
On successful completion of this course, the student will be able to:
● Able to get different parallel processing approaches.
● Describe different parallel processing platforms involved in achieving High Performance
Computation.
● Able to give different solution to design issues, efficient and high performance parallel programming
techniques.
● Analyze an existing program for OpenMP and MPI parallelization possibilities.
● Evaluate parallel programming using message passing paradigm using open source APIs.

TEXT BOOK:
1. Georg Hager, Gerhard Wellein, Introduction to High Performance Computing for Scientists and
Engineers, Second edition, CRC Press Taylor & Francis Group, 2014.

REFERENCES:
1. John Levesque, Gene Wagenbreth, High Performance Computing: Programming and Applications,
Chapman and Hall/CRC, 2010.
2. Charles Severance Kevin Dowd, High Performance Computing, Second Edition, O'Reilly Media,
1998.

IT17E62 DATA WAREHOUSING AND DATA MINING L T P C
(Common to B.E. CSE and B.Tech. IT) 0 0 4 2

OBJECTIVES:
 To learn the concepts of Data Warehousing and Business Analysis.
 Familiar with the concepts of Data Mining.
 To understand the concepts of Association and Correlations Algorithms.
 To understand the concepts of Classification Algorithms.
 To understand the concepts of Clustering and outlier Analysis.

UNIT I DATA WAREHOUSING 9
Data Warehouse: Basic Concepts, A Multitier Architecture, Data Warehouse Models, Metadata Repository-
Data Warehouse Modelling: Data Cube and OLAP, Data Cube: A Multidimensional Data Model: Schemas-
Concept Hierarchies-OLAP Operations.

UNIT II DATA MINING AND VISUALIZATION 8
Introduction: Kinds of Data, Kinds of Patterns- Data Objects and Attribute Types- Data Visualization: Pixel-
Oriented, Geometric Projection, Icon-Based, Hierarchical, Visualizing Complex Data and Relations- Data
Preprocessing.

UNIT III ASSOCIATIONS AND CORRELATIONS 9
Basic Concepts: Frequent Itemsets, Closed Itemsets, and Association Rules - Frequent Item set Mining
Methods: Finding Frequent Itemsets by Confined Candidate Generation, Growth Approach for Mining
Frequent Itemsets, Mining Frequent Itemsets Using Vertical Data Format, Mining Closed and Max Patterns -
Interesting Patterns: Pattern Evaluation Methods.

Curriculum and Syllabus | B.E. Computer Science and Engineering | R2017 Page 83