Load balancing is a critical aspect of parallel computing, as it ensures that the workload is evenly distributed among processors to achieve maximum performance. Mapping techniques are used to assign tasks to processors in such a way that the workload is balanced. Here are some common mapping techniques used for load balancing in parallel computing:
- In static mapping, the assignment of tasks to processors is determined at the beginning of the execution of the parallel algorithm and remains fixed throughout the execution.
- Static mapping is suitable for problems where the workload and communication patterns are known in advance.
- In dynamic mapping, the assignment of tasks to processors is determined dynamically during the execution of the parallel algorithm based on the workload and communication patterns.
- Dynamic mapping is suitable for problems where the workload and communication patterns are unknown or vary during the execution.
- In work stealing, idle processors steal tasks from busy processors to balance the workload dynamically. When a processor becomes idle, it requests work from a busy processor
- The busy processor responds by sending a subset of its workload to the idle processor. Work stealing is suitable for problems where the workload is unevenly distributed or unpredictable.
- Space-filling curves are used to map the problem domain to a one-dimensional array that can be partitioned and distributed among processors.
- This mapping technique ensures that nearby points in the problem domain are assigned to nearby processors, minimizing communication overhead. Space-filling curves are suitable for problems that can be represented in a regular grid or mesh.
Load Balancing Heuristics:
- Load balancing heuristics uses statistical or rule-based techniques to dynamically adjust the assignment of tasks to processors based on the current workload and communication patterns.
- Load-balancing heuristics are suitable for problems that exhibit complex and dynamic workloads and communication patterns.
These mapping techniques can be combined or modified to suit the specific requirements of the problem and the available parallel computing resources. The key to designing an efficient load-balancing strategy is to optimize the balance between computation, communication, and synchronization to achieve high-performance parallel computing.