This repository showcases a collection of projects and assignments that span multiple core areas of computer science, from low-level hardware interactions to high-level algorithmic problem solving. It represents a comprehensive foundation in understanding computer systems, data structures, and algorithms, which are essential for developing efficient software.
This repository is organized into five main areas:
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Assembly Language
- Low-level programming using assembly language, focusing on direct interaction with hardware components.
- Demonstrates understanding of CPU architecture, memory management, and instruction sets.
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Computer Architecture
- Explores how computers are structured and operate at the hardware level.
- Includes topics such as logic gates, processor design, memory hierarchy, and performance optimization.
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Data Structures and Algorithms
- Contains implementations of fundamental data structures (e.g., arrays, linked lists, trees, graphs) and algorithms (e.g., sorting, searching, dynamic programming).
- Emphasizes algorithm design, complexity analysis, and real-world application.
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Numerical Methods
- Focuses on solving mathematical problems computationally, using techniques such as interpolation, root finding, and solving systems of equations.
- Includes iterative methods, matrix operations, and polynomial manipulation.
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GPU Computing
- Introductory work in massively parallel programming using CUDA C on NCSA GPUs.
- [Space to add future projects/programs here].
This repository follows a progression from understanding low-level hardware interactions in Assembly Language and Computer Architecture to mastering high-level algorithmic thinking in Data Structures and Algorithms. By working through these areas, I have built a robust foundation in both computer system fundamentals and the design of efficient software solutions.
- Assembly Language gives insight into how software interacts directly with hardware, allowing for efficient resource management.
- Computer Architecture bridges the gap between software and hardware, providing a deeper understanding of how code executes on real machines.
- Data Structures and Algorithms take the lessons from lower levels and apply them to build scalable and optimized solutions for complex computational problems.
- Numerical Methods showcases the application of computational techniques to solve scientific and engineering problems.
- GPU Computing introduces the power of parallel processing to handle data-intensive computations, enhancing performance significantly.
Each directory corresponds to one of the five focus areas. Inside each directory, you'll find code implementations and detailed explanations.
- AssemblyLanguage/: Contains assembly programs demonstrating fundamental hardware interactions.
- ComputerArchitecture/: Projects exploring the design and performance of computer systems.
- DataStructuresAlgorithms/: Implementations of common data structures and algorithms.
- NumericalMethods/: Numerical computation projects tackling mathematical problems.
- GPUComputing/: CUDA C programs focusing on parallel computing techniques.