MapReduce Fundamentals: Architecture, Matrix Multiplication, and Distributed Processing
MapReduce Algorithm for Matrix Multiplication
- Input matrices A and B are stored in HDFS.
- Map Phase: Emits key-value pairs for each element multiplication.
- The Mapper emits the key
(i, j), where the value isA[i][k] × B[k][j]. - Shuffle Phase: Groups all intermediate values by the key
(i, j). - Reduce Phase: Sums all values associated with each key
(i, j). - The final result
C[i][j]is the summation:Σ A[i][k] × B[k][j]. - Each Mapper processes a partial multiplication set.
- The Reducer aggregates these partial
Parallel and Distributed Computing Models Explained
Parallel and Distributed Programming Models
Parallel Programming Models
Shared Memory Model: Allows all processors to use the same memory space.
Distributed Memory Model: Uses separate memory for each processor and relies on message passing.
Data Parallelism: Applies the same operation to many data items at once.
Task Parallelism: Runs different tasks in parallel on possibly different data.
Distributed Programming Models
- Client-Server Model: Involves clients requesting services and servers providing them.
I is correct ii is correct
Explain the use of scanf() and printf() with examples
#include <stdio.H>
int main() {
int a = 10;
float b = 5.5;
printf(“The value of a is %d and b is %.2f\n”, a, b);
return 0;
}
The value of a is 10 and b is 5.50
#include <stdio.H>
int main() {
int age;
printf(“Enter your age: “);
scanf(“%d”, &age);
printf(“You are %d years old.\n”, age);
return 0;
} , You are 21 years old.
Explain the following types of function arguments
Read MoreKali Linux Command Reference: Basic to Intermediate Shell
This cheat sheet covers essential commands for system navigation, file management, user administration, and networking in Kali Linux, spanning foundational concepts (Lessons 1-2) and more advanced topics (Lessons 3-4).
System Information and Navigation
| Concept | Command / Info | Key Point |
|---|---|---|
| OS Info | uname -a | Show kernel, version, and architecture |
| Hostname | hostname | Show the system’s hostname (L3-4) |
| List files | ls, ls -l, ls -als | -l = detailed listing, -a = show hidden files |
| Manual | man <cmd> | View command help documentation |
System Programming Fundamentals: Linking, Memory, and Concurrency
Linking
- The linker is unaware of local variables that exist only within functions.
- Local variables with the same name as global variables follow their local scope.
Symbol Types in Linking
Global Symbols
- Symbols defined by module
m(e.g.,m.cpp) that can be referenced by other modules. - Includes non-static C functions and non-static global variables.
External Symbols
- Global symbols referenced by module
mbut defined by some other module.
Local Symbols
- Symbols defined and referenced exclusively by module
m. - Includes
Implementing Hash Tables with Chaining and Linear Probing
Hash Function Implementation
def hash_func(key, size):
return sum(ord(c) for c in key) % size
Hash Table with Chaining
class HashTableChaining:
def __init__(self, size):
self.table = [[] for _ in range(size)]
self.comparisons = 0
Insert Method
def insert(self, name, number):
index = hash_func(name, len(self.table))
self.table[index].append((name, number))
Search Method
def search(self, name):
index = hash_func(name, len(self.table))
for n, num in