Definition
Parallel programming is a computational approach where multiple processes or threads execute tasks simultaneously to solve a problem faster and more efficiently.
- Instead of performing operations sequentially (one at a time), parallel programming divides the workload into smaller tasks that run concurrently across multiple processors or computing units.
Tip
In parallel processes, always running the longest task first, to reduce the overall execution time
Why is Parallel Programming Useful?
- Speed and Performance: by executing multiple operations simultaneously, parallel programming reduces execution time, making it ideal for handling large-scale computations (e.g., simulations, big data processing, AI training).
- Efficient Resource Utilization: modern CPUs and GPUs have multiple cores; parallel programming allows leveraging these resources effectively
- Scalability: in distributed systems (e.g., cloud computing, supercomputers), workloads can be divided across many machines, improving efficiency as the system scales
- Solving Complex Problems: many scientific, engineering, and AI tasks require high computational power, which would be infeasible with traditional sequential computing
Limitations of Parallel Programming
- Synchronization Overhead: coordinating multiple processes requires extra effort, leading to overhead in data sharing and communication.
- Complexity: writing parallel programs is more challenging than sequential programming due to race conditions, deadlocks, and debugging difficulties
- Not all problems are parallelizable: some tasks are inherently sequential (e.g., dependent calculations) and cannot be efficiently parallelized (Amdahl’s Law)
- Hardware Constraints: Performance gains depend on the hardware capabilities (number of cores, memory bandwidth). Over-parallelization can lead to diminishing returns
- Data Consistency Issues: in shared-memory models, ensuring data consistency and avoiding conflicts requires careful management (e.g., locks, atomic operations).