This markdown file provides an overview of concurrency, parallelism, and distributed systems. Key topics include:
- Definitions: Explains concurrency, parallelism, and distributed systems with examples.
- Comparison Table: Highlights differences in execution, hardware, memory, and scalability.
- Java Concurrency: Covers thread creation, lifecycle, and common patterns in Java.
- Synchronization: Discusses techniques for managing shared resources and results.
This file implements the Particle Swarm Optimization (PSO) algorithm in both sequential and parallel versions. Key features include:
- Objective Function: Sphere function for optimization.
- Particle Class: Represents individual particles in the swarm.
- GlobalBest Class: Manages the global best solution with thread-safe updates.
- Sequential PSO: Implements the PSO algorithm in a single-threaded manner.
- Parallel PSO: Uses multithreading to divide the workload among available processors.
- Utility Methods: Includes methods for swarm initialization, cloning, and result comparison.
- Main Method: Compares the performance and results of sequential and parallel implementations.
This file implements the Dijkstra algorithm for finding shortest paths in a graph, with both sequential and parallel versions. Key features include:
- Graph Representation: Uses an adjacency matrix.
- Sequential Dijkstra: Computes shortest paths for all nodes in a single-threaded manner.
- Parallel Dijkstra: Divides the computation across multiple threads for efficiency.
- Utility Methods: Includes graph generation and result comparison.
- Main Method: Measures and compares the performance of sequential and parallel implementations.
This file implements the Coppersmith-Winograd algorithm for matrix multiplication, with sequential and parallel versions. Key features include:
- Sequential Implementation: Uses recursive matrix partitioning for efficient multiplication.
- Parallel Implementation: Includes ForkJoin and traditional threading approaches.
- Matrix Operations: Provides utility methods for addition, subtraction, and submatrix extraction.
- Performance Comparison: Measures execution time and verifies the correctness of results.
- Main Method: Demonstrates the algorithm with user-defined matrix sizes.
This file implements LU decomposition with both serial and parallel approaches. Key features include:
- Serial LU Decomposition: Factorizes a matrix into lower and upper triangular matrices.
- Parallel LU Decomposition: Uses a thread pool to parallelize the computation.
- Forward and Backward Substitution: Solves the system of equations after decomposition.
- Utility Methods: Includes matrix generation, copying, and result comparison.
- Main Method: Compares the performance and accuracy of serial and parallel implementations.
This file is currently empty but is intended to provide an overview of the project, its purpose, and instructions for usage.