what are mapping techniques for load balancing in details
Mapping techniques for load balancing are used to assign computational tasks to different processors in a parallel computing system in a way that minimizes the imbalance of workload between the processors. Here are some commonly used mapping techniques for load balancing:
Static mapping: In this technique, the workload is divided into fixed-sized chunks, and each chunk is assigned to a processor before the computation begins. This approach is simple and easy to implement, but it may not be optimal if the workload is dynamic or unpredictable.
Dynamic mapping: In this technique, the workload is divided into smaller units, and these units are assigned to processors dynamically based on the current workload of each processor. This approach can achieve better load balancing than static mapping, but it may incur higher communication overhead.what are characteristics of Task and Interaction ?
Task stealing: In this technique, processors that have completed their assigned tasks can “steal” tasks from other processors that are overloaded. This approach can achieve good load balancing even in highly dynamic workloads, but it can also increase communication overhead.Parallel Programming Platforms:
Hierarchical mapping: In this technique, the workload is divided into hierarchical levels, and tasks are assigned to processors at each level based on their workload. This approach can achieve good load balancing while reducing communication overhead, but it requires more complex algorithms and data structures.Models: SIMD, MI ?MD, SIMT, SPMD, Data Flow Models ?
Random mapping: In this technique, tasks are assigned to processors randomly. This approach is simple and easy to implement, but it may not achieve good load balancing in practice.Decomposition Techniques in Parallel Computing | Recursive, Data, Exploratory, Speculative
Overall, the choice of mapping technique depends on the characteristics of the workload, the available hardware resources, and the programming model being used. The goal is to achieve as close to perfect load balance as possible, which can result in better performance and scalability for parallel computing applications.Leet Code : Three (3) Sum Java | CPP | Python Solution
