For running an embarrassingly parallel workload like risk analysis using Monte Carlo, what would be the first guess for computational resources?

In a Monte Carlo simulation, particularly for workloads like risk analysis that can be executed independently across multiple threads or nodes, the choice of computational resources is critical for achieving efficiency and speed.

Selecting a resource configuration, such as BM.Standard.E2.64, suggests an approach focused on balancing performance and resource availability. This option typically provides a suitable amount of CPU power and memory for handling the substantial calculations involved in Monte Carlo simulations, while still allowing for parallel execution. The configuration chosen should ideally cater to the computational demands based on the number of simulations and the complexity of the model being analyzed.

Higher configurations, like BM.Standard.E2.128, would offer even more performance but might be unnecessarily powerful for certain workloads, potentially leading to wastage of resources and associated costs if not all computational capacity is utilized. On the other hand, selecting a configuration that's too low, such as BM.Standard.E2.16, might constrain memory and compute resources, leading to inefficient processing and increased run times.

Thus, BM.Standard.E2.64 is a thoughtful choice for an embarrassingly parallel workload like Monte Carlo simulations in risk analysis, effectively striking a balance between adequate resources and cost-efficiency.