BE COMPUTER SPPU LP1 Programs
LP1 Lab Programs
Department:
Computer Engineering Class:
B.E Semester: VII
Subject: Laboratory Practice I (410246)
Download LP1 Programs
Download LP1 Program Set 2 Thanks to Amruta Kulkarni
Part 1: HPC Programs
1 
Test
for input N and generate a randomized vector V of length N (N
should be large). The program should generate output as the two
computed maximum values as well as the
time
taken to find each value.

2  3. Multiply two N × N arrays using n2 processors 
3 
Parallel
Sorting Algorithms (OPENMP)
For Bubble Sort and Merger Sort, based on existing sequential algorithms, design and implement parallel algorithm utilizing all resources available.

4 
Parallel
Search Algorithm(MPI)
Design
and implement parallel algorithm utilizing all resources
available. for
BreadthFirst
Search ( tree or an undirected graph) OR
BestFirst Search that ( traversal of graph to reach a target in the shortest possible path)

Download HPC_LAB
Part II: AIR LAB
1

Solve
8puzzle problem using A* algorithm. Assume any initial
configuration and define goal configuration clearly.
1. JAVA Implementation 
2

1. Python Implementation 
3

Develop elementary chatbot for suggesting investment as per the customers need.
1. Java Implementation 
4

Constraint
Satisfaction Problem:
Implement
cryptarithmetic problem or nqueens or graph coloring problem (
Branch and Bound and Backtracking)
Note.: CPP is not allowed for final Practicals.

5 
Implement
goal stack planning for the following configurations from the blocks
world,

6 
Part III: Data Analytics LAB
No.  Problem Statement  
1

Download
the Iris flower dataset or any other dataset into a DataFrame. (eg
https://archive.ics.uci.edu/ml/datasets/Iris ) Use Python/R and
Perform following –
How
many features are there and what are their types (e.g., numeric,
nominal)?
Compute
and display summary statistics for each feature available in the
dataset. (eg. minimum value, maximum value, mean, range, standard
deviation, variance and percentiles
Data
VisualizationCreate a histogram for each feature in the dataset
to illustrate the feature distributions. Plot each histogram.
Create
a boxplot for each feature in the dataset. All of the boxplots
should be combined into a single plot. Compare distributions and
identify outliers.


2

Download
Pima Indians Diabetes dataset. Use Naive Bayes‟ Algorithm for
classification
Load
the data from CSV file and split it into training and test
datasets.
summarize
the properties in the training dataset so that we can calculate
probabilities and make predictions.
Classify
samples from a test dataset and a summarized training dataset.


3

Write
a Hadoop program that counts the number of occurrences of each
word in a text file.


4 

DOWNLOAD DA ALL PROGRAMS
P.S. Please double check programs for correct algorithm and implementation.
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