Memory Usage:

1. Memory Allocation:

• Memory is allocated for variables and data structures when they are created.
• Different data types consume varying amounts of memory. For instance, integers use less memory than floating-point numbers.
• Dynamic data structures like lists or arrays may consume more memory as they grow.

2. Memory Management:

• Efficient memory management involves allocating and deallocating memory appropriately.
• Languages like Python handle memory allocation and deallocation automatically (garbage collection).
• Manual memory management in languages like C/C++ requires explicit allocation and deallocation.

3. Memory Profiling:

• Tools like memory profilers (e.g., Python’s memory_profiler) help analyze memory usage, identifying memory leaks or high-consumption areas in code.

Example 1: Memory Allocation for NumPy Arrays

Example 2: Memory Profiling with memory_profiler

Performance Optimization:

1. Algorithm Complexity:

• Optimizing algorithms can significantly impact performance. For example, using more efficient sorting algorithms or data structures.
• Analyzing time complexity (e.g., Big O notation) helps assess algorithm efficiency.

2. Vectorization and Parallelization:

• Leveraging vectorized operations (NumPy for Python) or parallel processing (using multiprocessing or threading) can enhance performance by executing tasks concurrently.

3. Profiling Tools:

• Profiling tools (e.g., Python’s cProfile, line_profiler, or timeit) help identify bottlenecks in code execution.
• They measure execution time and identify which parts of the code consume the most resources.

Example 1 : Vectorized Operations vs. Loops

Example 2: Performance Profiling with cProfile

Memory Usage: Demonstrating memory allocation for different data types in NumPy arrays and profiling memory usage using memory_profiler.

Performance Optimization: Comparing the performance of vectorized operations vs. loops and profiling execution time using cProfile.