Task-based parallelism is a parallel computing paradigm where the computation is divided into tasks that can be executed concurrently, often independently of each other. This approach contrasts with data parallelism and is particularly useful for problems where tasks have varying workloads or dependencies, allowing for dynamic scheduling and load balancing.

Task-based Parallelism

Task-based parallelism

parallel computing paradigm

Computation divided into tasks

executed concurrently

Independent tasks

Data parallelism is a technique in computing where a dataset is divided into smaller chunks, and computations on these chunks are executed simultaneously across multiple processors to expedite processing time. It is commonly used in parallel computing environments to optimize performance, especially in tasks like machine learning model training and large-scale data processing.

data parallelism

varying workloads

dynamic scheduling

Load balancing is a method used to distribute network or application traffic across multiple servers to ensure no single server becomes overwhelmed, thereby improving responsiveness and availability. It is critical for optimizing resource use, maximizing throughput, and minimizing response time in distributed computing environments.

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