How do I use the data I have to make predictions?
I'm quite pessimistic about this: ideally one would run the actual task (or a proxy for the task, like a simplified version of the task) with the 100 CPUs. Here the only option I can think of is to:

use the noisy measurements of the 10-20 CPUs as the basis of the data for the actual task
try to map these measurements to the 8 benchmarks, so that we can predict the performance of the 100 CPUs based on their results with the 8 benchmarks.

I'm pessimistic because:

the measurements on the tasks are noisy,
there's no guarantee that these measurements can be reliably linked with the 8 benchmarks,
and no guarantee either that the 8 benchmarks would provide a good estimate on the task.

That being said, technically you could imagine a design with supervised learning, the idea being to predict performance on the task (target variable) from the results of the 8 benchmarks (features). The training data would consist of the results of the 10-20 CPUs for which you have both results (8 benchmarks + performance on the task). I would suggest trying this with decision tree regression because it provides you with an interpretable model (white box solution).