Parallel and distributed computing paradigms come with their own set of challenges and issues. Some of the common issues are:
Load Balancing: In both parallel and distributed computing, it is important to distribute the workload evenly across all available resources to ensure maximum efficiency. Load balancing issues can arise due to differences in processing power, memory capacity, and network bandwidth between the available resources.
Communication Overhead: In distributed computing, communication between nodes can become a bottleneck as it requires transferring data over a network, which can be slower than accessing data locally. The overhead involved in sending and receiving messages between nodes can impact overall performance.
Data Management: In both parallel and distributed computing, managing data across multiple resources can be challenging. Ensuring consistency and correctness of data across different nodes requires careful management and coordination.
Fault Tolerance: As the number of resources involved in parallel and distributed computing increases, the likelihood of failures also increases. Ensuring fault tolerance and recovery mechanisms is critical to ensure the overall reliability and availability of the system.
Scalability: Scalability is a key issue in both parallel and distributed computing. As the number of resources increases, it should be possible to add more resources to the system without affecting the overall performance or requiring major changes to the system architecture.
Security: In distributed computing, the security of data and communication between nodes is critical. Ensuring secure communication and access to data across multiple nodes can be challenging and requires careful design and implementation.
Overall, addressing these issues requires careful design and implementation of parallel and distributed computing systems, taking into account the specific requirements and constraints of the application domain.
