If streaming is embarrassingly parallel, what shape do you choose?

When streaming data is described as embarrassingly parallel, it means that the tasks can be performed independently and can be processed simultaneously without requiring significant interaction or communication between them. In this context, choosing a GPU (Graphics Processing Unit) is particularly advantageous due to its architecture, which is designed to handle multiple parallel tasks efficiently.

GPUs are optimized for parallel processing, as they consist of hundreds or thousands of smaller cores that can execute many threads simultaneously. This makes them well-suited to handle workloads where the same operation is applied to large datasets, such as those encountered in streaming data applications. Tasks such as video processing, real-time data analysis, and complex computations can all benefit from the inherent parallelism that GPUs provide.

In contrast, other shapes such as CPUs, Memory-Optimized, or DenseIO are not as specialized for the high level of parallelism found in streaming scenarios. While CPUs are capable of performing parallel operations, they are typically more suited for tasks that require tight coupling or significant inter-task communication. Memory-Optimized configurations focus on high memory bandwidth for data-intensive applications, and DenseIO provides enhanced storage performance which is not necessarily aligned with the needs of embarrassingly parallel workloads.

Thus, leveraging a GPU for embarrassingly parallel