System Programming & Parallel Computing in C

A comprehensive collection of systems programming and parallel computing projects demonstrating expertise in operating systems, concurrent programming, distributed systems, and performance optimization. All projects implemented in pure C with emphasis on low-level programming and parallel algorithms.

📚 Table of Contents

• Overview
• Projects
  • Core Systems Programming
  • MPI Distributed Computing
  • OpenMP Shared Memory Parallelism
• Technologies Used
• Skills Demonstrated
• Building and Running
• Project Structure
• Learning Outcomes
• Academic Context

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🎯 Overview

This repository contains 11 complete C programming projects spanning systems programming and parallel computing:

• 2 Core Systems Projects - OS scheduling, filesystems
• 6 MPI Projects - Distributed memory parallel computing
• 3 OpenMP Projects - Shared memory multithreading

All projects include:

• ✅ Complete source code with documentation
• ✅ Build systems (Makefiles)
• ✅ Example outputs and test cases
• ✅ Performance analysis and benchmarking

Total Lines of Code: ~3,000+
Language: Pure C (C99 standard)
Complexity: Advanced undergraduate/graduate level

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📂 Projects

Core Systems Programming

1. 🔄 MLFQ Scheduler

Multi-Level Feedback Queue CPU scheduling implementation with pthread synchronization

• Technology: C, pthreads, mutexes, condition variables
• Lines of Code: ~500
• Key Features:
  • Three-level priority queue system
  • Round-robin scheduling within levels
  • Priority aging and starvation prevention
  • Thread-safe implementation with mutex protection
  • Configurable time quantums and boost intervals
• Demonstrates: OS scheduling algorithms, thread synchronization, concurrency control
• Location: MLFQ/

2. 💾 FAT32 Filesystem Reader

Low-level FAT32 filesystem parser and file extraction tool

• Technology: C, binary I/O, bit manipulation
• Lines of Code: ~600
• Key Features:
  • Parse FAT32 boot sector and FSInfo structures
  • Navigate directory entries and cluster chains
  • Extract files from disk images
  • Handle long filenames (VFAT extensions)
  • Support for large filesystems (>2GB)
• Demonstrates: Filesystem architecture, binary parsing, pointer arithmetic
• Location: fat32_filesystem_reader/

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MPI Distributed Computing

All MPI projects use distributed memory parallelism with explicit message passing.

3. 📡 MPI Collective Operations

Demonstrates MPI Scatterv, Gatherv, and Reduce_scatter operations

• Technology: C, MPI, collective communication
• Lines of Code: ~200
• Key Features:
  • Scatterv with strided data distribution (stride=2)
  • Gatherv with variable-sized collection (rank+1 elements)
  • Reduce_scatter with SUM operation
  • Performance timing with MPI_Wtime()
• Demonstrates: MPI collective operations, parallel patterns
• Location: parallel computing/mpi_collective_operations/

4. 🏃 MPI Relay Race

Team-based relay race simulation with odd vs even process teams

• Technology: C, MPI, point-to-point communication
• Lines of Code: ~150
• Key Features:
  • Two teams: odd-ranked vs even-ranked processes
  • Relay pattern: P1→P3→P5→... vs P2→P4→P6→...
  • Race timing and winner determination
  • Dynamic next-rank calculation
  • MPI_ANY_SOURCE and MPI_ANY_TAG usage
• Demonstrates: Point-to-point communication, parallel algorithms
• Location: parallel computing/mpi/mpi-relay_race/

5. 🔢 Sequential Matrix-Vector Multiplication

Baseline 1024×1024 matrix-vector multiplication for performance comparison

• Technology: C, MPI (timing only)
• Lines of Code: ~100
• Key Features:
  • Large matrix multiplication (1024×1024)
  • Performance baseline for parallel comparison
  • Dynamic memory allocation
  • MPI_Wtime() for accurate timing
• Demonstrates: Sequential algorithms, memory management
• Location: parallel computing/mpi/matrix_multiply/matrix_vector_multply/

6. ⚡ Parallel Matrix-Vector Multiplication

Distributed 64×64 matrix-vector multiplication using MPI

• Technology: C, MPI, Scatter/Gather/Allgather
• Lines of Code: ~130
• Key Features:
  • Row-wise matrix distribution across processes
  • MPI_Scatter for data distribution
  • MPI_Allgather for vector broadcasting
  • MPI_Gather for result collection
  • Scalability testing with 2-64 processes
• Demonstrates: Data parallelism, collective operations
• Location: parallel computing/mpi/matrix_multiply/parallel-matrix-multiply/

7. 🔍 Parallel Vector Search

Find first zero in distributed vector using MIN reduction

• Technology: C, MPI, reduction operations
• Lines of Code: ~60
• Key Features:
  • 256-element vector distributed across processes
  • Parallel search with early termination
  • MPI_Reduce with MIN operation
  • Handles "not found" case with sentinel value (-1)
  • Efficient for large-scale search problems
• Demonstrates: Parallel search algorithms, reductions
• Location: parallel computing/mpi/parallel-vector-search/

8. 🥈 Parallel Second Maximum Finder

Two-pass parallel algorithm to find second-largest element

• Technology: C, MPI, Allreduce operations
• Lines of Code: ~80
• Key Features:
  • 100-element array distributed across processes
  • First pass: find global maximum
  • Elimination phase: replace max with -1
  • Second pass: find second maximum
  • Uses MPI_Allreduce with MAX operation (twice)
• Demonstrates: Multi-pass parallel algorithms, reduction patterns
• Location: parallel computing/mpi/parallel-second-max/

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OpenMP Shared Memory Parallelism

All OpenMP projects use shared memory multithreading with compiler directives.

9. 🔷 Parallel Matrix Multiplication

Multithreaded 100×100 matrix multiplication with thread scaling analysis

• Technology: C, OpenMP, parallel loops
• Lines of Code: ~70
• Key Features:
  • Tests with 2, 4, 8, 16 threads
  • Loop collapsing for better parallelism
  • Static scheduling for load balance
  • Performance comparison across thread counts
  • Initialization and computation both parallelized
• Demonstrates: Thread parallelism, loop optimization, speedup analysis
• Location: parallel computing/openmp/openmp-matrix-multiply/

10. 🔀 Parallel Floyd-Warshall

All-pairs shortest path algorithm with OpenMP parallelization

• Technology: C, OpenMP, parallel algorithms
• Lines of Code: ~50 (core algorithm)
• Key Features:
  • Graph shortest paths using dynamic programming
  • Parallel outer loop (k iterations)
  • Works with weighted directed graphs
  • Scalable to large graphs (tested up to 128×128)
  • Configurable thread count and scheduling
• Demonstrates: Dynamic programming parallelization, graph algorithms
• Location: parallel computing/openmp/openmp-floyd-warshall/

11. 🔢 Parallel Sieve of Eratosthenes

Prime number generation using parallel sieve algorithm

• Technology: C, OpenMP, dynamic scheduling
• Lines of Code: ~60
• Key Features:
  • Find all primes up to n (tested to 100,000+)
  • Parallel marking of composite numbers
  • Dynamic scheduling for load balancing
  • Efficient bit array implementation
  • Scales to large n values (80M+ tested)
• Demonstrates: Parallel number theory algorithms, dynamic scheduling
• Location: parallel computing/openmp/openmp-sieve-eratosthenes/

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🛠️ Technologies Used

Language

• C (C99 standard) - All projects implemented in pure C

Parallel Computing Frameworks

• MPI (Message Passing Interface) - Distributed memory parallelism
  • OpenMPI or MPICH implementation
  • Collective operations (Scatter, Gather, Reduce, Allreduce)
  • Point-to-point communication (Send, Recv)
• OpenMP - Shared memory multithreading
  • Compiler directives (#pragma omp)
  • Loop parallelization with various scheduling policies
  • Thread management and synchronization

Systems Programming

• POSIX Threads (pthreads) - Threading and synchronization primitives
• Binary I/O - Low-level file operations for filesystem parsing
• Bit Manipulation - Efficient data structure operations

Build Systems & Tools

• Make - Automated compilation and dependency management
• GCC/Clang - C compiler with optimization flags
• SLURM - Job scheduling for HPC environments
• Git - Version control

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💡 Skills Demonstrated

Core Computer Science

• ✅ Operating system concepts (scheduling, processes, threads)
• ✅ Data structures (queues, linked lists, arrays, bit arrays)
• ✅ Algorithms (sorting, searching, graph algorithms, dynamic programming)
• ✅ Memory management (dynamic allocation, pointer manipulation)
• ✅ Filesystem architecture and binary formats

Parallel & Distributed Computing

• ✅ Parallelism models: Data parallelism, task parallelism
• ✅ Communication patterns: Point-to-point, collective, broadcast
• ✅ Synchronization: Mutexes, condition variables, barriers
• ✅ Load balancing: Static vs dynamic scheduling
• ✅ Performance analysis: Speedup, efficiency, scalability
• ✅ Distributed algorithms: Parallel search, reduction, matrix operations

Low-Level Programming

• ✅ Pointer arithmetic and memory management
• ✅ Bit manipulation and binary operations
• ✅ Structure packing and alignment
• ✅ Endianness handling
• ✅ Direct hardware/filesystem interaction

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🚀 Building and Running

Prerequisites

# C Compiler
gcc --version  # or clang

# MPI (for distributed projects)
sudo apt-get install openmpi-bin openmpi-dev  # Ubuntu/Debian
brew install open-mpi  # macOS

# OpenMP (usually included with GCC)
gcc -fopenmp --version

Building Individual Projects

Each project has its own Makefile:

# Generic build process
cd project-directory/
make clean
make

# Run
./executable-name

# Or with MPI
mpirun -np 4 ./executable-name

# Clean build artifacts
make clean

Example: Building MPI Project

cd parallel\ computing/mpi_collective_operations/
make
mpirun -np 4 ./mpi_collective_demo

Example: Building OpenMP Project

cd parallel\ computing/openmp/openmp-matrix-multiply/
make
export OMP_NUM_THREADS=8
./matrix_multiply_omp

Using SLURM (HPC Clusters)

cd project-directory/
sbatch scripts/myjob
# Check output
cat output.txt

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📁 Project Structure

system_programming_c/
│
├── MLFQ/                                    # MLFQ Scheduler
│   ├── src/MLFQ.c
│   ├── examples/tasks.txt
│   ├── Makefile
│   └── README.md
│
├── fat32_filesystem_reader/                # FAT32 Reader
│   ├── src/fat32Reader.c
│   ├── include/*.h
│   ├── Makefile
│   └── README.md
│
└── parallel computing/
    │
    ├── mpi_collective_operations/          # MPI Collective Ops
    │   ├── src/A2Q2.c
    │   ├── scripts/myjob
    │   └── Makefile
    │
    ├── mpi/
    │   ├── mpi-relay_race/                 # MPI Relay Race
    │   ├── parallel-vector-search/         # Parallel Search
    │   ├── parallel-second-max/            # Second Max Finder
    │   └── matrix_multiply/
    │       ├── matrix_vector_multply/      # Sequential Matrix
    │       └── parallel-matrix-multiply/   # Parallel Matrix
    │
    └── openmp/
        ├── openmp-matrix-multiply/         # OpenMP Matrix Multiply
        ├── openmp-floyd-warshall/          # Floyd-Warshall
        └── openmp-sieve-eratosthenes/      # Sieve of Eratosthenes

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📊 Performance Highlights

MLFQ Scheduler

• Handles 100+ concurrent tasks
• Sub-millisecond context switch overhead
• Proper priority aging prevents starvation

Matrix Multiplication

• Sequential (1024×1024): ~6ms
• Parallel (64×64, 8 processes): ~0.17ms
• Speedup analysis: Tests with 2-16 threads/processes

Sieve of Eratosthenes

• Finds all primes up to 100,000 in < 1 second
• Scales efficiently to 80M+ with proper scheduling
• Dynamic scheduling handles load imbalance

Parallel Search

• 256-element vector search with early termination
• Efficient MIN reduction for finding first occurrence
• Scalable to millions of elements

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🎓 Learning Outcomes

Through these projects, I have demonstrated proficiency in:

Systems Programming

• Understanding of operating system internals (scheduling, memory management)
• Low-level programming with pointers and bit manipulation
• Binary file formats and filesystem structures
• Thread synchronization and race condition prevention

Parallel Computing

• Distributed memory programming (MPI)
• Shared memory programming (OpenMP)
• Understanding of Amdahl's Law and parallel performance
• Various parallelization patterns (master-worker, SPMD, pipeline)

Algorithm Design

• Parallel algorithm design and analysis
• Load balancing strategies
• Communication optimization
• Scalability considerations

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🔬 Academic Context

These projects were completed as part of advanced systems programming and parallel computing coursework, demonstrating:

• ✅ Theoretical understanding of OS, distributed systems, and parallel computing concepts
• ✅ Practical implementation of complex systems from specifications
• ✅ Performance analysis and optimization techniques
• ✅ Professional software engineering practices

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🔍 Code Quality

All projects follow professional coding standards:

• ✅ Consistent naming conventions
• ✅ Comprehensive comments
• ✅ Error handling
• ✅ Memory leak prevention (verified with valgrind)
• ✅ Compiler warnings addressed (-Wall -Wpedantic)
• ✅ No hardcoded values (use #define constants)
• ✅ Pure C99 standard compliance

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📈 Repository Statistics

• Total Projects: 11
• Total Files: 80+
• Lines of Code: ~3,000+
• Language: Pure C (100%)
• Standards: C99, POSIX
• Documentation: Complete README for each project

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📜 License

These projects are academic work and are provided for educational purposes and portfolio demonstration.

Academic Integrity: If you're a student, please do not copy this code for your assignments. Use it as a learning reference only.

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🔗 Related Skills

• Systems: Linux, POSIX, concurrent programming, low-level I/O
• Parallel: MPI, OpenMP, pthreads, CUDA (learning)
• Languages: C (expert), Python, Assembly (familiar)
• Tools: Git, GCC, Make, SLURM, GDB, Valgrind
• Concepts: Algorithms, data structures, OS internals, parallel algorithms

⭐ Acknowledgments

• Course instructors for project specifications and guidance
• MPI and OpenMP communities for excellent documentation
• Open source tools that made this work possible

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Last Updated: October 2024

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This repository showcases real-world systems programming projects in pure C, suitable for technical interviews, graduate school applications, and professional portfolio demonstrations.